data set case study

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Statistics Case Study and Dataset Resources

The philosophies of transparency and open access are becoming more widespread, more popular, and—with the ever-increasing expansion of the Internet—more attainable. Governments and institutions around the world are working to make more and more of their accumulated data available online for free. The datasets below are just a small sample of what is available. If you have a particular interest, do not hesitate to search for datasets on that topic. The table below provides a quick visual representation of what each resource offers, while the annotated links below the table provide further information on each website. Links to additional data sets are also provided.

Annotated Links and Further Data Sources

The links below follow a general-to-specific trajectory and have been marked with a maple leaf where content is Canada-specific. At the top of the list are datasets that have been created with post-secondary statistics students in mind.

Approximately two case studies per year have been featured at the Statistical Society of Canada Annual Meetings. This website includes all case studies since 1996. Case Studies vary widely in subject matter, from the cod fishery in Newfoundland, to the gender gap in earnings among young people, to the effect of genetic variation on the relationship between diet and cardiovascular disease risk. The data is contextualized, provided for download in multiple formats, and includes questions to consider as well as references for each data set. The case studies for the current year can be found by clicking on the “Meetings” tab in the navigation sidebar, or by searching for “case study” in the search bar.

Journal of Statistics Education

This international journal, published and accessible online for free, includes at least two data sets with each volume. All volumes to 1993 are archived and available online. Each data set includes the context, methodology, questions asked, analysis, and relevant references. The data are included in the journal’s data archive , linked to both on the webpage sidebar and at the end of each data set.

UK Data Service, Economic and Social Data Service Teaching Datasets

The Economic and Social Data Service (run by the government of the United Kingdom) has an online catalogue of over 5,000 datasets with over 35 sampler survey datasets tailor-made to be easier for students to use. Study methods and data can be downloaded free of charge. These datasets use UK studies from the University of Essex and the University of Manchester. The datasets are for NESSTOR, not SPSS, but can also be downloaded in plain-text format.

The Rice Virtual Lab in Statistics, Case Studies

The Rice Virtual Lab in Statistics is an initiative by the National Science Foundation in the United States created to provide free online statistics help and practice. The online case studies are fantastic not only because they provide context, datasets, and downloadable raw data where appropriate, but they also allow the user to search by type of statistical analysis required for the case study, allowing you to focus on t-tests, histograms, regression, ANOVA, or whatever you need the most practice with. There are a limited number of case studies on this site.

The United Nations (UN) Statistics Division of the Department of Economic and Social Affairs has pooled major UN databases from the various divisions as accumulated over the past sixty or more years in order to allow users to access information from multiple UN sources simultaneously. This database of datasets includes over 60 million data points. The datasets can be searched, filtered, have columns changed, and downloaded for ease of use.

Open Data is an initiative by the Government of Canada to provide free, easily navigable access to data collected by the Canadian Government in areas such as health, environment, agriculture, and natural resources. You can browse the datasets by subject, file format, or department, or use an advanced search to filter using all of the above as well as keywords. The site also includes links to Provincial and Municipal-level open data sites available across Canada (accessible in the “Links” section of the left-hand sidebar).

The University of Toronto Library has prepared this excellent and exhaustive list of sources for Canadian Statistics on a wide variety of topics, organized by topic. Some have restricted access; you may or may not be able to access these through your university library, depending on which online databases your institution is subscribed to. The restricted links are all clearly labelled in red. This resource also has an international section, accessible through the horizontal vertical toolbar at the top left of the page.

CANSIM is Statistics Canada’s key socioeconomic database, providing fast and easy access to a large range of the latest statistics available in Canada. The data is sorted both by category and survey in which the data was collected. The site not only allows you to access tables of data, but lets you customize your own table of data based on what information you would like CANSIM to display. You can add or remove content, change the way in which the information is summarized, and download your personalized data table.

The National Climate Data and Information Archive provides historical climate data for major cities across Canada, both online and available for download, as collected by the Government of Canada Weather Office. The data can be displayed hourly for each day, or daily for each month. Other weather statistics including engineering climate datasets can be found at http://climate.weather.gc.ca/prods_servs/engineering_e.html .

GeoGratis is a portal provided by Natural Resources Canada which provides a single point of access to a broad collection of geospatial data, topographic and geoscience maps, images, and scientific publications that cover all of Canada at no cost and with no restrictions. Most of this data is in GIS format. You can use the Government of Canada’s GeoConnections website’s advanced search function to filter out only information that includes datasets available for download. Not all of the data that comes up on GeoConnections is available online for free, which is why we have linked to GeoGratis in this guide.

This website allows users to download datasets collected by the Canadian Association of Research Libraries (CARL) on collection size, emerging services, and salaries, by year, in excel format.

Online Sources of International Statistics Guide, University of Maryland

This online resource, provided by the University of Maryland’s Libraries website, has an impressive list of links to datasets organized by Country and Region, as well as by category (Economic, Environmental, Political, Social, and Population). Some of the datasets are only available through subscriptions to sites such as Proquest. Check with your institution’s library to see if you can access these resources.

Organization for Economic Co-Operation and Development (OECD) Better Life Index

The OECD ’s mission is to promote policies that will improve the economic and social well-being of people around the world. Governments work together, using the OECD as a forum to share experiences and seek solutions to common problems. In service to this mission, the OECD created the Better Life Index, which uses United Nations statistics as well as national statistics, to represent all 34 member countries of the OECD in a relational survey of life satisfaction. The index is interactive, allowing you to set your own levels of importance and the website organizes the data to represent how each country does according to your rankings. The raw index data is also available for download on the website (see the link on the left-hand sidebar).

Human Development Index

The HDI, run by the United Nations Development Programme , combines indicators of life expectancy, educational attainment, and income into a composite index, providing a single statistic to serve as a frame of reference for both social and economic development. Under the “ Getting and Using Data ” tab in the left-hand sidebar, the HDI website provides downloads of the raw data sorted in various ways (including an option to build your own data table), as well as the statistical tables underlying the HDI report. In the “ Tools and Rankings ” section ( also in the left-hand side bar) you can also see various visualizations of the data and tools for readjusting the HDI.

The World Bank DataBank

The World Bank is an international financial institution that provides loans to developing countries towards the goal of worldwide reduction of poverty. DataBank is an analysis and visualization tool that allows you to generate charts, tables, and maps based on the data available in several databases. You can also access the raw data by country, topic, or by source on their Data page.

Commission for Environmental Cooperation (CEC): North American Environmental Atlas

The CEC is a collaborative effort between Canada, the United States, and Mexico to address environmental issues of continental concern. The North American Environmental Atlas (first link above) is an interactive mapping tool to research, analyze, and manage environmental issues across the continent. You can also download the individual map files and data sets that comprise the interactive atlas on the CEC website. Most of the map layers are available in several mapping files, but also provide links to the source datasets that they use, which are largely available for download.

Population Reference Bureau DataFinder

The Population Reference Bureau informs people about population, health, and the environment, and empowers them to use that information to advance the well-being of current and future generations. It is based in the United States but has international data. The DataFinder website combines US Census Bureau data with international data from national surveys. It allows users to search and create custom tables comparing countries and variables of your choice.

Mathematics-in-Industry Case Studies Journal

This international online journal (run by the FIELDS Institute for Research in Mathematical Sciences, Toronto) is dedicated to stimulating innovative mathematics by the modelling and analysis of problems across the physical, biological, and social sciences. While the information in this journal is more about the process of modelling various industry-related issues, and so it does not explicitly provide case study data sets for students to explore on their own, this journal does provide examples of problems worked on by mathematicians in industry, and can give you an understanding of the myriad ways in which statistics and modelling can be applied in a variety of industries.

UCLA Department of Statistics Case Studies

The University of California Los Angeles offers HTML-based case studies for student perusal. Many of these include small datasets, a problem, and a worked solution. They are short and easy to use, but not formatted to allow students to try their hand before seeing the answer. This website has not been updated since 2001.

National Center for Case Study Teaching in Science

This website, maintained by the National Center for Case Study Teaching in Science out of the University of Buffalo, is a collection of over 450 peer-reviewed cases at the high school, undergraduate, and graduate school levels. The cases can be filtered by subject, and several are listed under “statistics.” In order to access the answer keys, you must be an instructor affiliated with an educational institution. If you would like to access the answer to a particular case study, you can ask your professor to register in order to access the answer key, if he or she will not be marking your case study his/herself.

The DHS Program

The Demographic and Health Surveys Program collects and has ready to use data for over 90 countries from over 300 surveys. The website is very comprehensive and contains detailed information pertaining to the different survey data available for each of the participating countries, a guide to the DHS statistics and recode manual, as well as tips on working with the different data sets. Although registration is required for access to the data, registration is free.

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Top 25 Data Science Case Studies [2024]

In an era where data is the new gold, harnessing its power through data science has led to groundbreaking advancements across industries. From personalized marketing to predictive maintenance, the applications of data science are not only diverse but transformative. This compilation of the top 25 data science case studies showcases the profound impact of intelligent data utilization in solving real-world problems. These examples span various sectors, including healthcare, finance, transportation, and manufacturing, illustrating how data-driven decisions shape business operations’ future, enhance efficiency, and optimize user experiences. As we delve into these case studies, we witness the incredible potential of data science to innovate and drive success in today’s data-centric world.

Related: Interesting Data Science Facts

Top 25 Data Science Case Studies [2024]

Case study 1 – personalized marketing (amazon).

Challenge:  Amazon aimed to enhance user engagement by tailoring product recommendations to individual preferences, requiring the real-time processing of vast data volumes.

Solution:  Amazon implemented a sophisticated machine learning algorithm known as collaborative filtering, which analyzes users’ purchase history, cart contents, product ratings, and browsing history, along with the behavior of similar users. This approach enables Amazon to offer highly personalized product suggestions.

Overall Impact:

• Increased Customer Satisfaction:  Tailored recommendations improved the shopping experience.
• Higher Sales Conversions:  Relevant product suggestions boosted sales.

Key Takeaways:

• Personalized Marketing Significantly Enhances User Engagement:  Demonstrating how tailored interactions can deepen user involvement and satisfaction.
• Effective Use of Big Data and Machine Learning Can Transform Customer Experiences:  These technologies redefine the consumer landscape by continuously adapting recommendations to changing user preferences and behaviors.

This strategy has proven pivotal in increasing Amazon’s customer loyalty and sales by making the shopping experience more relevant and engaging.

Case Study 2 – Real-Time Pricing Strategy (Uber)

Challenge:  Uber needed to adjust its pricing dynamically to reflect real-time demand and supply variations across different locations and times, aiming to optimize driver incentives and customer satisfaction without manual intervention.

Solution:  Uber introduced a dynamic pricing model called “surge pricing.” This system uses data science to automatically calculate fares in real time based on current demand and supply data. The model incorporates traffic conditions, weather forecasts, and local events to adjust prices appropriately.

• Optimized Ride Availability:  The model reduced customer wait times by incentivizing more drivers to be available during high-demand periods.
• Increased Driver Earnings:  Drivers benefitted from higher earnings during surge periods, aligning their incentives with customer demand.
• Efficient Balance of Supply and Demand:  Dynamic pricing matches ride availability with customer needs.
• Importance of Real-Time Data Processing:  The real-time processing of data is crucial for responsive and adaptive service delivery.

Uber’s implementation of surge pricing illustrates the power of using real-time data analytics to create a flexible and responsive pricing system that benefits both consumers and service providers, enhancing overall service efficiency and satisfaction.

Case Study 3 – Fraud Detection in Banking (JPMorgan Chase)

Challenge:  JPMorgan Chase faced the critical need to enhance its fraud detection capabilities to safeguard the institution and its customers from financial losses. The primary challenge was detecting fraudulent transactions swiftly and accurately in a vast stream of legitimate banking activities.

Solution:  The bank implemented advanced machine learning models that analyze real-time transaction patterns and customer behaviors. These models are continuously trained on vast amounts of historical fraud data, enabling them to identify and flag transactions that significantly deviate from established patterns, which may indicate potential fraud.

• Substantial Reduction in Fraudulent Transactions:  The advanced detection capabilities led to a marked decrease in fraud occurrences.
• Enhanced Security for Customer Accounts:  Customers experienced greater security and trust in their transactions.
• Effectiveness of Machine Learning in Fraud Detection:  Machine learning models are greatly effective at identifying fraud activities within large datasets.
• Importance of Ongoing Training and Updates:  Continuous training and updating of models are crucial to adapt to evolving fraudulent techniques and maintain detection efficacy.

JPMorgan Chase’s use of machine learning for fraud detection demonstrates how financial institutions can leverage advanced analytics to enhance security measures, protect financial assets, and build customer trust in their banking services.

Case Study 4 – Optimizing Healthcare Outcomes (Mayo Clinic)

Challenge:  The Mayo Clinic aimed to enhance patient outcomes by predicting diseases before they reach critical stages. This involved analyzing large volumes of diverse data, including historical patient records and real-time health metrics from various sources like lab results and patient monitors.

Solution:  The Mayo Clinic employed predictive analytics to integrate and analyze this data to build models that predict patient risk for diseases such as diabetes and heart disease, enabling earlier and more targeted interventions.

• Improved Patient Outcomes:  Early identification of at-risk patients allowed for timely medical intervention.
• Reduction in Healthcare Costs:  Preventing disease progression reduces the need for more extensive and costly treatments later.
• Early Identification of Health Risks:  Predictive models are essential for identifying at-risk patients early, improving the chances of successful interventions.
• Integration of Multiple Data Sources:  Combining historical and real-time data provides a comprehensive view that enhances the accuracy of predictions.

Case Study 5 – Streamlining Operations in Manufacturing (General Electric)

Challenge:  General Electric needed to optimize its manufacturing processes to reduce costs and downtime by predicting when machines would likely require maintenance to prevent breakdowns.

Solution:  GE leveraged data from sensors embedded in machinery to monitor their condition continuously. Data science algorithms analyze this sensor data to predict when a machine is likely to disappoint, facilitating preemptive maintenance and scheduling.

• Reduction in Unplanned Machine Downtime:  Predictive maintenance helped avoid unexpected breakdowns.
• Lower Maintenance Costs and Improved Machine Lifespan:  Regular maintenance based on predictive data reduced overall costs and extended the life of machinery.
• Predictive Maintenance Enhances Operational Efficiency:  Using data-driven predictions for maintenance can significantly reduce downtime and operational costs.
• Value of Sensor Data:  Continuous monitoring and data analysis are crucial for forecasting equipment health and preventing failures.

Related: Data Engineering vs. Data Science

Case Study 6 – Enhancing Supply Chain Management (DHL)

Challenge:  DHL sought to optimize its global logistics and supply chain operations to decreases expenses and enhance delivery efficiency. It required handling complex data from various sources for better route planning and inventory management.

Solution:  DHL implemented advanced analytics to process and analyze data from its extensive logistics network. This included real-time tracking of shipments, analysis of weather conditions, traffic patterns, and inventory levels to optimize route planning and warehouse operations.

