research data science summer school

Courses from June 22, 2024 — August 18, 2024

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Explore Data Science at Stanford This Summer

Courses on Data Science

Plan your summer around data science. Build a strong foundational set of skills with Data Science 101 and develop a deep understanding of complex data sets with Data Mining and Analysis. Choose from a wide variety of courses such as Solving Social Problems with Data or Introduction to Regression Models and Analysis of Variance.

This program is currently open to undergraduate, graduate, and gap year students. At this time, our high school program is at a waitlist.

Program Dates

June 24 – august 20, 2023, how it works.

• APPLY to attend our 8-week program.
• RECEIVE and accept your admission. 
• REGISTER for courses.
• ATTEND as a Residential or Commuter student.

Want more information?

I’ve had a lot of experience with machine learning and data mining. This has given me a completely different perspective on how you can see the process of machine learning and data mining.

Keep an open mind—try to put yourself out there and talk to as many people as possible. Part of the experience is that there are people from more than 30 different countries in this program—it’s a concentrated space for you to meet people from all over the world. I think this has been one of the fastest and most incredible experiences to actually get to know people—to get to talk to them and hear from them about their own personal experiences is something only IHP is able to provide us.

Summer at Stanford Sets You Apart

Stanford Summer Session is the only opportunity to be a visiting Stanford Student. Get ahead with foundational core classes, make progress towards your chosen course of study, or try something new. You'll have the opportunity to experience all that Stanford has to offer: teaching and academic support, campus activities, access to libraries and fitness facilities, and a thriving campus in the heart of Silicon Valley. Live on campus or commute to class. 

Residential space is limited.

Data Science Courses

Stanford Summer Session provides high-achieving and ambitious students a transformative educational experience at a world-class university. By combining challenging academics with a rich array of extra-curricular programming, Stanford Summer Session successfully shares the University’s culture of innovation, academic excellence, and global responsibility.

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Summer Program for Undergraduates in Data Science (SPUDS)

DEADLINE:  Wednesday, February 14, 2024 at 11:59pm

BRIEF DESCRIPTION: The  Summer Program for Undergraduates in Data Science (SPUDS) is a ten-week summer program, co-sponsored by Harvard College and the Harvard Data Science Initiative (HDSI), that aims to provide a formative and substantive data science research experience and to promote community, creativity, and scholarship amongst Harvard College students.  SPUDS will support Fellows with interests in computer science, mathematics, and statistics, including those who are interested in data science applications across the arts, humanities, sciences and more. We also encourage SPUDS projects that focus on or emphasize ethical practices in data science research.

To participate in SPUDS, prospective fellows should seek a research collaboration with a Harvard faculty host, and apply to SPUDS directly through the Office of Undergraduate Research and Fellowships. It is not necessary to have secured a research position by the SPUDS application deadline. Fellows will work with Harvard-affiliated researchers and live in one of the Harvard College houses with other fellows in the Summer Undergraduate Research Village. As part of SPUDS, fellows will participate in rich evening programming, including both social and academic activities, and become members of a vibrant intellectual and social summer community.

SPUDS will run from early June 2024 through mid August 2024. Participants must commit to the full ten weeks of the program.

Download URAF Applications

What does SPUDS stand for? SPUDS stands for Summer Program for Undergraduates in Data Science.

Who is eligible for SPUDS? Any continuing Harvard undergraduate (current freshmen, sophomores, and juniors) in good standing conducting research with Harvard-affiliated Faculty over the summer in the Cambridge-Boston-Longwood area is eligible to apply for the Program.

Is the program limited to applicants concentrating in computer science, mathematics, or statistics? No, applicants may concentrate in any academic discipline. In fact, SPUDS encourages interdisciplinary research and welcomes projects that engage the arts, humanities, social sciences, natural and physical sciences, and more.

Are there limitations to the kind of research I can do and still be eligible for SPUDS? Since SPUDS encourages interdisciplinary work and research across academic fields, proposed projects do not need to be located within a specific academic discipline. Projects may be grounded in computer science, mathematics, and statistics—but applications for these fields stretch far beyond these fields!