• Enhanced Efficiency in Logistics Operations:  More precise route planning and inventory management improved delivery times and reduced resource wastage.
• Reduced Operational Costs:  Streamlined operations led to significant cost savings across the supply chain.
• Critical Role of Comprehensive Data Analysis:  Effective supply chain management depends on integrating and analyzing data from multiple sources.
• Benefits of Real-Time Data Integration:  Real-time data enhances logistical decision-making, leading to more efficient and cost-effective operations.

Case Study 7 – Predictive Maintenance in Aerospace (Airbus)

Challenge:  Airbus faced the challenge of predicting potential failures in aircraft components to enhance safety and reduce maintenance costs. The key was to accurately forecast the lifespan of parts under varying conditions and usage patterns, which is critical in the aerospace industry where safety is paramount.

Solution:  Airbus tackled this challenge by developing predictive models that utilize data collected from sensors installed on aircraft. These sensors continuously monitor the condition of various components, providing real-time data that the models analyze. The predictive algorithms assess the likelihood of component failure, enabling maintenance teams to schedule repairs or replacements proactively before actual failures occur.

• Increased Safety:  The ability to predict and prevent potential in-flight failures has significantly improved the safety of Airbus aircraft.
• Reduced Costs:  By optimizing maintenance schedules and minimizing unnecessary checks, Airbus has been able to cut down on maintenance expenses and reduce aircraft downtime.
• Enhanced Safety through Predictive Analytics:  The use of predictive analytics in monitoring aircraft components plays a crucial role in preventing failures, thereby enhancing the overall safety of aviation operations.
• Valuable Insights from Sensor Data:  Real-time data from operational use is critical for developing effective predictive maintenance strategies. This data provides insights for understanding component behavior under various conditions, allowing for more accurate predictions.

This case study demonstrates how Airbus leverages advanced data science techniques in predictive maintenance to ensure higher safety standards and more efficient operations, setting an industry benchmark in the aerospace sector.

Case Study 8 – Enhancing Film Recommendations (Netflix)

Challenge:  Netflix aimed to improve customer retention and engagement by enhancing the accuracy of its recommendation system. This task involved processing and analyzing vast amounts of data to understand diverse user preferences and viewing habits.

Solution:  Netflix employed collaborative filtering techniques, analyzing user behaviors (like watching, liking, or disliking content) and similarities between content items. This data-driven approach allows Netflix to refine and personalize recommendations continuously based on real-time user interactions.

• Increased Viewer Engagement:  Personalized recommendations led to longer viewing sessions.
• Higher Customer Satisfaction and Retention Rates:  Tailored viewing experiences improved overall customer satisfaction, enhancing loyalty.
• Tailoring User Experiences:  Machine learning is pivotal in personalizing media content, significantly impacting viewer engagement and satisfaction.
• Importance of Continuous Updates:  Regularly updating recommendation algorithms is essential to maintain relevance and effectiveness in user engagement.

Case Study 9 – Traffic Flow Optimization (Google)

Challenge:  Google needed to optimize traffic flow within its Google Maps service to reduce congestion and improve routing decisions. This required real-time analysis of extensive traffic data to predict and manage traffic conditions accurately.

Solution:  Google Maps integrates data from multiple sources, including satellite imagery, sensor data, and real-time user location data. These data points are used to model traffic patterns and predict future conditions dynamically, which informs updated routing advice.

• Reduced Traffic Congestion:  More efficient routing reduced overall traffic buildup.
• Enhanced Accuracy of Traffic Predictions and Routing:  Improved predictions led to better user navigation experiences.
• Integration of Multiple Data Sources:  Combining various data streams enhances the accuracy of traffic management systems.
• Advanced Modeling Techniques:  Sophisticated models are crucial for accurately predicting traffic patterns and optimizing routes.

Case Study 10 – Risk Assessment in Insurance (Allstate)

Challenge:  Allstate sought to refine its risk assessment processes to offer more accurately priced insurance products, challenging the limitations of traditional actuarial models through more nuanced data interpretations.

Solution:  Allstate enhanced its risk assessment framework by integrating machine learning, allowing for granular risk factor analysis. This approach utilizes individual customer data such as driving records, home location specifics, and historical claim data to tailor insurance offerings more accurately.

• More Precise Risk Assessment:  Improved risk evaluation led to more tailored insurance offerings.
• Increased Market Competitiveness:  Enhanced pricing accuracy boosted Allstate’s competitive edge in the insurance market.
• Nuanced Understanding of Risk:  Machine learning provides a deeper, more nuanced understanding of risk than traditional models, leading to better risk pricing.
• Personalized Pricing Strategies:  Leveraging detailed customer data in pricing strategies enhances customer satisfaction and business performance.

Related: Can you move from Cybersecurity to Data Science?

Case Study 11 – Energy Consumption Reduction (Google DeepMind)

Challenge:  Google DeepMind aimed to significantly reduce the high energy consumption required for cooling Google’s data centers, which are crucial for maintaining server performance but also represent a major operational cost.

Solution:  DeepMind implemented advanced AI algorithms to optimize the data center cooling systems. These algorithms predict temperature fluctuations and adjust cooling processes accordingly, saving energy and reducing equipment wear and tear.

• Reduction in Energy Consumption:  Achieved a 40% reduction in energy used for cooling.
• Decrease in Operational Costs and Environmental Impact:  Lower energy usage resulted in cost savings and reduced environmental footprint.
• AI-Driven Optimization:  AI can significantly decrease energy usage in large-scale infrastructure.
• Operational Efficiency Gains:  Efficiency improvements in operational processes lead to cost savings and environmental benefits.

Case Study 12 – Improving Public Safety (New York City Police Department)

Challenge:  The NYPD needed to enhance its crime prevention strategies by better predicting where and when crimes were most likely to occur, requiring sophisticated analysis of historical crime data and environmental factors.

Solution:  The NYPD implemented a predictive policing system that utilizes data analytics to identify potential crime hotspots based on trends and patterns in past crime data. Officers are preemptively dispatched to these areas to deter criminal activities.

• Reduction in Crime Rates:  There is a notable decrease in crime in areas targeted by predictive policing.
• More Efficient Use of Police Resources:  Enhanced allocation of resources where needed.
• Effectiveness of Data-Driven Crime Prevention:  Targeting resources based on data analytics can significantly reduce crime.
• Proactive Law Enforcement:  Predictive analytics enable a shift from reactive to proactive law enforcement strategies.

Case Study 13 – Enhancing Agricultural Yields (John Deere)

Challenge:  John Deere aimed to help farmers increase agricultural productivity and sustainability by optimizing various farming operations from planting to harvesting.

Solution:  Utilizing data from sensors on equipment and satellite imagery, John Deere developed algorithms that provide actionable insights for farmers on optimal planting times, water usage, and harvest schedules.

• Increased Crop Yields:  More efficient farming methods led to higher yields.
• Enhanced Sustainability of Farming Practices:  Improved resource management contributed to more sustainable agriculture.
• Precision Agriculture:  Significantly improves productivity and resource efficiency.
• Data-Driven Decision-Making:  Enables better farming decisions through timely and accurate data.

Case Study 14 – Streamlining Drug Discovery (Pfizer)

Challenge:  Pfizer faced the need to accelerate the process of discoverying drug and improve the success rates of clinical trials.

Solution:  Pfizer employed data science to simulate and predict outcomes of drug trials using historical data and predictive models, optimizing trial parameters and improving the selection of drug candidates.

• Accelerated Drug Development:  Reduced time to market for new drugs.
• Increased Efficiency and Efficacy in Clinical Trials:  More targeted trials led to better outcomes.
• Reduction in Drug Development Time and Costs:  Data science streamlines the R&D process.
• Improved Clinical Trial Success Rates:  Predictive modeling enhances the accuracy of trial outcomes.

Case Study 15 – Media Buying Optimization (Procter & Gamble)

Challenge:  Procter & Gamble aimed to maximize the ROI of their extensive advertising budget by optimizing their media buying strategy across various channels.

Solution:  P&G analyzed extensive data on consumer behavior and media consumption to identify the most effective times and channels for advertising, allowing for highly targeted ads that reach the intended audience at optimal times.

• Improved Effectiveness of Advertising Campaigns:  More effective ads increased campaign impact.
• Increased Sales and Better Budget Allocation:  Enhanced ROI from more strategic media spending.
• Enhanced Media Buying Strategies:  Data analytics significantly improves media buying effectiveness.
• Insights into Consumer Behavior:  Understanding consumer behavior is crucial for optimizing advertising ROI.

Related: Is Data Science Certificate beneficial for your career?

Case Study 16 – Reducing Patient Readmission Rates with Predictive Analytics (Mount Sinai Health System)

Challenge:  Mount Sinai Health System sought to reduce patient readmission rates, a significant indicator of healthcare quality and a major cost factor. The challenge involved identifying patients at high risk of being readmitted within 30 days of discharge.

Solution:  The health system implemented a predictive analytics platform that analyzes real-time patient data and historical health records. The system detects patterns and risk factors contributing to high readmission rates by utilizing machine learning algorithms. Factors such as past medical history, discharge conditions, and post-discharge care plans were integrated into the predictive model.

• Reduced Readmission Rates:  Early identification of at-risk patients allowed for targeted post-discharge interventions, significantly reducing readmission rates.
• Enhanced Patient Outcomes: Patients received better follow-up care tailored to their health risks.
• Predictive Analytics in Healthcare:  Effective for managing patient care post-discharge.
• Holistic Patient Data Utilization: Integrating various data points provides a more accurate prediction and better healthcare outcomes.

Case Study 17 – Enhancing E-commerce Customer Experience with AI (Zalando)

Challenge:  Zalando aimed to enhance the online shopping experience by improving the accuracy of size recommendations, a common issue that leads to high return rates in online apparel shopping.

Solution:  Zalando developed an AI-driven size recommendation engine that analyzes past purchase and return data in combination with customer feedback and preferences. This system utilizes machine learning to predict the best-fit size for customers based on their unique body measurements and purchase history.

• Reduced Return Rates:  More accurate size recommendations decreased the returns due to poor fit.
• Improved Customer Satisfaction: Customers experienced a more personalized shopping journey, enhancing overall satisfaction.
• Customization Through AI:  Personalizing customer experience can significantly impact satisfaction and business metrics.
• Data-Driven Decision-Making: Utilizing customer data effectively can improve business outcomes by reducing costs and enhancing the user experience.

Case Study 18 – Optimizing Energy Grid Performance with Machine Learning (Enel Group)

Challenge:  Enel Group, one of the largest power companies, faced challenges in managing and optimizing the performance of its vast energy grids. The primary goal was to increase the efficiency of energy distribution and reduce operational costs while maintaining reliability in the face of fluctuating supply and demand.

Solution:  Enel Group implemented a machine learning-based system that analyzes real-time data from smart meters, weather stations, and IoT devices across the grid. This system is designed to predict peak demand times, potential outages, and equipment failures before they occur. By integrating these predictions with automated grid management tools, Enel can dynamically adjust energy flows, allocate resources more efficiently, and schedule maintenance proactively.

• Enhanced Grid Efficiency:  Improved distribution management, reduced energy wastage, and optimized resource allocation.
• Reduced Operational Costs: Predictive maintenance and better grid management decreased the frequency and cost of repairs and outages.
• Predictive Maintenance in Utility Networks:  Advanced analytics can preemptively identify issues, saving costs and enhancing service reliability.
• Real-Time Data Integration: Leveraging data from various sources in real-time enables more agile and informed decision-making in energy management.

Case Study 19 – Personalizing Movie Streaming Experience (WarnerMedia)

Challenge:  WarnerMedia sought to enhance viewer engagement and subscription retention rates on its streaming platforms by providing more personalized content recommendations.

Solution:  WarnerMedia deployed a sophisticated data science strategy, utilizing deep learning algorithms to analyze viewer behaviors, including viewing history, ratings given to shows and movies, search patterns, and demographic data. This analysis helped create highly personalized viewer profiles, which were then used to tailor content recommendations, homepage layouts, and promotional offers specifically to individual preferences.

• Increased Viewer Engagement:  Personalized recommendations resulted in extended viewing times and increased interactions with the platform.
• Higher Subscription Retention: Tailored user experiences improved overall satisfaction, leading to lower churn rates.
• Deep Learning Enhances Personalization:  Deep learning algorithms allow a more nuanced knowledge of consumer preferences and behavior.
• Data-Driven Customization is Key to User Retention: Providing a customized experience based on data analytics is critical for maintaining and growing a subscriber base in the competitive streaming market.

Case Study 20 – Improving Online Retail Sales through Customer Sentiment Analysis (Zappos)

Challenge:  Zappos, an online shoe and clothing retailer, aimed to enhance customer satisfaction and boost sales by better understanding customer sentiments and preferences across various platforms.

Solution:  Zappos implemented a comprehensive sentiment analysis program that utilized natural language processing (NLP) techniques to gather and analyze customer feedback from social media, product reviews, and customer support interactions. This data was used to identify emerging trends, customer pain points, and overall sentiment towards products and services. The insights derived from this analysis were subsequently used to customize marketing strategies, enhance product offerings, and improve customer service practices.

• Enhanced Product Selection and Marketing:  Insight-driven adjustments to inventory and marketing strategies increased relevancy and customer satisfaction.
• Improved Customer Experience: By addressing customer concerns and preferences identified through sentiment analysis, Zappos enhanced its overall customer service, increasing loyalty and repeat business.
• Power of Sentiment Analysis in Retail:  Understanding and reacting to customer emotions and opinions can significantly impact sales and customer satisfaction.
• Strategic Use of Customer Feedback: Leveraging customer feedback to drive business decisions helps align product offerings and services with customer expectations, fostering a positive brand image.

Related: Data Science Industry in the US

Case Study 21 – Streamlining Airline Operations with Predictive Analytics (Delta Airlines)

Challenge:  Delta Airlines faced operational challenges, including flight delays, maintenance scheduling inefficiencies, and customer service issues, which impacted passenger satisfaction and operational costs.

Solution:  Delta implemented a predictive analytics system that integrates data from flight operations, weather reports, aircraft sensor data, and historical maintenance records. The system predicts potential delays using machine learning models and suggests optimal maintenance scheduling. Additionally, it forecasts passenger load to optimize staffing and resource allocation at airports.

• Reduced Flight Delays:  Predictive insights allowed for better planning and reduced unexpected delays.
• Enhanced Maintenance Efficiency:  Maintenance could be scheduled proactively, decreasing the time planes spend out of service.
• Improved Passenger Experience: With better resource management, passenger handling became more efficient, enhancing overall customer satisfaction.
• Operational Efficiency Through Predictive Analytics:  Leveraging data for predictive purposes significantly improves operational decision-making.
• Data Integration Across Departments: Coordinating data from different sources provides a holistic view crucial for effective airline management.

Case Study 22 – Enhancing Financial Advisory Services with AI (Morgan Stanley)

Challenge:  Morgan Stanley sought to offer clients more personalized and effective financial guidance. The challenge was seamlessly integrating vast financial data with individual client profiles to deliver tailored investment recommendations.