Where can do I do research and participate in SPUDS? SPUDS Fellows must work with a Harvard-affiliated faculty member on their project. Faculty hosts can be appointed in the Faculty of Arts and Science, Harvard’s other graduate and professional schools, Harvard’s affiliated hospitals and research institute, or elsewhere. For questions about eligibility, please reach out to URAF at [email protected] .

Do I need to have my project and faculty member confirmed when I apply for SPUDS? You may apply to SPUDS without having solidified your research project and faculty host for the summer. If you are in this situation, please respond to the first essay prompt by describing your research interest in data science, what you hope to learn, and how you expect this summer research experience to contribute to your academic goals.

I want to work on a project for only part of the summer. Can I start my project later in the summer or leave early? No. Since SPUDS Fellows are committed to being active members of the SPUDS and larger Research Village community, every SPUDS Fellows is required to participate in the full ten weeks of the program.

SPUDS is a Harvard Summer Undergraduate Research Village Program (HSURV). HSURV programs provide a research experience with Harvard-affiliated faculty mentors, along with a stipend and on-campus housing and a partial meal plan for 10 weeks over the summer. Browse other programs in the Research Village:

• PRISE (Program for Research in Science and Engineering)
• BLISS (Build Learning through Inquiry in the Social Sciences)
• PRIMO (Program for Research in Markets and Organizations)
• SHARP (Summer Humanities and Arts Research Program)
• SURGH (Summer Undergraduate Research in Global Health)
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HSURV Abstract Books

Interested in learning more about projects that past fellows in the Harvard Summer Undergraduate Research Village (HSURV) have worked on? Check out our achive HSURV Abstract Books!

• 2024 HSURV Abstract Book  (AMGEN, BLISS, CSB, DUBSP, FUEL, HIP, KRANIUM, MCB SCION, PRIMO, PRISE, SEAS REU, SHARP, SPUDS, SURF, SURGH)
• 2023 HSURV Abstract Book (PRISE, BLISS, PRIMO, SHARP, SURGH, SPUDS)
• 2022 HSURV Abstract Book (PRISE, BLISS, PRIMO, SHARP, SURGH, SPUDS)
• 2021 HSURV Abstract Book (PRISE, BLISS, PRIMO, SHARP, SURGH, SPUDS)
• 2020 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP, SURGH)
• 2019 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP, SURGH)
• 2018 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP, SURGH)
• 2017 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP, SURGH, PCER)
• 2016 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP, SURGH)
• 2015 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP, SURGH)
• 2014 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP)
• 2013 HSURV Abstract Book  (PRISE, BLISS, PRIMO, SHARP)
• 2012 HSURV Abstract Book  (PRISE, BLISS, PRIMO)
• 2011 PRISE Abstract Book
• 2010 PRISE Abstract Book
• 2009 PRISE Abstract Book
• 2008 PRISE Abstract Book
• 2007 PRISE Abstract Book

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Data and Policy Summer Scholar Program

Academic & professional benefits, student experience, program fee & payment, frequently asked questions.

Data is everywhere and analytical skills are in high demand by public and private institutions around the world. Over seven weeks, you will gain the skills to retrieve, analyze, and present data through a public policy lens. Learn to use a scientific approach to address today's social issues and create a measurable impact on society.

• Asynchronous lectures: Ideal for working professionals and students around the world, watch structured mini lectures and learn at your own pace.
• Live teaching sessions and support: Join live virtual teaching sessions with faculty and teaching assistants and ask questions in discussion boards.
• Applied learning: Engage in research or data visualization projects alongside UChicago faculty, and publish your GitHub repository and enhance your online presence to showcase your work to the world. 
• Specialized track design: Focus on building your academic research skills or applied data analytics and storytelling skills with numerous professional development resources and networking opportunities.
• Beginner friendly: Learners across all backgrounds and levels are welcome.

Assistant Professor at the Harris School of Public Policy

Read full bio

"> "> “The DPSS program was a critical first step. Being out of an academic environment for almost a decade, I wanted to prepare for this rigorous curriculum. While DPSS was challenging, I had an extremely supportive community of classmates, TAs, and professors. DPSS was the tipping point that made Harris my number one choice.”