Solution:  Morgan Stanley developed an AI-powered platform that utilizes natural language processing and ML to analyze financial markets, client portfolios, and historical investment performance. The system identifies patterns and predicts market trends while considering each client’s financial goals, risk tolerance, and investment history. This integrated approach enables financial advisors to offer highly customized advice and proactive investment strategies.

• Improved Client Satisfaction:  Clients received more relevant and timely investment recommendations, enhancing their overall satisfaction and trust in the advisory services.
• Increased Efficiency: Advisors were able to manage client portfolios more effectively, using AI-driven insights to make faster and more informed decisions.
• Personalization through AI:  Advanced analytics and AI can significantly enhance the personalization of financial services, leading to better client engagement.
• Data-Driven Decision Making: Leveraging diverse data sets provides a comprehensive understanding crucial for tailored financial advising.

Case Study 23 – Optimizing Inventory Management in Retail (Walmart)

Challenge:  Walmart sought to improve inventory management across its vast network of stores and warehouses to reduce overstock and stockouts, which affect customer satisfaction and operational efficiency.

Solution:  Walmart implemented a robust data analytics system that integrates real-time sales data, supply chain information, and predictive analytics. This system uses machine learning algorithms to forecast demand for thousands of products at a granular level, considering factors such as seasonality, local events, and economic trends. The predictive insights allow Walmart to dynamically adjust inventory levels, optimize restocking schedules, and manage distribution logistics more effectively.

• Reduced Inventory Costs:  More accurate demand forecasts helped minimize overstock and reduce waste.
• Enhanced Customer Satisfaction: Improved stock availability led to better in-store experiences and higher customer satisfaction.
• Precision in Demand Forecasting:  Advanced data analytics and machine learning significantly enhance demand forecasting accuracy in retail.
• Integrated Data Systems:  Combining various data sources provides a comprehensive view of inventory needs, improving overall supply chain efficiency.

Case Study 24: Enhancing Network Security with Predictive Analytics (Cisco)

Challenge:  Cisco encountered difficulties protecting its extensive network infrastructure from increasingly complex cyber threats. The objective was to bolster their security protocols by anticipating potential breaches before they happen.

Solution:  Cisco developed a predictive analytics solution that leverages ML algorithms to analyze patterns in network traffic and identify anomalies that could suggest a security threat. By integrating this system with their existing security protocols, Cisco can dynamically adjust defenses and alert system administrators about potential vulnerabilities in real-time.

• Improved Security Posture:  The predictive system enabled proactive responses to potential threats, significantly reducing the incidence of successful cyber attacks.
• Enhanced Operational Efficiency: Automating threat detection and response processes allowed Cisco to manage network security more efficiently, with fewer resources dedicated to manual monitoring.
• Proactive Security Measures:  Employing predictive cybersecurity analytics helps organizations avoid potential threats.
• Integration of Machine Learning: Machine learning is crucial for effectively detecting patterns and anomalies that human analysts might overlook, leading to stronger security measures.

Case Study 25 – Improving Agricultural Efficiency with IoT and AI (Bayer Crop Science)

Challenge:  Bayer Crop Science aimed to enhance agricultural efficiency and crop yields for farmers worldwide, facing the challenge of varying climatic conditions and soil types that affect crop growth differently.

Solution:  Bayer deployed an integrated platform that merges IoT sensors, satellite imagery, and AI-driven analytics. This platform gathers real-time weather conditions, soil quality, and crop health data. Utilizing machine learning models, the system processes this data to deliver precise agricultural recommendations to farmers, including optimal planting times, watering schedules, and pest management strategies.

• Increased Crop Yields:  Tailored agricultural practices led to higher productivity per hectare.
• Reduced Resource Waste: Efficient water use, fertilizers, and pesticides minimized environmental impact and operational costs.
• Precision Agriculture:  Leveraging IoT and AI enables more precise and data-driven agricultural practices, enhancing yield and efficiency.
• Sustainability in Farming:  Advanced data analytics enhance the sustainability of farming by optimizing resource utilization and minimizing waste.

Related: Is Data Science Overhyped?

The power of data science in transforming industries is undeniable, as demonstrated by these 25 compelling case studies. Through the strategic application of machine learning, predictive analytics, and AI, companies are solving complex challenges and gaining a competitive edge. The insights gleaned from these cases highlight the critical role of data science in enhancing decision-making processes, improving operational efficiency, and elevating customer satisfaction. As we look to the future, the role of data science is set to grow, promising even more innovative solutions and smarter strategies across all sectors. These case studies inspire and serve as a roadmap for harnessing the transformative power of data science in the journey toward digital transformation.

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12 Data Science Case Studies: Across Various Industries

Home Blog Data Science 12 Data Science Case Studies: Across Various Industries

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI. An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey.   In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this Data Science course content .

Let’s look at the top data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more.

List of Data Science Case Studies 2024

• Hospitality:  Airbnb focuses on growth by  analyzing  customer voice using data science.  Qantas uses predictive analytics to mitigate losses
• Healthcare:  Novo Nordisk  is  Driving innovation with NLP.  AstraZeneca harnesses data for innovation in medicine  
• Covid 19:  Johnson and Johnson use s  d ata science  to fight the Pandemic  
• E-commerce:  Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction  
• Supply chain management:  UPS optimizes supp l y chain with big data analytics
• Meteorology:  IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''  
• Entertainment Industry:  Netflix  u ses data science to personalize the content and improve recommendations.  Spotify uses big   data to deliver a rich user experience for online music streaming  
• Banking and Finance:  HDFC utilizes Big  D ata Analytics to increase income and enhance  the  banking experience
• Urban Planning and Smart Cities:  Traffic management in smart cities such as Pune and Bhubaneswar
• Agricultural Yield Prediction:  Farmers Edge in Canada uses Data science to help farmers improve their produce
• Transportation Industry:  Uber optimizes their ride-sharing feature and track the delivery routes through data analysis
• Environmental Industry:  NASA utilizes Data science to predict potential natural disasters, World Wildlife analyzes deforestation to protect the environment

Top 12 Data Science Case Studies

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science.  A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk.  Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine.  AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Data science case studies reveal how Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management, drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

9. Data Science in Urban Planning and Smart Cities  

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.  

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.  

10. Data Science in Agricultural Prediction   

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.  

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.  

11. Data Science in the Transportation Industry   

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.  

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science case studies approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.  

12. Data Science in the Environmental Industry    

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.  

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

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Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a SWOT analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue.  Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

• Defining the problem statement and strategy to solve it  
• Gather and pre-process the data by making relevant assumptions  
• Select tool and appropriate algorithms to build machine learning /deep learning models 
• Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary. 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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Top 10 real-world data science case studies.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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About the Authors

Dr. Marlene Smith

University of Colorado Denver

Saint-Gobain NorPro

Mia Stephens

Dr. DeWayne Derryberry

Idaho State University

Eric Stephens

Nashville General Hospital

Dr. Shirley Shmerling

University of Massachusetts

Dr. Volker Kraft

Dr. Markus Schafheutle

Dr. M Ajoy Kumar

Siddaganga Institute of Technology

Sam Gardner

Dr. Jennifer Verdolin

University of Arizona

Kevin Potcner

Dr. Jane Oppenlander

Clarkson University

Dr. Mary Ann Shifflet

University of South Indiana

Muralidhara A

Dr. Jim Grayson

Augusta University

Dr. Robert Carver

Brandeis University

Dr. Frank Deruyck

University College Ghent

Dr. Simon Stelzig

Lohmann GmbH & Co. KG

Andreas Trautmann

Lonza Group AG

Claire Baril

Chandramouli R

Ross Metusalem

Benjamin Ingham

The University of Manchester

Melanie McField

Healthy Reefs for Healthy People

Case Study Solutions request

To request solutions to the exercises within the Case Studies, please complete this form and indicate which case(s) and their number you would like to request in the space provided below.  Solutions are provided to qualified instructors only and all requests including academic standing will be verified before solutions are sent. 

Medical Malpractice

Explore claim payment amounts for medical malpractice lawsuits and identify factors that appear to influence the amount of the payment using descriptive statistics and data visualizations.

Key words: Summary statistics, frequency distribution, histogram, box plot, bar chart, Pareto plot, and pie chart

• Download the case study (PDF)
• Download the data set

Baggage Complaints

Analyze and compare baggage complaints for three different airlines using descriptive statistics and time series plots. Explore differences between the airlines, whether complaints are getting better or worse over time, and if there are other factors, such as destinations, seasonal effects or the volume of travelers that might affect baggage performance.

Key words: Time series plots, summary statistics

Defect Sampling

Explore the effectiveness of different sampling plans in detecting changes in the occurrence of manufacturing defects.

Key words: Tabulation, histogram, summary statistics, and time series plots

Film on the Rocks

Use survey results from a summer movie series to answer questions regarding customer satisfaction, demographic profiles of patrons, and the use of media outlets in advertising.

Key words: Bar charts, frequency distribution, summary statistics, mosaic plot, contingency table, (cross-tabulations), and chi-squared test

Improving Patient Satisfaction

Analyze patient complaint data at a medical clinic to identify the issues resulting in customer dissatisfaction and determine potential causes of decreased patient volume. 

Key words: Frequency distribution, summary statistics, Pareto plot, tabulation, scatterplot, run chart, correlation

• Download the data set 1
• Download the data set 2

Price Quotes

Evaluate the price quoting process of two different sales associate to determine if there is inconsistency between them to decide if a new more consistent pricing process should be developed.

Key words: Histograms, summary statistics, confidence interval for the mean, one sample t-Test

Treatment Facility

Determine what effect a reengineering effort had on the incidence of behavioral problems and turnover at a treatment facility for teenagers.

Key words: Summary statistics, time series plots, normal quantile plots, two sample t-Test, unequal variance test, Welch's test

Use data from a survey of students to perform exploratory data analysis and to evaluate the performance of different approaches to a statistical analysis.

Key words: Histograms, normal quantile plots, log transformations, confidence intervals, inverse transformation

Fish Story: Not Too Many Fishes in the Sea

Use the DASL Fish Prices data to investigate whether there is evidence that overfishing occurred from 1970 to 1980.

Key words: Histograms, normal quantile plots, log transformations, inverse transformation, paired t-test, Wilcoxon signed rank test

Subliminal Messages

Determine whether subliminal messages were effective in increasing math test scores, and if so, by how much.

Key words: Histograms, summary statistics, box plots, t-Test and pooled t-Test, normal quantile plot, Wilcoxon Rank Sums test, Cohen's d

Priority Assessment

Determine whether a software development project prioritization system was effective in speeding the time to completion for high priority jobs.

Key words: Summary statistics, histograms, normal quantile plot, ANOVA, pairwise comparison, unequal variance test, and Welch's test

Determine if a backgammon program has been upgraded by comparing the performance of a player against the computer across different time periods.

Key words: Histograms, confidence intervals, stacking data, one-way ANOVA, unequal variances test, one-sample t-Test, ANOVA table and calculations, F Distribution, F ratios

Per Capita Income

Use data from the World Factbook to explore wealth disparities between different regions of the world and identify those with the highest and lowest wealth.

Key words: Geographic mapping, histograms, log transformation, ANOVA, Welch's ANOVA, Kruskal-Wallis

• Download the data set 3

Kerrich: Is a Coin Fair?

Using outcomes for 10,000 flips of a coin, use descriptive statistics, confidence intervals and hypothesis tests to determine whether the coin is fair. 

Key words: Bar charts, confidence intervals for proportions, hypothesis testing for proportions, likelihood ratio, simulating random data, scatterplot, fitting a regression line

Lister and Germ Theory

Use results from a 1860’s sterilization study to determine if there is evidence that the sterilization process reduces deaths when amputations are performed.

Key words: Mosaic plots, contingency tables, Pearson and likelihood ratio tests, Fisher's exact test, two-sample proportions test, one- and two-sided tests, confidence interval for the difference, relative risk

Salk Vaccine

Using data from a 1950’s study, determine whether the polio vaccine was effective in a cohort study, and, if it was, quantify the degree of effectiveness.

Key words: Bar charts, two-sample proportions test, relative risk, two-sided Pearson and likelihood ratio tests, Fisher's exact test, and the Gamma measure of association

Smoking and Lung Cancer

Use the results of a retrospective study to determine if there is a positive association between smoking and lung cancer, and estimate the risk of lung cancer for smokers relative to non-smokers.

Key words: Mosaic plots, two-by-two contingency tables, odds ratios and confidence intervals, conditional probability, hypothesis tests for proportions (likelihood ratio, Pearson's, Fisher's Exact, two sample tests for proportions)

Mendel's Laws of Inheritance

Use the data sets provided to explore Mendel’s Laws of Inheritance for dominant and recessive traits.

Key words: Bar charts, frequency distributions, goodness-of-fit tests, mosaic plot, hypothesis tests for proportions

Contributions

Predict year-end contributions in an employee fund-raising drive.

Key words: Summary statistics, time series plots, simple linear regression, predicted values, prediction intervals

Direct Mail

Evaluate different regression models to determine if sales at small retail shop are influence by direct mail campaign and using the resulting models to predict sales based upon the amount of marketing.

Key words: Time series plots, simple linear regression, lagged variables, predicted values, prediction intervals

Cost Leadership

Assess the effectiveness of a cost leadership strategy in increasing market share, and assess the potential for additional gains in market share under the current strategy.

Key words: Simple linear regression, spline fitting, transformations, predicted values, prediction intervals

Archosaur:  The Relationship Between Body Size and Brain Size

Analyze data on the brain and body weight of different dinosaur species to determine if a proposed statistical model performs well at describing the relationship and use the model to predict brain weight based on body weight.

Key words: Histogram and summary statistics, fitting a regression line, log transformations, residual plots, interpreting regression output and parameter estimates, inverse transformations

Cell Phone Service

Determine whether wind speed and barometric pressure are related to phone call performance (percentage of dropped or failed calls) and use the resulting model to predict the percentage of bad calls based upon the weather conditions.

Key words: Histograms, summary statistics, simple linear regression, multiple regression, scatterplot, 3D-scatterplot

Housing Prices

After determining which factors relate to the selling prices of homes located in and around a ski resort, develop a model to predict housing prices.

Key words: Scatterplot matrix, correlations, multiple regression, stepwise regression, multicollinearity, model building, model diagnostics

Bank Revenues

A bank wants to understand how customer banking habits contribute to revenues and profitability. Build a model that allows the bank to predict profitability for a given customer. The resulting model will be used to forecast bank revenues and guide the bank in future marketing campaigns.

Key words: Log transformation, stepwise regression, regression assumptions, residuals, Cook’s D, model coefficients, singularity, prediction profiler, inverse transformations

Determine whether certain conditions make it more likely that a customer order will be won or lost.