Charles Huang

DPSS’22, MPP Class of 2025

"> "> “It’s a summer I won’t forget. The quantity and quality of learning kept me engaged, and the other scholars were such a joy to work with. There were people from all walks of life, which was very reflective of the real world. I was among the youngest, so I was mindful of that as I went through it. But after each session, I felt more and more confident and encouraged to engage with my classmates, faculty, and staff.”

Shreeya Misra

"> "> “In the short-term, I wanted to apply the quantitative skills I gained from DPSS to better analyze cyber conflict in the context of the Department of Defense Cyber Strategy of Defending Forward. Long-term, I hoped my exposure to policy practitioners and UChicago faculty would prepare me for graduate study.”

Jose Macias

DPSS’21, MPP Class of 2024

DPSS equips you with training in data analytics paired with hands-on policy research experience and professional development resources. The program includes three required modules: (1) Data Analytics in Public Policy; (2) Introduction to R Programming; and (3) an immersive Capstone Research Project.    

Depending on your primary learning goal, participants can opt for one of the two tracks - the academic track and the professional track. Both tracks provide core foundational courses accompanied by a diverse array of customized resources, ensuring you are ready for your next steps - whether it be getting ready for top-notch graduate programs or preparing for a career pivot.  

Module 1: Data Analytics in Public Policy

This course provides an introduction to the statistical foundations, tools, and methods employed by public policy researchers. Explore the fundamental problem of causal inference and learn how to use data, research design, and statistical modeling to navigate around this problem.

Module 2: Introduction to Programming in R

This is an introductory course in programming and data analysis for students with no prior coding experience. It begins with foundational basics and progresses to advanced, practical data analytical and visualization skills in R. By the end of the course, you will be able to use R to retrieve, clean and analyze complicated datasets and produce tables, charts and other data visualization tools to convey your findings.

Read the Blog Post "How Data Can be Used to Tell Stories About Policy" by Victor Hinardi, DPSS'20, MPP Class of 2023

Read the Blog Post “What can I do with R” by Anna Weiss, DPSS’21, MPP Class of 2024

Module 3: Capstone Project

In the capstone research project, you will collaborate with faculty and a group of peers to tackle a real-world issue with datasets, devising solutions and creating deliverables that can enhance your portfolio online.    

Academic track participants will harness the skills of research design, policy analysis, and team collaboration to conduct a research project using open-source or faculty-provided datasets. Participants will individually write a research note, showcasing their academic readiness for graduate programs or research jobs.    

Professional track participants will dive into real-world datasets with practical analysis and a focus on data visualization. They will produce a policy memo with data visualization (charts, infographics) and get the chance to explore Github to showcase their results online.

Read Professor Wright's blog post about the capstone project.

Academic Track Requirement and Resources

The academic track provides a comprehensive toolkit for participants who are interested in further advancing their academic growth by earning a master or PhD-level degree in the social sciences field. Students undertaking this track are required to complete advanced statistics and programming learning such as panel data designs, regression discontinuity, instrument variables etc. of the Data Analytics and Programming in R courses, fostering a comprehensive understanding of advanced content. In addition, as part of the Module 3 Capstone Project, participants will individually write a research note that can be used as a writing sample for one's graduate degree application. Participants will also join live workshops focusing on understanding the academic world and learn tools such as LaTeX to support their future research journey. 

Professional Track Requirement and Resources

The professional track prioritizes applied data analytical skills and techniques of data visualization and storytelling, allowing participants to maximize the application of data analysis in real-world professional settings. In this track, participants only need to complete a portion of the assignments of the Data Analytics and Programming in R courses (Module 1 & 2), providing greater flexibility in their time management. Participants will use the Capstone Project as an immersive learning experience to conduct data storytelling and data visualization practice , and produce a policy memo as a deliverable. In addition, participants will join live workshops focusing on understanding how data skills can be applied in various types of industry jobs, and explore useful resources such as GitHub and QGIS to build the online presence to signal their skillset to future employers.

Academic and Professional Resources for All

• Orientation: You will meet your DPSS teaching and administrative team, learn the academic schedule and expectations, and participate in an activity to meet your peers. 
• Research Essentials Series: Students are encouraged to participate in the Research Essentials Series, led by Prof. Wright. This series adopts a hands-on, practical approach to navigating the components of policy research. Across these sessions, students will cultivate a nuanced understanding of policy research methodologies, honing critical analysis skills for effective review of research papers. The sessions empower participants to acquire proficiency in scrutinizing and analyzing data, which fosters a comprehensive grasp of policy research and enhances their analytical capabilities.  
• Career Workshops: The backgrounds and experience s  of the DPSS cohort  are diverse. We range from undergraduate students to PhD candidates and working professionals. The career workshops are your space to l earn from experienced career coaches on personal branding, resume building, networking tips and more.
• Writing Workshops : The courses in DPSS teach you how to analyze data and apply the evidence into policy. The writing workshops will dive into the methods to convey rich information in a persuasive and informative way. 
• Community Chats : UChicago Community Chats are small-group networking opportunities to connect with members of the UChicago community including but not limited to current graduate students, degree and DPSS alums, Harris staff and more.
• Graduate Admission Information Sessions : Ask questions of UChicago Harris Admissions. Learn more about our graduate degree programs, application tips and general advice on the graduate school process.

Sample Schedule

2024 DPSS Sample Syllabus

DPSS alumni have achieved remarkable success in their pursuit of graduate programs and career development. They continue their academic pursuit in Master's and Ph.D. programs across various fields, including Data Science, Business, Economics, Law, Mathematics, Political Science, Public Policy, and Social Science. Discover how DPSS can empower you on your path to graduate study, including exciting opportunities right here at UChicago, by exploring our blog post, "DPSS Benefits for Graduate School.”

started or completed a graduate program after DPSS

enrolled in a graduate program at UChicago

enrolled in a graduate program at UChicago Harris School of Public Policy

DPSS alumni have made significant strides in their professional journeys, securing internships, part-time and full-time employment. They've attained diverse positions, including roles as Business Analytics Interns, Data Analyst Assistants, Congressional Interns, Development Coordinators, Financial Analysts, Management Consultants, Software Engineers, and Research Assistants.

Here are some example organizations: 

Faculty and Teaching Team

Austin wright.

Austin Wright is the Assistant Professor at the Harris School of Public Policy and Faculty Director for the Data and Policy Summer Scholar Program, ensuring the holistic curriculum is designed and taught to meet student needs in the UChicago way. 

Faculty Profile

"The goal with our credential programs is to find ways to enable a broader set of learners to have access to the UChicago tools and resources and help them achieve their ultimate goals of improving the world around us."

Jeff Levy is an  Assistant Instructional Professor  at the University of Chicago Harris School of Public Policy.  His background is in applied policy research, working at the intersection of social science and data science. 

At Harris, Levy teaches both microeconomics, and the core sequence on programming and data skills.  His own research has focused on economic uncertainty, particularly around the Great Recession.

Teaching Team

Alex and Jose form part of the teaching team that will help the faculty and students with office hours, assignments, and overall instructional material.

Teaching Team Profile

Jose M. Macias, a current student at Harris, serves as a research associate at the Center for Strategic and International Studies, specializing in quantitative analysis of war and machine learning integration in International Relations. He has previously interned at the U.S. Department of Defense, contributed to The Correlates of War Project, and held fellowships, showcasing a diverse background in cybersecurity, policymaking, and academic achievement.

Alex Sobczynski

Alex Sobczynski, a Chicago native and recent graduate from Harris, is a short term consultant at the World Bank and a research assistant in Harris and at UChicago's Development Innovation Lab. Her research is at the intersection of agriculture, conflict, and climate and focuses on household decision making and social networks. With a passion of quantitative methods, her work utilizes satellite spectroscopy and cell phone location data to directly measure outcomes in regions with low state capacity, particularly Afghanistan. Additionally, she has done analysis for the UNHCR and works as a tutor and college consultant.