Key words: Bar charts, frequency distribution, mosaic plots, contingency table, chi-squared test, logistic regression, predicted values, confusion matrix

Titanic Passengers

Use the passenger data related to the sinking of the RMS Titanic ship to explore some questions of interest about survival rates for the Titanic. For example, were there some key characteristics of the survivors? Were some passenger groups more likely to survive than others? Can we accurately predict survival?

Key words: Logistic regression, log odds and logit, odds, odds ratios, prediction profiler

Credit Card Marketing

A bank would like to understand the demographics and other characteristics associated with whether a customer accepts a credit card offer. Build a Classification model that will provide insight into why some bank customers accept credit card offers.

Key words: Classification trees, training & validation, confusion matrix, misclassification, leaf report, ROC curves, lift curves

Call Center Improvement: Visual Six Sigma

The scenario relates to the handling of customer queries via an IT call center. The call center performance is well below best in class. Identify potential process changes to allow the call center to achieve best in class performance.

Key words: Interactive data visualization, graphs, distribution, tabulate, recursive partitioning, process capability, control chart, multiple regression, prediction profiler

Customer Churn

Analyze the factors related to customer churn of a mobile phone service provider. The company would like to build a model to predict which customers are most likely to move their service to a competitor. This knowledge will be used to identify customers for targeted interventions, with the ultimate goal of reducing churn.

Key words: Neural networks, activation functions, model validation, confusion matrix, lift, prediction profiler, variable importance

Boston Housing

Build a variety of prediction models (multiple regression, partition tree, and a neural network) to determine the one that performs the best at predicting house prices based upon various characteristics of the house and its location.

Key words: Stepwise regression, regression trees, neural networks, model validation, model comparison

Durability of Mobile Phone Screen - Part 1

Evaluate the durability of mobile phone screens in a drop test. Determine if a desired level of durability is achieved for each of two types of screens and compare performance.

Key words: Confidence Intervals, Hypothesis Tests for One and Two Population Proportions, Chi-square, Relative Risk

Durability of Mobile Phone Screen - Part 2

Evaluate the durability of mobile phone screens in a drop test at various drop heights. Determine if a desired level of durability is achieved for each of three types of screens and compare performance.

Key words: Contingency analysis, comparing proportions via difference, relative risk and odds ratio

Durability of Mobile Phone Screen - Part 3

Evaluate the durability of mobile phone screens in a drop test across various heights by building individual simple logistic regression models. Use the models to estimate the probability of a screen being damaged across any drop height.

Key words: Single variable logistic regression, inverse prediction

Durability of Mobile Phone Screen - Part 4

Evaluate the durability of mobile phone screens in a drop test across various heights by building a single multiple logistic regression model. Use the model to estimate the probability of a screen being damaged across any drop height.

Key words: Multivariate logistic regression, inverse prediction, odds ratio

Online Mortgage Application

Evaluate the potential improvement to the UI design of an online mortgage application process by examining the usability rating from a sample of 50 customers and comparing their performance using the new design vs. a large collection of historic data on customer’s performance with the current design.

Key words: Distribution, normality, normal quantile plot, Shapiro Wilk and Anderson Darling tests, t-Test

Performance of Food Manufacturing Process - Part 1

Evaluate the performance to specifications of a food manufacturing process using graphical analyses and numerical summarizations of the data.

Key words: Distribution, summary statistics, time series plots

Performance of Food Manufacturing Process - Part 2

Evaluate the performance to specifications of a food manufacturing process using confidence intervals and hypothesis testing.

Key words: Distribution, normality, normal quantile plot, Shapiro Wilk and Anderson Darling tests, test of mean and test of standard deviation

Detergent Cleaning Effectiveness

Analyze the results of an experiment to determine if there is statistical evidence demonstrating an improvement in a new laundry detergent formulation. Explore and describe the affect that multiple factors have on a response, as well as identify conditions with the most and least impact.

Key words: Analysis of variance (ANOVA), t-Test, pairwise comparison, model diagnostics, model performance

Manufacturing Systems Variation

Study the use of Nested Variability chart to understand and analyze the different components of variances. Also explore the ways to minimize the variability by applying various rules of operation related to variance.

Key words: Variability gauge, nested design, component analysis of variance

Text Exploration of Patents

This study requires the use of unstructured data analysis to understand and analyze the text related to patents filed by different companies.

Key words: Word cloud, data visualization, term selection

US Stock Indices

Understand the basic concepts related to time series data analysis and explore the ways to practically understand the risks and rate of return related to the financial indices data.

Key words: Differencing, log transformation, stationarity, Augmented Dickey Fuller (ADF) test

Pricing Musical Instrument

Study the application of regression and concepts related to choice modeling (also called conjoint analysis) to understand and analyze the importance of the product attributes and their levels influencing the preferences.

Key words: Part Worth, regression, prediction profiler

Pricing Spectacles

Design and analyze discrete choice experiments (also called conjoint analysis) to discover which product or service attributes are preferred by potential customers.

Key words: Discrete choice design, regression, utility and probability profiler, willingness to pay

Modeling Gold Prices

Learn univariate time series modeling using US Gold Prices. Build AR, MA, ARMA and ARMA models to analyze the characteristics of the time series data and forecast.

Key words: Stationarity, AR, MA, ARMA, ARIMA, model comparison and diagnostics

Explore statistical evidence demonstrating an association between Saguro size and the amount of flowers it produces.

Key words: Kendall's Tau, correlation, normality, regression

Manufacturing Excellence at Pharma Company - Part 1

Use control charts to understand process stability and analyze the patterns of process variation.

Key words: Statistical Process Control, Control Chart, Process Capability

Manufacturing Excellence at Pharma Company - Part 2

Use Measurement Systems Analysis (MSA) to assess the precision, consistency and bias of a measurement system.

Key words: Measurement Systems Analysis (MSA), Analysis of Variance (ANOVA)

Manufacturing Excellence at Pharma Company - Part 3

Use Design of Experiments (DOE) to advance knowledge about the process.

Key words: Definitive Screening Design, Custom Design, Design Comparison. Prediction, Simulation and Optimization

Polymerization at Lohmann - Part 1

Application of statistical methods to understand the process and enhance its performance through Design of Experiments and regression techniques.

Key words: Custom Design, Stepwise Regression, Prediction Profiler

Polymerization at Lohmann - Part 2

Use Functional Data Analysis to understand the intrinsic structure of the data.

Key words: Functional Data Analysis (FDA), B Splines, Functional PCA, Generalized Regression

Optimization of Microbial Cultivation Process

Use Design of Experiments (DOE) to optimize the microbial cultivation process.

Key words: Custom Design, Design Evaluation, Predictive Modeling

Cluster Analysis in the Public Sector

Use PCA and Clustering techniques to segment the demographic data.

Key words: Clustering, Principal Component Analysis, Exploratory Data Analysis

Forecasting Copper Prices

Learn various exponential smoothing techniques to build various forecasting models and compare them.

Key words: Time series forecasting, Exponential Smoothing

Increasing Bioavailability of a Drug using SMEDDS

Use Mixture/formulation design to optimize multiple responses related to bioavailability of a drug.

Key words: Custom Design, Mixture/Formulation Design, Optimization

Where Have All the Butterflies Gone?

Apply time series forecasting and Generalized linear mixed model (GLMM) to evaluate butterfly populations being impacted by climate and land-use changes.

Key words: Time series forecasting, Generalized linear mixed model

Exploratory Factor Analysis of Trust in Online Sellers

Apply exploratory factor analysis to uncover latent factor structure in an online shopping questionnaire.

Key words: Exploratory Factor Analysis (EFA), Bartlett’s Test, KMO Test

Modeling Online Shopping Perceptions

Apply measurement and structural models to survey responses from online shoppers to build and evaluate competing models.

Key words : Confirmatory Factor Analysis (CFA), Structural Equation Modeling (SEM), Measurement and Structural Regression Models, Model Comparison

Functional Data Analysis for HPLC Optimization

Apply functional data analysis and functional design of experiments (FDOE) for the optimization of an analytical method to allow for the accurate quantification of two biological components.

Key words: Functional Data Analysis, Functional PCA, Functional DOE

Nonlinear Regression Modeling for Cell Growth Optimization

Apply nonlinear models to understand the impact of factors on a cell growth.

Key words: Nonlinear Modeling, Logistic 3P, Curve DOE

Quantifying Sentiment in Economic Reports

Apply Sentiment analysis to quantify the emotion in unstructured text.

Key words: Word Cloud, Sentiment Analysis

Monitoring Fish Abundance in the Mesoamerican Reef

Apply exploratory data analysis in the context of wildlife monitoring and nature conservation

Key words: Summary statistics, Crosstabulation, Data visualization

A Dataset Exploration Case Study with Know Your Data

August 9, 2021

Posted by Mark Díaz and Emily Denton, Research Scientists, Google Research, Ethical AI Team

Data underlies much of machine learning (ML) research and development, helping to structure what a machine learning algorithm learns and how models are evaluated and benchmarked. However, data collection and labeling can be complicated by unconscious biases, data access limitations and privacy concerns, among other challenges. As a result, machine learning datasets can reflect unfair social biases along dimensions of race, gender, age, and more.

Methods of examining datasets that can surface information about how different social groups are represented within are a key component of ensuring development of ML models and datasets is aligned with our AI Principles . Such methods can inform the responsible use of ML datasets and point toward potential mitigations of unfair outcomes. For example, prior research has demonstrated that some object recognition datasets are biased toward images sourced from North America and Western Europe, prompting Google’s Crowdsource effort to balance out image representations in other parts of the world.

Today, we demonstrate some of the functionality of a dataset exploration tool, Know Your Data (KYD), recently introduced at Google I/O, using the COCO Captions dataset as a case study. Using this tool, we find a range of gender and age biases in COCO Captions — biases that can be traced to both dataset collection and annotation practices. KYD is a dataset analysis tool that complements the growing suite of responsible AI tools being developed across Google and the broader research community. Currently, KYD only supports analysis of a small set of image datasets, but we’re working hard to make the tool accessible beyond this set.

Introducing Know Your Data

Know Your Data helps ML research, product and compliance teams understand datasets, with the goal of improving data quality, and thus helping to mitigate fairness and bias issues. KYD offers a range of features that allow users to explore and examine machine learning datasets — users can filter, group, and study correlations based on annotations already present in a given dataset. KYD also presents automatically computed labels from Google’s Cloud Vision API , providing users with a simple way to explore their data based on signals that weren’t originally present in the dataset.

A KYD Case Study

As a case study, we explore some of these features using the COCO Captions dataset , an image dataset that contains five human-generated captions for each of over 300k images. Given the rich annotations provided by free-form text, we focus our analysis on signals already present within the dataset.

Exploring Gender Bias

Previous research has demonstrated undesirable gender biases within computer vision datasets, including pornographic imagery of women and image label correlations that align with harmful gender stereotypes. We use KYD to explore gender biases within COCO Captions by examining gendered correlations within the image captions. We find a gender bias in the depiction of different activities across the images in the dataset, as well as biases relating to how people of different genders are described by annotators.

The first part of our analysis aimed to surface gender biases with respect to different activities depicted in the dataset. We examined images captioned with words describing different activities and analyzed their relation to gendered caption words, such as “man” or “woman”. Building upon recent work that leverages the PMI metric to measure associations learned by a model , the KYD relations tab makes it easy to examine associations between different signals in a dataset. This tab visualizes the extent to which two signals in the dataset co-occur more (or less) than would be expected by chance. Each cell indicates either a positive (blue color) or negative (orange color) correlation between two specific signal values along with the strength of that correlation.

KYD also allows users to filter rows of a relations table based on substring matching. Using this functionality, we initially probed for caption words containing “-ing”, as a simple way to filter by verbs. We immediately saw strong gendered correlations :

Digging further into these correlations, we found that several activities stereotypically associated with women, such as “shopping” and “cooking”, co-occur with images captioned with “women” or “woman” at a higher rate than with images captioned with “men” or “man”. In contrast captions describing many physically intensive activities, such as “skateboarding”, “surfing”, and “snowboarding”, co-occur with images captioned with “man” or “men” at higher rates.

While individual image captions may not use stereotypical or derogatory language, such as with the example below, if certain gender groups are over (or under) represented within a particular activity across the whole dataset, models developed from the dataset risk learning stereotypical associations. KYD makes it easy to surface, quantify, and make plans to mitigate this risk.

In addition to examining biases with respect to the social groups depicted with different activities, we also explored biases in how annotators described the appearance of people they perceived as male or female. Inspired by media scholars who have examined the “male gaze” embedded in other forms of visual media, we examined the frequency with which individuals perceived as women in COCO are described using adjectives that position them as an object of desire. KYD allowed us to easily examine co-occurrences between words associated with binary gender (e.g. "female/girl/woman" vs. "male/man/boy") and words associated with evaluating physical attractiveness. Importantly, these are captions written by human annotators, who are making subjective assessments about the gender of people in the image and choosing a descriptor for attractiveness. We see that the words "attractive", "beautiful", "pretty", and "sexy" are overrepresented in describing people perceived as women as compared to those perceived as men, confirming what prior work has said about how gender is viewed in visual media.

KYD also allows us to manually inspect images for each relation by clicking on the relation in question. For example, we can see images whose captions include female terms (e.g. “woman”) and the word “beautiful”.

Exploring Age Bias

Adults older than 65 have been shown to be underrepresented in datasets relative to their presence in the general population — a first step toward improving age representation is to allow developers to assess it in their datasets. By looking at caption words describing different activities and analyzing their relation to caption words describing age, KYD helped us to assess the range of example captions depicting older adults. Having example captions of adults in a range of environments and activities is important for a variety of tasks, such as image captioning or pedestrian detection.

The first trend that KYD made clear is how rarely annotators described people as older adults in captions detailing different activities. The relations tab also shows a trend wherein “elderly”, “old”, and “older” tend not to occur with verbs that describe a variety of physical activities that might be important for a system to be able to detect. Important to note is that, relative to “young”, “old” is more often used to describe things other than people, such as belongings or clothing, so these relations are also capturing some uses that don’t describe people.

The underrepresentation of captions containing the references to older adults that we examined here could be rooted in a relative lack of images depicting older adults as well as in a tendency for annotators to omit older age-related terms when describing people in images. While manual inspection of the intersection of “old” and “running” shows a negative relation, we notice that it shows no older people and a number of locomotives . KYD makes it easy to quantitatively and qualitatively inspect relations to identify dataset strengths and areas for improvement.

Understanding the contents of ML datasets is a critical first step to developing suitable strategies to mitigate the downstream impact of unfair dataset bias. The above analysis points towards several potential mitigations. For example, correlations between certain activities and social groups, which can lead trained models to reproduce social stereotypes, can be potentially mitigated by “dataset balancing” — increasing the representation of under-represented group/activity combinations. However, mitigations focused exclusively on dataset balancing are not sufficient, as our analysis of how different genders are described by annotators demonstrated. We found annotators’ subjective judgements of people portrayed in images were reflected within the final dataset, suggesting a deeper look at methods of image annotations are needed. One solution for data practitioners who are developing image captioning datasets is to consider integrating guidelines that have been developed for writing image descriptions that are sensitive to race, gender, and other identity categories.