Program Dates and Time Commitment

June 10 - July 26, 2024

Participants expect to spend about 10-15 hours per week including watching lecture recordings, working on assignments and projects, joining interactive office hours and community activities.

Virtual Format with Maximum Flexibility

Asynchronous (pre-recorded) lectures from faculty, optional synchronous teaching sessions with faculty and graduate teaching assistants, and interactive community activities.

Office Hours

These synchronous office hours, conducted in Zoom, are offered approximately 15 hours per week at various times to accommodate our global and working students.

Documenting Your DPSS Achievement

Participants will receive two documents, issued electronically, upon successful completion of the credential program:

Official UChicago Transcript

With pass/fail marks for each academic course (non-credit): Data Analytics, R Programming, and Capstone research project.

Students will be able to request transcripts in early October from the University Registrar at an additional fee: https://registrar.uchicago.edu/records/transcripts/

Certification of Program Completion

Issued by the Harris School of Public Policy, contains a grade (of points earned) for each academic course.

Students will receive an email from our office ([email protected]) with the electronic certification within one month after the program.

In-Person Chicago Weekend (Optional)

Interested in meeting your cohort in person? DPSS offers an optional Chicago Weekend where students gather for three days of workshops, networking, and social activities.

The program fee is $4,500. We are pleased to offer merit-based partial scholarships to outstanding applicants.

Once being admitted, participants will secure the seat by submitting a $1,000 USD enrollment deposit, which is non-refundable and applies toward your total program fee. The remaining program fee balance will be paid before the program starts. Detailed payment instructions will be provided to admitted participants via email.

"Not only was Harris the best quantitative program that I researched, but after experiencing DPSS, it was the best place to resolve the coding gap I had in undergrad. I really appreciated that coding is built into the curriculum.” 

Olivia Gunther

DPSS’21 , MPP’23

"> "> "Transitioning from undergrad to master’s level was challenging when I made the jump a few years ago, and I didn’t want the same thing to happen at Harris. After completing DPSS, I felt much better prepared than when I’d enrolled at Penn State." 

Wrojensky André

DPSS'20, AM Class of 2021

Eligibility

• Minimum age of 18.
• One-year, full-time undergraduate study completed at least one month prior to the start of the program.
• No prerequisites needed! DPSS is open to all academic backgrounds.
• How to Apply
• Select “2024” - “2024 Cred” - “Non-Degree” - “Data and Policy Summer Scholar Program”.
• Motivation statement (300-word limit) describing why you want to join the program and how it will benefit your future development.
• Resume (1-2 pages recommended).
• Must include your full name and institution/university name. Transcripts must include the course names, dates (term and year), grades and grading scales for each year of post-secondary education (college or university).
• Provide a transcript for each degree conferred (undergraduate and graduate).
• Unofficial transcripts are accepted. Please upload a PDF copy.
• English translation must be submitted for all foreign language transcripts.
• English language proficiency proof for some international applicants. See detailed requirements.

Application Deadline

Refund and Cancellation Policy

If the participant decides to cancel their Program enrollment, the participant must submit a request in writing at least 30 days prior to the program start date to [email protected] to receive the paid amount less the non-refundable enrollment deposit. Requests received 14 to 30 days before the Program start date are subject to a payment of 50% of the Program fee. Requests received within 14 days of the Program start date are subject to full payment of the Program fee. If the participant is unable to join the Program due to circumstances that the participant has no control or influence over, the refund amount will be considered on an individual case-by-case basis. 

Explore the DPSS FAQ section for quick answers to common program inquiries. Find valuable information about admissions, scholarships, program fees, and more, ensuring a smooth and informed DPSS experience.

Is it possible to complete the DPSS program while juggling a full-time job, internship, or academic courses?

Absolutely! DPSS is designed to accommodate individuals from diverse academic and professional backgrounds, including working professionals, recent graduates, and current students. While it may not be without its challenges in terms of time management, it is certainly manageable. You'll benefit from a dedicated support staff, an exceptional teaching team, and the enriching experience of collaborating with a diverse cohort throughout your journey.

How challenging is the DPSS content, and should I be concerned if I have no prior experience?