The above case studies highlight only some of the KYD features. For example, Cloud Vision API signals are also integrated into KYD and can be used to infer signals that annotators haven't labeled directly. We encourage the broader ML community to perform their own KYD case studies and share their findings.

KYD complements other dataset analysis tools being developed across the ML community, including Google's growing Responsible AI toolkit . We look forward to ML practitioners using KYD to better understand their datasets and mitigate potential bias and fairness concerns. If you have feedback on KYD, please write to [email protected] .

Acknowledgements

The analysis and write-up in this post were conducted with equal contribution by Emily Denton, Mark Díaz, and Alex Hanna. We thank Marie Pellat, Ludovic Peran, Daniel Smilkov, Nikhil Thorat and Tsung-Yi for their contributions to and reviews of this post. We also thank the researchers and teams that have developed the signals and metrics used in KYD and particularly the team that has helped us implement nPMI.

Data Analysis Case Study: Learn From Humana’s Automated Data Analysis Project

Lillian Pierson, P.E.

Playback speed:

Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.

If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…

Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.

But how you’re in the right place to find out..

As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis. 

In the post below, we’ll look at:

• A shining data success story;
• What went on ‘under-the-hood’ to support that successful data project; and
• The exact data technologies used by the vendor, to take this project from pure strategy to pure success

If you prefer to watch this information rather than read it, it’s captured in the video below:

Here’s the url too: https://youtu.be/xMwZObIqvLQ

3 Action Items You Need To Take

To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:

• Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
• Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
• Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck

Step 1: Reflect Upon Your Organization

Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.

Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:

• What is the business vision for our organization?
• What industries do we primarily support?
• What data technologies do we already have up and running, that we could use to generate even more value?
• What team members do we have to support a new data project? And what are their data skillsets like?
• What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?

Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)

Step 2: Review Data Case Studies

Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies  (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.

Humana’s Automated Data Analysis Case Study

The key thing to note here is that the approach to creating a successful data program varies from industry to industry .

Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.

Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.

Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.

Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).

Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).

In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.

Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.

The AI listens to cues like the customer’s voice pitch.

If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.

Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.

The Outcome

Customers were happier, and customer service representatives were more engaged..

This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.

The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.

What does this mean for you and your business?

Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.

Humana’s Business Use Cases

Humana’s data analysis case study includes two key business use cases:

• Analyzing customer sentiment; and
• Suggesting actions to customer service representatives.

Analyzing Customer Sentiment

First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.

In the case of Humana, the actors were:

• The health insurance system itself
• The customer, and
• The customer service representative

As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.

Humana focused on collecting the key data points, shown in the image below, from their customer service operations.

By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’  manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.

Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.

Suggesting actions to customer service representatives.

The second use case for the Humana data program follows on from the data gathered in the first case.

In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.

In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.

The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:

• The tone of voice is too tense
• The speed of speaking is high
• The customer representative and customer are speaking at the same time

These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.

The preconditions for success in this use case were:

• The call-related data must be collected and stored
• The AI models must be in place to generate analysis on the data points that are recorded during the calls

Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.

Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.

The Technology That Supports This Data Analysis Case Study

I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.

Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.

• For cloud data management Cogito uses AWS, specifically the Athena product
• For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
• They utilize MapReduce, for processing their data
• And Cogito also has traditional systems and relational database management systems such as PostgreSQL
• In terms of analytics and data visualization tools, Cogito makes use of Tableau
• And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)

These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.

If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.

Step 3: S elect The “Quick Win” Data Use Case

Still there? Great!

It’s time to close the loop.

Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…

YES ▶ Excellent!

Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.

NO , Lillian – It’s not applicable. ▶  No problem.

Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.

More resources to get ahead...

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We love helping tech brands gain exposure and brand awareness among our active audience of 530,000 data professionals. If you’d like to explore our alternatives for brand partnerships and content collaborations, you can reach out directly on this page and book a time to speak.

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Power BI Case Study – CFI Capital Partners

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Power BI Case Study – CFI Capital Partners Overview

In this case study, you’ll take on the role of a business intelligence analyst in an investment bank. The sales and trading team at CFI Capital Partners needs you to develop a customized Power BI report to help with bespoke market analysis

You will need to connect to a variety of data sources for information about an investment portfolio, the securities in the portfolio, and the exchange on which the securities are traded. You will be required to transform and model data, create DAX measures, and build report visuals to satisfy the report requirements.

Power BI Case Study – CFI Capital Partners Learning Objectives

• Transform data in Power Query and create a data model and DAX measures
• Analyze and visualize data by creating report visuals
• Build in better user experiences with functionality like Page Drillthrough, Bookmarks, and Conditional Formatting

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Approx 3h to complete

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Case study introduction, transform & model data, analyze & visualize data, user experience, qualified assessment, this course is part of the following programs.

Why stop here? Expand your skills and show your expertise with the professional certifications, specializations, and CPE credits you’re already on your way to earning.

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Datasets for Credit Risk Modeling

Important credit risk modeling projects.

• Probability of Default (PD) tells us the likelihood that a borrower will default on the debt (loan or credit card). In simple words, it returns the expected probability of customers fail to repay the loan.
• Loss Given Default (LGD) is a proportion of the total exposure when borrower defaults. It is calculated by (1 - Recovery Rate). For example someone takes $200,000 loan from bank for purchase of flat. He/She paid some installments before he stopped paying installments further. When he defaults, loan has an outstanding balance of $100,000. Bank took possession of flat and was able to sell it for $90,000. Net loss to the bank is $10,000 which is 100,000-90,000, and the LGD is 10% i.e. $10,000/$100,000.
• Exposure at Default (EAD) is the amount that the borrower has to pay the bank at the time of default. In the above example shown in LGD, outstanding balance of $100,000 is EAD

Datasets for Credit Risk Modeling Projects

• UCI Machine Learning Repository
• Econometric Analysis Book by William H. Greene
• Credit scoring and its applications Book by Lyn C. Thomas
• Credit Risk Analytics Book by Harald, Daniel and Bart
• Lending Club
• PAKDD 2009 Data Mining Competition, organized by NeuroTech Ltd. and Center for Informatics of the Federal University of Pernambuco
• Credit bureau variables which contains details about borrower's previous credits provided by other banks
• Previous Loans that the applicant had with Home Credit
• Previous Point of sales and cash loans that the applicant had with Home Credit
• Previous Credit Cards that the applicant had with Home Credit

You can download data and its description from this link

• Taiwan: http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
• Germany: http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29
• Australia: http://archive.ics.uci.edu/ml/datasets/Statlog+%28Australian+Credit+Approval%29
• Japan: http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening
• Poland: http://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data

Dataset about credit card defaults in Taiwan contains several attributes or characters which can be leveraged to test various machine learning algorithms for building credit scorecard. Note : Poland dataset contains information about attributes of companies rather than retail customers.

To download the datasets below, visit the link and fill the required details in the form. Once filled, you can download the datasets.

The data set HMEQ reports characteristics and delinquency information for 5,960 home equity loans. A home equity loan is a loan where the obligor uses the equity of his or her home as the underlying collateral.

The data set mortgage is in panel form and reports origination and performance observations for 50,000 residential U.S. mortgage borrowers over 60 periods. The periods have been deidentified. As in the real world, loans may originate before the start of the observation period (this is an issue where loans are transferred between banks and investors as in securitization). The loan observations may thus be censored as the loans mature or borrowers refinance. The data set is a randomized selection of mortgage-loan-level data collected from the portfolios underlying U.S. residential mortgage-backed securities (RMBS) securitization portfolios and provided by International Financial Research (www.internationalfinancialresearch.org).

The data set has been kindly provided by a European bank and has been slightly modified and anonymized. It includes 2,545 observations on loans and LGDs.

The ratings data set is an anonymized data set with corporate ratings where the ratings have been numerically encoded (1 = AAA, etc.).

Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.

You just saved a hell lot of time for me!! I was struggling a lot to find lgd data. You just made my task simpler.

Hi , I am looking for Indian credit data set , along with default flags , and loan types for my research . Will you be able to help me with any references please

find listen data extremely useful.It makes understanding difficult concepts of analytics extremely easy. Thanks a ton once again :)

You've done so much a great job! Thanks a bunch!

Thanks a lot...

Hi, I am not able to download the LGD data from the link given above. Could anyone kindly help me with a source to get the LGD data

I have updated the link

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University Libraries purchases Sage Research Methods package

Ohio University Libraries has purchased Sage Research Methods, a platform that includes textbooks, foundation research guidelines, data sets, code books, peer-reviewed case studies and more with updates through 2029.

If members of the OHIO community are looking to explore a new research methodology, hoping to reduce textbook costs or needing a case study for a course, Sage Research Methods can help.

The platform boasts more than 500 downloadable datasets, with code books and instructional materials that provide step by step walk-throughs of the data analysis. The platform also includes quantitative data sets that come with software guides that can assist in understanding tools like SPSS, R, Stata, and Python.

Ohio University now has access to more than 1,000 book titles (including the quantitative social sciences "little green books") that support a variety of research methodologies and approaches from beginner to expert. The OHIO package also includes peer-reviewed case studies with accompanying discussion questions and multiple-choice quiz questions and can be embedded into Canvas courses. Further, the collection includes a Diversifying and Decolonizing Research subcollection that highlights the importance of inclusive research, perspectives from marginalized populations and cultures, and minimizing bias in data analysis. 

Highlighted features within Sage Research Methods include:

• Ability to browse content, including datasets, by disciplines and/or methodology. 
• Informational and instructional videos that cover topics like market research, data visualization, ethics and integrity, and Big Data. The videos are easy to embed, too.
• Interactive research tools that help with research plan development: Methods Map visualizer, Project Planner for outlining, and Reading Lists. 
• Permalinks are easy to access by just copying and pasting the URL into Canvas or your syllabus.

Learn more about Sage Research Methods. 

University Libraries strives to support the OHIO community in and out of the classroom by supporting varying pedagogic approaches and finding ways to make learning more affordable for our students. Further, the Libraries aims to provide access and discoverability to research materials to support Ohio University’s innovative research enterprise. Purchasing Sage Research Methods supports both initiatives as this resource can be used by all students, faculty and staff at Ohio University for research support and instructors for course materials.

Students, faculty and staff Interested in learning more about any of the resources mentioned above are encouraged to reach out to Head of Learning Services and Education Librarian Dr. Chris Guder , Head of Research Services and Health Sciences Librarian Hanna Schmillen or a subject librarian.

Be sure to explore Sage Research Methods on your own; the platform can be accessed through Ohio University Libraries . In addition, there are training sessions and videos from Sage on its training website.