DPSS content is rigorous, but we accommodate participants of all backgrounds. There are no prerequisites for this program. If you're new to data analytics, expect to dedicate approximately 12-15 hours per week. For those who have taken some statistics and/or R programming courses, plan for around 8-12 hours per week. If you already possess a working knowledge of statistical concepts and the R programming language, you can typically commit 6-8 hours per week. Our aim is to provide support and guidance to help you succeed, regardless of your prior experience. 

We also highly encourage our DPSS participants to take the advantage of the Policy Analytics Credential Program before DPSS starts to ensure you have a solid quantitative foundation and maximize your DPSS experience.

Are scholarships offered for DPSS?

While full funding is not available for the DPSS program, merit-based scholarships are awarded to eligible applicants. Applying early can significantly benefit your chances of receiving financial support. 

There is no additional application document required to request being considered for scholarship.

For more financial tips, please read the blog " Financial Planning for Credential Programs ".

What professional development resources are available in DPSS?

DPSS provides a comprehensive array of community and co-curricular resources designed to enhance your professional development. Our offerings include weekly virtual events such as the "Policy in Action Speaker Series," "UChicago Community Chats" for networking, "Writing in Policy Workshops," and "Career Exploration Workshops." You can also engage in social activities and consider participating in the optional "Chicago Weekend" on campus. These opportunities are designed to strengthen your professional connections and enrich your DPSS experience. Admitted students will receive additional information about the Chicago Weekend.

What is the Capstone Research Project in the DPSS program, and what does it involve?

The Capstone Research Project is a pivotal aspect of the DPSS program, offering students the opportunity to engage in hands-on policy research. Under the guidance of DPSS faculty, including Austin Wright, Assistant Professor and DPSS Faculty Director, students collaborate in small groups and access proprietary or open-source data to address pressing policy challenges. Past projects have explored topics such as refugee displacement, the economic impact of mask mandates, and the legacy of the George Floyd protests. The goal of this project is for students to apply the quantitative analysis and R programming skills they've acquired throughout the program to produce a policy memo. This memo includes a literature review, data analysis, visualization, and a summary of their findings. It's a chance for students to put their newfound skills into practice and make a tangible impact.

Read Professor Wright's blog post about the capstone research project.

How can I apply the outcomes of the capstone project in academic or professional contexts after completing DPSS?

After successfully completing the Capstone Research Project, students often submit their projects to their current academic institutions or incorporate them into their graduate school applications. Some continue to build on their specific projects, while others apply the acquired research skills to areas they are most passionate about. These research skills are highly transferable and can be applied to various other projects and career pursuits. Many DPSS alumni have found that the Capstone project has strengthened their graduate school applications and opened up opportunities for internships and career advancement in their respective fields.

Can I obtain an official UChicago Transcript for the coursework I completed during my time in DPSS?

You can request an official UChicago Transcript that includes pass/fail marks (non-credit) for each academic component: Data Analytics, R Programming, and the Capstone research project. Access to this transcript will be available in early autumn, around early October. You'll also have access to request a transcript from the University Registrar.

There will be an additional transcript fee that you are responsible to pay. Please take a look at the notes at this webpage: https://registrar.uchicago.edu/records/transcripts/ 

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Data Science for Social Good Summer Program

Main navigation.

The Data Science for Social Good summer program trains aspiring researchers to work on data science projects with social impact.  Working closely with governments and nonprofits, participants take on real-world problems in education, health, energy, public safety, transportation, economic development, international development and more. Participants include a diverse and inclusive cohort of students who spend eight weeks of the summer working with Stanford researchers and technical mentors, learning insights from data that benefit society.

• Click  here  to learn more about becoming a student fellow
• Click  here  to learn more about becoming a technical mentor [Stanford only]
• Click here to learn how to have your data sets be part of the program

Upcoming Events

Related news, the spark for research reproducibility & leading data science for social good.

June 25, 2024

• Stanford Data Science Scholars Program
• Open and Reproducible Science
• Data Science for Social Good

2022 Data Science for Social Good - Call for Application

Stanford Data Science is now calling for applications to our 2022 Data Science for Social Good summer research program. Apply by Feb 14, 2022!