		You must be a registered user to view the in this website. If you already have a username and password, enter it below. If your textbook came with a card and this is your first visit to this site, you can to register. Username: Password: '); document.write(''); } // --> ( ) .'); } else{ document.write('This form changes settings for this website only.'); } //--> Send mail as: '); } else { document.write(' '); } } else { document.write(' '); } // --> '); } else { document.write(' '); } } else { document.write(' '); } document.write(' TA email: '); } else { document.write(' '); } } else { document.write(' '); } // --> Other email: '); } else { document.write(' '); } } else { document.write(' '); } // --> "Floating" navigation? '); } else if (floatNav == 2) { document.write(' '); } else { document.write(' '); } // --> Drawer speed: '; theseOptions += (glideSpeed == 1) ? ' ' : ' ' ; theseOptions += (glideSpeed == 2) ? ' ' : ' ' ; theseOptions += (glideSpeed == 3) ? ' ' : ' ' ; theseOptions += (glideSpeed == 4) ? ' ' : ' ' ; theseOptions += (glideSpeed == 5) ? ' ' : ' ' ; theseOptions += (glideSpeed == 6) ? ' ' : ' ' ; document.write(theseOptions); // --> 1. (optional) Enter a note here: 2. (optional) Select some text on the page (or do this before you open the "Notes" drawer). 3. Highlighter Color: 4. Search for: Search in: Course-wide Content Instructor Resources Course-wide Content An official website of the United States government The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Publications Account settings My Bibliography Collections Citation manager Save citation to file Email citation, add to collections. Create a new collection Add to an existing collection Add to My Bibliography Your saved search, create a file for external citation management software, your rss feed. Search in PubMed Search in NLM Catalog Add to Search Implementation of the World Health Organization Minimum Dataset for Emergency Medical Teams to Create Disaster Profiles for the Indonesian SATUSEHAT Platform Using Fast Healthcare Interoperability Resources: Development and Validation Study Affiliations. 1 Department of Medical Informatics, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan, 81 22-717-7572, 81 22-717-7505. 2 Department of Physiology, Faculty of Medicine, UIN Syarif Hidayatullah Jakarta, Tangerang Selatan, Indonesia. PMID: 39196270 DOI: 10.2196/59651 Background: The National Disaster Management Agency (Badan Nasional Penanggulangan Bencana) handles disaster management in Indonesia as a health cluster by collecting, storing, and reporting information on the state of survivors and their health from various sources during disasters. Data were collected on paper and transferred to Microsoft Excel spreadsheets. These activities are challenging because there are no standards for data collection. The World Health Organization (WHO) introduced a standard for health data collection during disasters for emergency medical teams (EMTs) in the form of a minimum dataset (MDS). Meanwhile, the Ministry of Health of Indonesia launched the SATUSEHAT platform to integrate all electronic medical records in Indonesia based on Fast Healthcare Interoperability Resources (FHIR). Objective: This study aims to implement the WHO EMT MDS to create a disaster profile for the SATUSEHAT platform using FHIR. Methods: We extracted variables from 2 EMT MDS medical records-the WHO and Association of Southeast Asian Nations (ASEAN) versions-and the daily reporting form. We then performed a mapping process to match these variables with the FHIR resources and analyzed the gaps between the variables and base resources. Next, we conducted profiling to see if there were any changes in the selected resources and created extensions to fill the gap using the Forge application. Subsequently, the profile was implemented using an open-source FHIR server. Results: The total numbers of variables extracted from the WHO EMT MDS, ASEAN EMT MDS, and daily reporting forms were 30, 32, and 46, with the percentage of variables matching FHIR resources being 100% (30/30), 97% (31/32), and 85% (39/46), respectively. From the 40 resources available in the FHIR ID core, we used 10, 14, and 9 for the WHO EMT MDS, ASEAN EMT MDS, and daily reporting form, respectively. Based on the gap analysis, we found 4 variables in the daily reporting form that were not covered by the resources. Thus, we created extensions to address this gap. Conclusions: We successfully created a disaster profile that can be used as a disaster case for the SATUSEHAT platform. This profile may standardize health data collection during disasters. Keywords: EMR; EMT; FHIR; Fast Healthcare Interoperability Resources; Indonesia; MDS; SATUSEHAT; WHO; WHO EMT MDS; World Health Organization; development; disaster; disaster management; disaster profile; electronic medical records; emergency medical team; health data; health data collection; implementation; interoperability; minimum dataset; reporting; resources; validation. © Hiro Putra Faisal, Masaharu Nakayama. Originally published in JMIR Medical Informatics (https://medinform.jmir.org). PubMed Disclaimer Citation Manager NCBI Literature Resources MeSH PMC Bookshelf Disclaimer The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited. Effects of tuning decision trees in random forest regression on predicting porosity of a hydrocarbon reservoir. A case study: volve oil field, north sea First published on 8th August 2024 Machine learning (ML) has emerged as a powerful tool in petroleum engineering for automatically interpreting well logs and characterizing reservoir properties such as porosity. As a result, researchers are trying to enhance the performance of ML models further to widen their applicability in the real world. Random forest regression (RFR) is one such widely used ML technique that was developed by combining multiple decision trees. To improve its performance, one of its hyperparameters, the number of trees in the forest ( n_estimators ), is tuned during model optimization. However, the existing literature lacks in-depth studies on the influence of n_estimators on the RFR model when used for predicting porosity, given that n_estimators is one of the most influential hyperparameters that can be tuned to optimize the RFR algorithm. In this study, the effects of n_estimators on the RFR model in porosity prediction were investigated. Furthermore, n_estimators ’ interactions with two other key hyperparameters, namely the number of features considered for the best split ( max_features ) and the minimum number of samples required to be at a leaf node ( min_samples_leaf ) were explored. The RFR models were developed using 4 input features, namely, resistivity log (RES), neutron porosity log (NPHI), gamma ray log (GR), and the corresponding depths obtained from the Volve oil field in the North Sea, and calculated porosity was used as the target data. The methodology consisted of 4 approaches. In the first approach, only n_estimators were changed; in the second approach, n_estimators were changed along with max_features ; in the third approach, n_estimators were changed along with min_samples_leaf ; and in the final approach, all three hyperparameters were tuned. Altogether 24 RFR models were developed, and models were evaluated using adjusted R 2 (adj. R 2 ), root mean squared error (RMSE), and their computational times. The obtained results showed that the highest performance with an adj. R 2 value of 0.8505 was achieved when n_estimators was 81, max_features was 2 and min_samples_leaf was 1. In approach 2, when n_estimators’ upper limit was increased from 10 to 100, there was a test model performance growth of more than 1.60%, whereas increasing n_estimators’ upper limit from 100 to 1000 showed a performance drop of around 0.4%. Models developed by tuning n_estimators from 1 to 100 in intervals of 10 had healthy test model adj. R 2 values and lower computational times, making them the best n_estimators’ range and interval when both performances and computational times were taken into consideration to predict the porosity of the Volve oil field in the North Sea. Thus, it was concluded that by tuning only n_estimators and max_features , the performance of RFR models can be increased significantly. 1. Introduction ML application in reservoir characterization has significantly increased over the last couple of decades due to its ability to tackle regression and classification-type problems. 5–7 With the evolution of ML, a notable number of algorithms have been introduced. The artificial neural network (ANN), which uses a parallel processing approach and was developed based on the function of a neuron of a human brain, has been utilized in petrophysical parameter prediction. 8,9 Support vector regression (SVR) is another algorithm developed in the initial stages of the ML timeline, and it can handle non-linear relationships between a set of inputs and an output. Moreover, SVR has been utilized widely in reservoir characterization. 10–13 The least absolute shrinkage and selection operator (LASSO) regression and Bayesian model averaging (BMA) have also been extensively used in ML-related studies in the literature. 14 BMA uses Bayes theorem and LASSO uses residual sums of squares to build a linear relationship between the inputs and the output. BMA and LASSO regressions have been used in permeability modelling in recent studies. 5 Apart from petrophysical parameter predictions, ML models have also been used in lithofacies classification. 15 Generally, these studies utilized ML approaches to model lithofacies sequences as a function of well-logging data to predict discrete lithofacies distribution at missing intervals. 16–18 Besides permeability prediction, water saturation estimation, and lithofacies classification, ML models have been used in reservoir porosity estimation, which is the parameter of focus in this study. ML algorithms, such as ANN, deep learning, and SVR, have been used to predict porosity using logging data, seismic attributes, and drilling parameters. 19–21 Apart from the mentioned ML models, the ML approach known as ensemble learning has been applied in many recent studies. Here, ML base models (weaker models) are strategically combined to produce a high-performing and efficient model as shown in Fig. 1 . Ensemble ML models have become a popular tool among researchers to predict petrophysical properties due to their ability to reduce overfitting and underfitting. 22–26 RFR is one such popular ensemble ML model that was developed by amalgamating multiple decision trees. 27 Representation of the ensemble model. Hyperparameter tuning is a process that is implemented to fine-tune ML algorithms to obtain optimal models. 28–30 Several hyperparameters can be controlled in an RFR model, such as n_estimators , max_features , min_samples_leaf , maximum depth of the tree ( max_depth ), fraction of the original dataset assigned to any individual tree ( max_samples ), minimum number of samples required to split an internal node ( min_samples_split ), maximum leaf nodes to restrict the growth of the tree ( max_leaf_nodes ). Hyperparameter optimization has been utilized in recent studies related to reservoir characterization. Wang et al. developed an RFR model to predict permeability in the Xishan Coalfield, China. 24 Five hyperparameters, n_estimators , max_features , max_depth , min_samples_leaf and min_samples_split , were tuned during hyperparameter optimization. Zou et al. estimated reservoir porosity using a random forest algorithm. 31 During the hyperparameter optimization stage, n_estimators , max_features , min_samples_leaf , min_samples_split and max_depth were tuned. Rezaee and Ekundayo tuned n_estimators , min_samples_leaf , min_samples_split , and max_depth during the development of the RFR model used to predict the permeability of precipice sandstone in the Surat Basin, Australia. 32 Even though hyperparameters have been tuned during the hyperparameter optimization phase of an ensemble ML model development, the literature lacks studies that specifically focus on the effects of hyperparameter tuning in ensemble learning when predicting petrophysical properties in reservoir characterization. Addressing this research gap, in this study, the authors investigated the influence of one of the most utilized hyperparameters in the literature, namely, the n_estimators of RFR, when predicting the porosity of a hydrocarbon reservoir. Also, the effects of n_estimators were studied along with another two widely used hyperparameters, max_features and min_samples_leaf , when predicting the porosity of the Volve oil field in the North Sea. The study considered a supervised learning regression approach. The workflow of the study consisted of data preprocessing, RFR model development, and model analysis. Several RFR models were developed, including tuning n_estimators , tuning n_estimators along with max_features , tuning n_estimators along with min_samples_leaf , and tuning all three hyperparameters at once under four approaches by integrating grid search optimization and K-fold cross-validation. The models’ performances were evaluated based on the adjusted coefficient of determination (adj. R 2 ), root mean squared error (RMSE), and computational time. Only the three aforementioned hyperparameters were considered due to processing capacity limitations; however, this study is expected to be a solid initiation towards the development of future studies on the effects of hyperparameters in ML algorithms in reservoir characterization. 2. Methodology 2.1 geological setting and dataset. Study area – Volve oil field's location in the North Sea. Adapted from Mapchart. The Hugin Formation is 153 m thick and oil-bearing and was penetrated at 3796.5 m, approximately 60 m deeper than expected. The total oil column in the well was 80 m, but no clear oil–water contact was observed. 38,40 The reservoir section was made up of highly variable fine to coarse-grained, well to poorly-sorted subarkosic arenite sandstones with good to excellent reservoir properties. The Hugin Formation of the area consists of a shallow marine shoreface, coastal plain/lagoonal, channel, and possibly mouth bar deposits. The underlying Skagerrak Formation was completely tight due to extensive kaolinite and dolomite cementation. The current study used data from well 15/9-19A. The well was drilled through the Skagerrak Formation and terminated approximately 30 m into the Triassic Smith Bank Formation. To fully utilize the available data, the study considered data from the 3666.59 to 3907.08 m depth interval. This depth interval ran through three formations, namely, Draupne, Heather, and Hugin. The stratigraphic column and description of the vertical facies distribution of the section are shown in Fig. 3 . Stratigraphic column and facies description of the considered subsurface section. Adapted from Statoil.   PHIF = PHID + A × (NPHI − PHID) + B (1)   (2) 2.2 Data preprocessing Feature scaling is also a common practice implemented during data preprocessing. There are two widely used feature scaling approaches in the literature, namely, normalization and standardization. However, in this study feature scaling was neglected since RFR is a tree-based ML model where splits do not change with any monotonic transformation. 52 2.3 Machine learning model development Random Forest architecture (left) and the base model architecture (right).   (3)   E ,Y(Y − (X)) → E ,Y(Y − E h(X;θ)) . (4)   (5)   (6) The inequality shown by eqn (6) highlights what is required for accurate RFR, which is having a low correlation between residuals of differing tree members of the forest and low prediction error for the individual trees. The model's performance can be further enhanced by tuning its hyperparameters. During the study, RFR models were developed using the Python programming language. The cleaned dataset obtained during the data preprocessing stage was loaded into Python, then split into training and testing. The Python-based scikit-learn library's RandomForestRegressor was used to develop the RFR algorithm. The RandomForestRegressor comes with default hyperparameters built into it. Default values assigned to some of the main hyperparameters of RFR in scikit-learn are given in Table 1 . Hyperparameter Default value n_estimators 100 max_features 1.0 min_samples_leaf 1 max_depth None max_samples None min_samples_split 2 max_leaf_nodes None However, rather than using the default hyperparameters assigned by the scikit-lean library, to achieve the primary objectives of the study, hyperparameter optimization was implemented. Hyperparameter optimization is a commonly used practice to build robust ML models. 56,57 The hyperparameters of RFR were tuned using the grid search optimization (GSO) approach. For this, the GridSearchCV optimization algorithm in the scikit-learn library was used. GSO was considered since it runs through all the possible combinations in the hyperparameter space, thus selecting the best combination of the space. 57,58 The hyperparameter space was predefined by including the possible values and it was fed into the GSO algorithm. GSO was implemented along with random subsampling cross-validation. An approach known as the K-fold cross-validation was used. During the K-fold cross-validation, the training dataset is divided into K number of same-sized portions (folds), and K − 1 of the portions are used for training and the remainder are used for validation. 59,60 This is repeated until each fold gets the chance to be the validation set. For this study, a 5-fold cross-validation was implemented as shown in Fig. 5 . Therefore, the training set was divided into five portions and during each split, four portions were used for training and one portion was used for validation. Demonstration of the K-fold cross-validation. Tuning was done under 4 approaches as shown in Fig. 6 to investigate the effects of the considered hyperparameters. In the first approach, n_estimators was changed from 1 to 10, 1 to 100, and 1 to 1000 in different intervals. The notation used to demonstrate the n_estimators change is shown in Table 2 . Workflow of the methodology. n_estimators change notation Starting value Ending value Increment 1 1 10 1 1 1 100 1 1 1 100 10 1 1 1000 1 1 1 1000 10 1 1 1000 100 In the second approach, n_estimators was changed from 1 to 1000 in the same way as approach 1 along with max_features . Here, max_features was changed from 10% to 100% of total features in increments of 10%. In the third approach, n_estimators was changed in the same way along with min_samples_leaf . In this case, min_samples_leaf was changed from 1 to 20 in intervals of 1. In the fourth approach, all 3 hyperparameters, i.e. , n_estimators , max_features and min_samples_leaf were varied at the same time in the above-mentioned intervals. In each approach, values of all the other hyperparameters of RFR were kept at their default values assigned by the scikit-learn library. The link to the GitHub folder with the developed codes is given in the appendix. 2.4 Results analysis   (7)   (8) In eqn (7) and (8) , y i is the actual value, ŷ is the predicted value, ȳ is the mean value of the distribution, n is the number of data points and m is the number of input features.   (9) 3. Results and discussion Model no. n_estimators change n_estimators Adj. R Computational time (s) Training Validation Testing M11 1 8 0.9650 0.8188 0.8024 0.81 M12 1 51 0.9760 0.8367 0.8202 70.25 M13 1 51 0.9760 0.8367 0.8202 6.88 M14 1 51 0.9760 0.8367 0.8202 6932.55 M15 1 51 0.9760 0.8367 0.8202 707.56 M16 1 801 0.9799 0.8352 0.8218 65.73 Adjusted coefficient of determination values of each approach for different changes in n_estimators. Interestingly, when the upper limit of the n_estimators range was pushed beyond 100, the performance of the model did not show any noticeable increase in all training validation and testing adj. R 2 values. When n_estimators changed from 1 to 100 in intervals 1 and 10 (models M12 and M13) and n_estimators changed from 1 to 1000 in intervals 1 and 10 (models M14 and M15), the models showed the same performance, i.e. a training score of 0.9760, validation score of 0.8367 and a testing score of 0.8202. However, when the n_estimators changed from 1 to 1000 in intervals of 100, the training and testing scores of the M16 model showed a slight increase in performance, yielding an adj. R 2 of 0.9799 and 0.8218, respectively. However, the validation score showed a slight decrease, which was negligible. The highest computational time of 6932.55 seconds was shown by the model M14 where n_estimators changed from 1 to 1000 in increments of 1. The results from approach 1 showed that after a certain n_estimators value, the models’ performances increased drastically and the performance was maintained at a constant value over a certain n_estimators range showing that the performance of the RFR when n_estimators was tuned was efficient within a certain range. Since the range and interval at which the n_estimators values are tuned affect the computational time, an effective range and an interval for n_estimators should be decided upon, taking computational time into account. In approach 2, max_features were also tuned along with n_estimators . Results obtained using approach 2 of the methodology are tabulated in Table 4 . As observed in approach 1, clear spikes in training, validation, and testing adj. R 2 values were observed when the upper limit of n_estimators was increased from 10 to 100. The training score had an increase of 1.36%, the validation score had an increase of 1.92%, and the test score had an increase of 1.60%. This clear jump in performance is noticeable in Fig. 7 . Interestingly, the performances of the models developed in approach 2 were significantly higher than the performance of the corresponding “ n_estimators change” in approach 1. This is quite visible in Fig. 8 . Further, going from approach 1 to 2, the average validation score increased by 2.24% and the testing score increased by 3.52%, which was significant. This increase in adj. R 2 values is an indication that tuning max_features has a major impact on predicting the porosity using RFR. Model M21, where n_estimators were changed from 1 to 10 in intervals of 1 and max_features were changed from 0.1 to 1 in intervals of 0.1, showed the least performance with a training score of 0.9672, validation score of 0.8381, and a testing score of 0.8366. On the other hand, model M23 showed the highest testing performance with an adj. R 2 of 0.8505 where n_estimators changed from 1 to 100 in intervals of 10 and max_features changed from 0.1 to 1 in intervals of 0.1. The model M23 yielded its best test model when n_estimators was 81 and max_features were 0.5. It should be noted that even though model M23 had the highest testing score, the training, and validation scores were not the best out of all the models developed in approach 2. The highest training score of 0.9823 was shown by models M24, M25, and M26. The highest validation scores were shown by models M24 and M25. However, it is more meaningful to select model M23 as the best-performing model since the testing set represents an independent dataset that had never been seen by the model before. Model no. n_estimators change n_estimators max_features Adj. R Computational time (s) Training Validation Testing M21 1 9 0.1 0.9672 0.8381 0.8366 3.69 M22 1 79 0.5 0.9804 0.8542 0.8500 326.56 M23 1 81 0.5 0.9806 0.8541 0.8505 30.20 M24 1 520 0.5 0.9823 0.8556 0.8467 32 M25 1 521 0.5 0.9823 0.8556 0.8467 3045.27 M26 1 801 0.5 0.9823 0.8554 0.8471 284.29 Adjusted coefficient of determination values for each change in n_estimators for different approaches. The anomaly in the validation score observed when the n_estimators were changed from 1 to 1000 in intervals of 100 in approach 1 was also observable in approach 2. The difference in train–test scores provides an idea about the generalizability of the model. The smaller the train–test difference, the higher the generalizability of the model. Overall, the train–test difference in approach 2 was noticeably less than that of approach 1. The average train–test difference decreased by 15.51% on going from approach 1 to 2. This showed that the generalizability of the models improved when max_features was introduced into the hyperparameter space. Similar to approach 1, the highest runtime was shown when the n_estimators changed from 1 to 1000 in increments of 1. In approach 3, n_estimators was investigated with the alteration of min_samples_leaf , and the results obtained are tabulated in Table 5 . Notably, all the performance results obtained for all the RFR models except the runtimes were the same as that of approach 1, as seen in Fig. 7 and 8 . This was because the optimum value selected by the grid search optimization of the min_samples_leaf was the same as the default value assigned by the scikit-learn library for the RFR algorithm, hence the best testing adj. R 2 was shown by model M34 when the n_estimators was changed from 1 to 1000 in intervals of 100. Computational times were longer than those obtained in approach 1 since models developed in approach 3 had a larger hyperparameter space as compared to approach 1. Model no. n_estimators change n_estimators min_samples_leaf Adj. R Computational time (s) Training Validation Testing M31 1 8 1 0.9650 0.8188 0.8024 7.79 M32 1 51 1 0.9760 0.8367 0.8202 674.81 M33 1 51 1 0.9760 0.8367 0.8202 64.96 M34 1 51 1 0.9760 0.8367 0.8202 70 M35 1 51 1 0.9760 0.8367 0.8202 6525.18 M36 1 801 1 0.9799 0.8352 0.8218 606.28 Model no. n_estimators change n_estimators max_features min_samples_leaf Adj. R Computational time (s) Training Validation Testing M41 1 9 0.1 1 0.9672 0.8381 0.8366 56.22 M42 1 79 0.5 1 0.9804 0.8542 0.8500 4242.86 M43 1 81 0.5 1 0.9806 0.8541 0.8505 425.65 M44 1 520 0.5 1 0.9823 0.8556 0.8467 82 M45 1 521 0.5 1 0.9823 0.8556 0.8467 51 M46 1 801 0.5 1 0.9823 0.8554 0.8471 3796.99 Table 7 shows the RMSE values of approaches 1, 2, 3, and 4. While the adj. R 2 values give an idea about the correlation between the actual porosities and the predicted porosities, the RMSE values provide an idea about the difference (or the error) between the two. Therefore, RMSE is also an important parameter in ML model performance evaluation. The pattern in which RMSE values fluctuated in the 4 approaches was similar to that of adj. R 2 . The smallest RMSEs were shown by model M16 with a training model RMSE of 0.9988 and a testing model RMSE of 2.8312. The improvement in the results when max_features was introduced into the hyperparameter space was also evident based on the RMSE values obtained in approach 2. There was a clear decrease in RMSE values in both training and testing models in approaches 2 and 4 where max_features was tuned. RMSE Approach 1 Approach 2 Approach 3 Approach 4 Model no. Training Testing Model no. Training Testing Model no. Training Testing Model no. Training Testing M11 1.2894 2.9967 M21 1.2516 2.7218 M31 1.2894 2.9967 M41 1.2516 2.7218 M12 1.0817 2.8499 M22 0.9835 2.5917 M32 1.0817 2.8499 M42 0.9835 2.5917 M13 1.0817 2.8499 M23 0.9798 2.5875 M33 1.0817 2.8499 M43 0.9798 2.5880 M14 1.0817 2.8499 M24 0.9399 2.6190 M34 1.0817 2.8499 M44 0.9399 2.6190 M15 1.0817 2.8499 M25 0.9396 2.6187 M35 1.0817 2.8499 M45 0.9396 2.6187 M16 0.9988 2.8312 M26 0.9396 2.6148 M36 0.9988 2.8312 M46 0.9396 2.6148 Runtime and grid search combinations had a positive relationship, i.e. , when the number of combinations in the grid search space was the largest, the runtime of the model was the highest, and vice versa . Further, it was observed that from approach 1 to approach 3, the increase in computational times was roughly proportional to each other as seen in Fig. 9 . However, in approach 4 where n_estimators was changed along with the tuning of max_features and min_samples_leaf , an anomaly was observed when n_estimators was changed from 1 to 1000 in intervals of 10. Runtimes of the models of each n_estimators’ change for different approaches. Even though the primary objective of the study was to investigate the influences of n_estimators along with max_features and min_samples_leaf on the performance of RFR, having an overall picture of the variation of the actual and predicted porosity and their relationship is important to understand the model's applicability in porosity prediction. To achieve this, depth-porosity graphs and correlation plots were plotted. Fig. 10 shows one such depth-porosity graph and a correlation plot developed for the best-performing RFR test model (model M23) of the study. The depth-porosity plot indicated that most of the time, the predicted porosity followed the pattern of the actual porosity. The correlation plot showed that the majority of the points were scattered around the perfect correlation line, which is an indication of a high correlation between the actual values and the predicted values. Depth-porosity and correlation plots obtained from the predictions of the best-performing RFR testing model. 4. Conclusions • Overall, based on both the performance and computational time, the RFR model with n_estimators at 81 and max_features at 2 (while keeping all the other hyperparameters at their default values), which was developed in approach 2, produced the most effective model for predicting the porosity of the Volve oil field in the North Sea with a testing model adj. R 2 of 0.8505, a testing model RMSE of 2.5875, and a computational time of 30.2 seconds. • There was a notable increase in performance when the upper limit of the n_estimators increased from 10 to 100. On the other hand, the performance of the models did not increase significantly when the upper limit of n_estimators increased from 100 to 1000. This phenomenon indicated that identifying an effective n_estimators range that is not too low (which will make the performance significantly low) and not too high (which will increase the computational time) is important to produce an efficient RFR model during porosity prediction. • A range of 1 to 100 changed in intervals of 10 can be suggested for n_estimators when developing an RFR model to predict the porosity of the Volve oil field since these models showed higher performances and lower computational times in all four approaches. When the n_estimators range of 1 to 100 was changed in intervals of 10, it always yielded a high adj. R 2 value (in approaches 2 and 4, it yielded the highest testing model adj. R 2 value) for the model and had the second least computational time. • When n_estimators was tuned along with max_features in approach 2, the results improved drastically as compared to approach 1 where only n_estimators was tuned. There was an average validation score increase of 2.24% and a testing score increase of 3.52% on going from approach 1 to 2. This improvement of the scores (adj. R 2 ) showed that max_features has a significant influence on the RFR model's performance. • It was observed that computational time was largely affected by the number of hyperparameters altered, their range, and interval. Of all the approaches, the longest computational time was when n_estimators was tuned from 1 to 1000 in intervals of 1 along with max_features and min_samples_leaf . Based on the results, only by adjusting n_estimators and max_features can an RFR model be developed with a robust prediction power to estimate the porosity in the Volve oil field. Recommendations Abbreviation. AI Artificial intelligence ML Machine learning RFR Random forest regression ANN Artificial neural network SVR Support vector regression LASSO Least absolute shrinkage and selection operator BMA Bayesian model averaging GSO Grid search optimization RMSE Root mean squared error R Coefficient of determination adj. R Adjusted coefficient of determination RES Resistivity log NPHI Neutron porosity log GR Gamma ray log PHIF Total porosity PHID Porosity from density log n_estimators Number of trees in the forest max_features Number of features considered for the best split min_samples_leaf Minimum number of samples required to be at a leaf node max_depth Maximum depth of the tree max_samples Fraction of the original dataset assigned to any individual tree min_samples_split Minimum number of samples required to split an internal node max_leaf_nodes Maximum leaf nodes to restrict the growth of the tree A A regression coefficient B A regression coefficient ρ Matrix density ρ Measured bulk density ρ Pore fluid density n Number of datapoints m Number of input features X Independent and identically distributed random vector θ Independent and identically distributed random vector x Observed input vector associated with vector X Y A vector with numerical outcomes y Actual value ŷ Predicted value ȳ Mean value of the distribution Author contributions Data availability, conflicts of interest, acknowledgements. 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Kim, Machine learning-based boosted regression ensemble combined with hyperparameter tuning for optimal adaptive learning, Sensors , 2022, 22 (10), 3776  Search PubMed . C. Zou, L. Zhao, M. Xu, Y. Chen and J. Geng, Porosity prediction with uncertainty quantification from multiple seismic attributes using random forest, J. Geophys. Res.: Solid Earth , 2021, 126 (7), e2021JB021826  Search PubMed . R. Rezaee and J. Ekundayo, Permeability prediction using machine learning methods for the CO 2 injectivity of the precipice sandstone in Surat Basin, Australia, Energies , 2022, 15 (6), 2053  Search PubMed . S. García, J. Luengo and F. Herrera, Introduction to data preprocessing, in Data preprocessing in data mining , ed. J. Kacprzyk and L. C. Jain, Springer International Publishing, Cham, Switzerland, 2015, pp. 10–13  Search PubMed . V. Gudivada, A. Apon and J. Ding, Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations, Int. J. Adv. 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World map: simple [Internet]. 2024 [cited 2024 Jul 22]. Available from: https://www.mapchart.net/world.html. S. Sen and S. S. Ganguli Estimation of pore pressure and fracture gradient in Volve field, Norwegian North Sea. InSPE Oil and Gas India Conference and Exhibition. 2019 Apr 8 (p. D022S027R002). SPE. Statoil. 15/9-19A Well Composite Log, Sleipner, Theta Vest Prospect Structure [Internet]. 1998 [cited 2023 Mar 1]. Available from: https://discovervolve.com/citation-non-commerciality-clause/. I. F. Ilyas and X. Chu, Data cleaning , Morgan & Claypool, 2019  Search PubMed . A. Jain, H. Patel, L. Nagalapatti, N. Gupta, S. Mehta, S. Guttula, S. Mujumdar, S. Afzal, R. Sharma Mittal and V. Munigala Overview and importance of data quality for machine learning tasks. InProceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining 2020 Aug 23 (pp. 3561–3562). S. Rawat, A. Rawat, D. Kumar and A. S. Sabitha, Application of machine learning and data visualization techniques for decision support in the insurance sector, Int. J. Inf. Manag. Data Insights , 2021, 1 (2), 100012  Search PubMed . M. M. Ahamad, S. Aktar, M. Rashed-Al-Mahfuz, S. Uddin, P. Liò, H. Xu, M. A. Summers, J. M. Quinn and M. A. Moni, A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients, Expert Syst. Appl. , 2020, 160 , 113661  Search PubMed . I. H. Sarker, Y. B. Abushark, F. Alsolami and A. I. Khan, Intrudtree: a machine learning based cyber security intrusion detection model, Symmetry , 2020, 12 (5), 754  Search PubMed . H. Feizi, H. Apaydin, M. T. Sattari, M. S. Colak and M. Sibtain, Improving reservoir inflow prediction via rolling window and deep learning-based multi-model approach: case study from Ermenek Dam, Turkey, Stoch. Environ. Res. Risk Assess. , 2022, 36 (10), 3149–3169  Search PubMed . J. J. Salazar, L. Garland, J. Ochoa and M. J. 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Sarno Implementation of random forest regression for COCOMO II effort estimation. In2020 international seminar on application for technology of information and communication (iSemantic) 2020 Sep 19 (pp. 476–481). IEEE. S. Yilmazer and S. Kocaman, A mass appraisal assessment study using machine learning based on multiple regression and random forest, Land use policy , 2020, 99 , 104889  Search PubMed . M. R. Segal Machine learning benchmarks and random forest regression. M. Abbaszadeh, S. Soltani-Mohammadi and A. N. Ahmed, Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method, Comput. Geosci. , 2022, 165 , 105140  Search PubMed . M. A. Abbas, W. J. Al-Mudhafar and D. A. Wood, Improving permeability prediction in carbonate reservoirs through gradient boosting hyperparameter tuning, Earth Sci. Inform. , 2023, 16 (4), 3417–3432  Search PubMed . K. Sandunil, Z. Bennour, H. Ben Mahmud and A. Giwelli Effects of Tuning Hyperparameters in Random Forest Regression on Reservoir's Porosity Prediction. Case Study: Volve Oil Field, North Sea. InARMA US Rock Mechanics/Geomechanics Symposium 2023 Jun 25 (pp. ARMA-2023). ARMA. W. J. Al-Mudhafar Incorporation of bootstrapping and cross-validation for efficient multivariate facies and petrophysical modeling. InSPE Rocky Mountain Petroleum Technology Conference/Low-Permeability Reservoirs Symposium 2016 May 5 (pp. SPE-180277). SPE. M. Rahimi and M. A. Riahi, Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield, J. Appl. Geophys. , 2022, 201 , 104640  Search PubMed . A. A. Mahmoud, S. Elkatatny, W. Chen and A. Abdulraheem, Estimation of oil recovery factor for water drive sandy reservoirs through applications of artificial intelligence, Energies , 2019, 12 (19), 3671  Search PubMed . H. Al Khalifah, P. W. Glover and P. 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Blog The Education Hub https://educationhub.blog.gov.uk/2024/08/20/gcse-results-day-2024-number-grading-system/ GCSE results day 2024: Everything you need to know including the number grading system Thousands of students across the country will soon be finding out their GCSE results and thinking about the next steps in their education.    Here we explain everything you need to know about the big day, from when results day is, to the current 9-1 grading scale, to what your options are if your results aren’t what you’re expecting.   When is GCSE results day 2024?   GCSE results day will be taking place on Thursday the 22 August.      The results will be made available to schools on Wednesday and available to pick up from your school by 8am on Thursday morning.   Schools will issue their own instructions on how and when to collect your results.    When did we change to a number grading scale?   The shift to the numerical grading system was introduced in England in 2017 firstly in English language, English literature, and maths.   By 2020 all subjects were shifted to number grades. This means anyone with GCSE results from 2017-2020 will have a combination of both letters and numbers.   The numerical grading system was to signal more challenging GCSEs and to better differentiate between students’ abilities - particularly at higher grades between the A *-C grades. There only used to be 4 grades between A* and C, now with the numerical grading scale there are 6.   What do the number grades mean?   The grades are ranked from 1, the lowest, to 9, the highest.   The grades don’t exactly translate, but the two grading scales meet at three points as illustrated below.   The bottom of grade 7 is aligned with the bottom of grade A, while the bottom of grade 4 is aligned to the bottom of grade C.     Meanwhile, the bottom of grade 1 is aligned to the bottom of grade G.   What to do if your results weren’t what you were expecting?   If your results weren’t what you were expecting, firstly don’t panic. You have options.   First things first, speak to your school or college – they could be flexible on entry requirements if you’ve just missed your grades.    They’ll also be able to give you the best tailored advice on whether re-sitting while studying for your next qualifications is a possibility.    If you’re really unhappy with your results you can enter to resit all GCSE subjects in summer 2025. You can also take autumn exams in GCSE English language and maths.   Speak to your sixth form or college to decide when it’s the best time for you to resit a GCSE exam.   Look for other courses with different grade requirements      Entry requirements vary depending on the college and course. Ask your school for advice, and call your college or another one in your area to see if there’s a space on a course you’re interested in.     Consider an apprenticeship     Apprenticeships combine a practical training job with study too. They’re open to you if you’re 16 or over, living in England, and not in full time education.   As an apprentice you’ll be a paid employee, have the opportunity to work alongside experienced staff, gain job-specific skills, and get time set aside for training and study related to your role.    You can find out more about how to apply here .   Talk to a National Careers Service (NCS) adviser     The National Career Service is a free resource that can help you with your career planning. Give them a call to discuss potential routes into higher education, further education, or the workplace.    Whatever your results, if you want to find out more about all your education and training options, as well as get practical advice about your exam results, visit the  National Careers Service page  and Skills for Careers to explore your study and work choices.    You may also be interested in: Results day 2024: What's next after picking up your A level, T level and VTQ results? When is results day 2024? GCSEs, A levels, T Levels and VTQs Tags: GCSE grade equivalent , gcse number grades , GCSE results , gcse results day 2024 , gsce grades old and new , new gcse grades Sharing and comments Share this page, related content and links, about the education hub. The Education Hub is a site for parents, pupils, education professionals and the media that captures all you need to know about the education system. You’ll find accessible, straightforward information on popular topics, Q&As, interviews, case studies, and more. Please note that for media enquiries, journalists should call our central Newsdesk on 020 7783 8300. This media-only line operates from Monday to Friday, 8am to 7pm. Outside of these hours the number will divert to the duty media officer. Members of the public should call our general enquiries line on 0370 000 2288. Sign up and manage updates Follow us on social media, search by date. August 2024 M T W T F S S   1 2 3 4 5 7 8 9 10 11 13 14 15 16 17 18 21 22 23 24 25 26 27 29 30 31   Comments and moderation policy WIC Participant and Program Characteristics 2018 In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis. FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2018 Participant and Program Characteristics (PC2018) is the fourteenth data submission to be completed using the WIC PC reporting system. In April 2018, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Processing methods and equipment used Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2018. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency.   Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2018. Study spatial scale (size of replicates and spatial scale of study area) In April 2018, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) State Agency Data Submissions. PC2018 is a participant dataset consisting of 7,837,672 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data. PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 7.8 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public. National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The national sample consists of 1 percent of the total number of participants, or 78,365 records. The distribution by category is 6,825 pregnant women, 6,189 breastfeeding women, 5,134 postpartum women, 18,552 infants, and 41,665 children. Level of subsampling (number and repeat or within-replicate sampling) The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers. Description of any gaps in the data or other limiting factors All State agencies except New Mexico provided data on a census of their WIC participants. Resource Title: WIC Participant and Program Characteristics 2018 Data. File Name: wicpc.wicpc2018_public_use.csv Resource Title: WIC Participant and Program Characteristics 2018 Dataset Codebook. File Name: PC2018 National Sample File Public Use Codebook updated.docx Resource Description: The 2018 Participant and Program Characteristics (PC2018) is the fourteenth data submission to be completed using the WIC PC reporting system. In April 2018, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Resource Title: WIC Participant and Program Characteristics 2018 Datasets SAS STATA SPSS. File Name: wicpc2018_agdatacoomonsupload.zip USDA-FNS: Contract No. No. AG-3198-C-11-0010 Data contact name, data contact email, intended use, use limitations, temporal extent start date, temporal extent end date. Not specified Geographic Coverage Geographic location - description, iso topic category, national agricultural library thesaurus terms, omb bureau code. 005:84 - Food and Nutrition Service OMB Program Code 005:040 - National Research Pending citation Public access level, preferred dataset citation, usage metrics. Food sciences Food nutritional balance Agricultural economics Strategies for Improving Sustainable Rice Seed Supply Chain Performance in Indonesia: A Case Study in Bali Province Description. The sustainability of the rice seed supply chain still needs to be improved to ensure the availability of rice seeds. To achieve food security (rice) cannot be separated from the availability of seeds. Data on sustainability attributes according to farmer groups, farmers implementing multiplication (cooperators), seed producers and key informants are used in analyzing the level of sustainability of the rice seed supply chain. Data were obtained through surveys and in-depth discussions with research objects. The tabulated data is continued by analyzing the data, then the results are found which are then juxtaposed with the criteria and results of previous research. Steps to reproduce The data used in this study are primary and secondary data. Primary data was obtained through interviews and field observations. Primary data includes: data related to preferences for VUB, partnerships, respondent characteristics, descriptive data on supply chain activities (qualitative and quantitative), data on the relationship between Key Performance Index (KPI) from ANP software, data on indicators of rice seed supply chain sustainability, data on the influence between sustainability variables. The time span of the data used is a six-year period from 2017 to 2022. Secondary data includes: data on producers and production of rice seeds, profiles of seed producers, data on groups (Subak) of paddy fields, rice area and production, agricultural labor, production costs and other data related to rice seed supply chain activities in Bali Province. Using the in-depth interview method, primary data was gathered through talks and interviews with key informants who included experts, practitioners and regulators. This study employs the Multi-dimensional Scaling and Rapid Appraisal for Sustainability (MDS-RAPS) approach to analyze the sustainable rice seed supply chain, followed by prospective analysis to generate expected sustainability strategies. The use of tools such as Super Decisions Software in implementing the Analytic Network Process (ANP). Sustainability data processing using Microsoft Excel software with Rapfish application and Prospective analysis using exsimpro software Institutions 463 IMAGES The case study data collection and analysis process (an author's view How to Customize a Case Study Infographic With Animated Data case study data interpretation DATA SETS OF CASE STUDY How to Perform Case Study Using Excel Data Analysis Data Analysis Case Study: Learn From These #Winning Data Projects VIDEO Data Science Research Showcase Case Function In Google Data Studio: Example & Use Cases Complete Data Structures and Algorithm Revision through Most Repeated PYQs| Exam Special Revision Lecture_55: Capstone Project on Data Analysis and Visualizations Case Study: Data Analyst တွေအတွက် Domain Knowledge ကဘာကြောင့်အရေးကြီးတာလဲ ? Socket Set Case Upgrade #tools #automobile #mechanic #socketset COMMENTS 10 Real World Data Science Case Studies Projects with Example Data Analytics Case Study Examples in Travel Industry . Below you will find case studies for data analytics in the travel and tourism industry. 5) Airbnb. ... Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on ... Statistics Case Study and Dataset Resources The data is contextualized, provided for download in multiple formats, and includes questions to consider as well as references for each data set. The case studies for the current year can be found by clicking on the "Meetings" tab in the navigation sidebar, or by searching for "case study" in the search bar. Journal of Statistics Education Top 25 Data Science Case Studies [2024] Data-Driven Decision-Making: Enables better farming decisions through timely and accurate data. Case Study 14 - Streamlining Drug Discovery (Pfizer) ... As we look to the future, the role of data science is set to grow, promising even more innovative solutions and smarter strategies across all sectors. These case studies inspire and serve as ... 12 Data Science Case Studies: Across Various Industries Top 12 Data Science Case Studies. 1. Data Science in Hospitality Industry. In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more. Airbnb focuses on growth by analyzing customer voice using data science. A famous example in this sector is ... Free Public Data Sets For Analysis Public data sets are ideal resources to tap into to create data visualizations. With the information provided below, you can explore a number of free, accessible data sets and begin to create your own analyses. The following COVID-19 data visualization is representative of the the types of visualizations that can be created using free public ... 10 Real-World Data Science Case Studies Worth Reading Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data ... Case Study Library Mendel's Laws of Inheritance. Use the data sets provided to explore Mendel's Laws of Inheritance for dominant and recessive traits. Key words: Bar charts, frequency distributions, goodness-of-fit tests, mosaic plot, hypothesis tests for proportions. Download the case study (PDF) Download the data set 1. Download the data set 2. Data in Action: 7 Data Science Case Studies Worth Reading 7 Top Data Science Case Studies . Here are 7 top case studies that show how companies and organizations have approached common challenges with some seriously inventive data science solutions: Geosciences. Data science is a powerful tool that can help us to understand better and predict geoscience phenomena. A Dataset Exploration Case Study with Know Your Data A KYD Case Study. As a case study, we explore some of these features using the COCO Captions dataset, an image dataset that contains five human-generated captions for each of over 300k images. Given the rich annotations provided by free-form text, we focus our analysis on signals already present within the dataset. Data Analysis Case Study: Learn From These Winning Data Projects Step 2: Review Data Case Studies. Here we are, already at step 2. It's time for you to start reviewing data analysis case studies (starting with the one I'm sharing below). Identify 5 that seem the most promising for your organization given its current set-up. 15 Free Data Sets for Your Next Project or Portfolio Data.gov. Data.gov is where all of the American government's public data sets live. You can access all kinds of data that is a matter of public record in the country. The main categories of data available are agriculture, climate, energy, local government, maritime, ocean, and older adult health. Practice take-home case study (datasets/code included) Going through several of these ourselves, and getting tips from friends, we've compiled a practice take home case study. Let us know what you think and we look forward to your feedback! 10. Award. jambery. • 6 yr. ago. Awesome insights into a realistic dataset! 2. Award. Case Study Method: A Step-by-Step Guide for Business Researchers Qualitative case study methodology enables researchers to conduct an in-depth exploration of intricate phenomena within some specific context. ... Villiers and Fouché (2015) depicted a paradigm as a set framework making various assumptions about the social world, about how ... The authors interpreted the raw data for case studies with the help ... Google Data Analytics Capstone: Complete a Case Study There are 4 modules in this course. This course is the eighth and final course in the Google Data Analytics Certificate. You'll have the opportunity to complete a case study, which will help prepare you for your data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you'll ... Data Science Use Cases Guide Data science use case in transport and logistics: Identifying the optimal positioning of taxi vehicles. Uber Technologies Inc., or Uber, is an American company that provides various logistics and transport services. In this case study, we're going to cluster Uber ride-sharing GPS data to identify the optimal positioning of taxi vehicles. Data Exploration Each environment included only the hardware each firm required, alongside premiere software and data. FactSet Data Exploration provided a turnkey solution, and granted users across Firm A and Firm B access to industry-standard tools such as Microsoft SQL Server, MATLAB, Python, R Studio, and Tableau. In addition, all of FactSet's Standard ... What is a Case Study? A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study. What are Cases in Statistics? (Definition & Examples) For example, the following dataset contains 10 cases and 3 variables that we measure for each case: Notice that each case has multiple variables or "attributes." For example, each player has a value for points, assists, and rebounds. Note that cases are also sometimes called experimental units. These terms are used interchangeably. Power BI Case Study with Practice Datasets Power BI Case Study - CFI Capital Partners Learning Objectives. Upon completing this course, you will be able to: Transform data in Power Query and create a data model and DAX measures. Analyze and visualize data by creating report visuals. Build in better user experiences with functionality like Page Drillthrough, Bookmarks, and Conditional ... Datasets for Credit Risk Modeling Data Set Mortgage. The data set mortgage is in panel form and reports origination and performance observations for 50,000 residential U.S. mortgage borrowers over 60 periods. The periods have been deidentified. As in the real world, loans may originate before the start of the observation period (this is an issue where loans are transferred ... How to Analyze a Dataset: 6 Steps 6 Steps to Analyze a Dataset. 1. Clean Up Your Data. Data wrangling —also called data cleaning—is the process of uncovering and correcting, or eliminating inaccurate or repeat records from your dataset. During the data wrangling process, you'll transform the raw data into a more useful format, preparing it for analysis. University Libraries purchases Sage Research Methods package Ohio University Libraries has purchased Sage Research Methods, a platform that includes textbooks, foundation research guidelines, data sets, code books, peer-reviewed case studies and more with updates through 2029. If members of the OHIO community are looking to explore a new research methodology ... Data Sets for Cases Data Sets for Cases. (See related pages) Donald, Bowersox 6e, Supply Chain Logistics Management, 2024 - 1265072604. Case 6 Western Pharmaceutical A Data. Case 7 Western Pharmaceutical B Data. Case 8 Figure 1 Woodson Chemical Company North America Division organization structure. Case 10 - Cooper Processing Solution. Case 11 - Dream Beauty Solution. Implementation of the World Health Organization Minimum ... Background: The National Disaster Management Agency (Badan Nasional Penanggulangan Bencana) handles disaster management in Indonesia as a health cluster by collecting, storing, and reporting information on the state of survivors and their health from various sources during disasters. Data were collected on paper and transferred to Microsoft Excel spreadsheets. Effects of tuning decision trees in random forest regression on A case study: volve oil field, north sea ... and the remainder are used for validation. 59,60 This is repeated until each fold gets the chance to be the validation set. For this study, ... K. Smith, N. Kadkhodayan, M. Leung and K. Baldwin, Reservoir Characterization, Machine Learning and Big Data-An Offshore California Case Study. InSPE ... User-centered design in brain-computer interfaces—A case study Objective: The array of available brain-computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the ... GCSE results day 2024: Everything you need to know including the number Apprenticeships combine a practical training job with study too. They're open to you if you're 16 or over, living in England, and not in full time education. As an apprentice you'll be a paid employee, have the opportunity to work alongside experienced staff, gain job-specific skills, and get time set aside for training and study related ... Environment-Specific Stable Carbon Isotope Fractionation of Raw data for all figures and models in the manuscript by Cong-cong Guo to be published by Geochimica et Cosmochimica Acta as "Environment-Specific Stable Carbon Isotope Fractionation of Phytoplankton As the Basis in Better Constraining Marine Bulk Particulate Organic Carbon Dynamics and Budgets: The case study in a Temperate Coastal Ocean (the Yellow Sea)" WIC Participant and Program Characteristics 2018 In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic ... Strategies for Improving Sustainable Rice Seed ... The sustainability of the rice seed supply chain still needs to be improved to ensure the availability of rice seeds. To achieve food security (rice) cannot be separated from the availability of seeds. Data on sustainability attributes according to farmer groups, farmers implementing multiplication (cooperators), seed producers and key informants are used in analyzing the level of ... assignment essay homework paper phd resume review speech thesis