December 15, 2021

• Announcements

2021 Data Science for Social Good - Call for Application

Stanford Data Science is now calling for applications to our 2021 Data Science for Social Good summer research program. Apply by Feb 15!

January 26, 2021

2020 Data Science for Social Good wraps up

The 2020 Data Science for Social Good summer program wrapped up this week, completing 8 weeks of intensive work on three projects.

August 21, 2020

• In the News

2020 Data Science for Social Good

Stanford Data Science is thrilled to invite applications for our second year of summer fellows for the Data Science for Social Good program! Applications are due on Sunday, March 2

March 04, 2020

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We are delighted to welcome the new co-chairs to the CODATA-RDA Schools of Research Data Science initiative! Meet our new members and learn more about their contributions here .

CODATA-RDA Schools of Research Data Science

Building core data science skills and introducing open tools and resources for researchers.

Behind the Scenes – read a blog on what it takes to run a School of Research Data Science.

Who are we?

Group of dedicated Open Science and Open Data enthusiasts.

Grow a network of host institutions, instructors, assistants and helpers.

Our Philosophy

Increasing research capacity, as it relates to data in Low and Middle Income Countries.

What areas do we cover on the curriculum?

• Principles and practices of Open Science
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• An introduction to research data management and the research data lifecycle
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• How to find data more efficiently
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• Introduction to theory and practice of visualization
• Introduction to machine learning
• Grounding in use of computational research infrastructures

Happy students!

Previous school's testimonies.

As a second-year Ph.D. student, I am ever grateful to the organisers and teachers (or volunteers) for the manner in which we were guided through the courses. The knowledge gained in data analysis and machine learning in addition to exposure to various online platforms including virtual machines will ease my Ph.D. research work.

Amadi Brians, Brazil (Student)

My heartfelt appreciation to the organizers and facilitators of CODATA-RDA Summer School 2021. It was a great experience for me learning some Data Science skills for the first time. Thank you all for the enlightenment.

Justina Achuka, Nigeria (student)

Honestly, the CODATA-RDA Research Data Science Summer School was absolutely amazing and a perfect one for all competency levels. The school provided me a solid foundation and improved my confidence in research data analysis and data curation using various software.

Lanre Anthony Gbadegesin, China (Student)

The CODATA-RDA summer school is a great opportunity to learn about data management and meet people who are working in exciting research. I’ve had the honor to participate in the school several years and it is great to see that both in person and virtually, the impact of the school is very positive.

José López, France (Helper)

The Winter cohort application deadline is November 24, 2024.  

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11 Data Science Summer Programs for High School Students

A summer program might be valuable to your application if you are an ambitious student looking to break into a career in data science.

In this post, we have curated a list of 10 data science programs. Programs on this list, such as CodeConnects or the Lumiere Research Scholar Program , are great research opportunities that will signal your passion for the subject and how you are actively exploring data science outside of classroom walls.

Other summer academies or camps, such as Stanford’s Introduction to Data Science or Wharton’s Data Science Academy , can give you exposure to the subject and allow you to explore potential career paths or research opportunities which you can pursue in the future!

Participating in a data science program or internship can allow you to network with researchers who are at the top of their field. You can later use these connections either for job opportunities or even letters of recommendation !

Programs are a great way to pick up skills and build networks, but becoming a data scientist will involve a lot more than that. Check out this great blog on ways to become a data scientist! Once you’ve built up some momentum, consider data science internships to take your skills to the next level.

However, while all the programs on this list provide great exposure and mentorship opportunities in data science, they can be expensive. If you are looking for more budget-friendly courses, you can take a look at Google’s ‘Data Science Foundations’ , Udemy’s ‘Essentials of Data Science’ , or Data Camp’s ‘Data Science for Everyone’ !

1. Veritas AI programs

Veritas AI has a range of AI programs for high school students, starting from close-group, collaborative learning to customized project pathways with 1:1 mentorship.  The programs have been designed and run by Harvard graduate students & alumni.

In the AI Fellowship program, students work with PhDs from top institutions to work on their own research projects . These research projects can be across fields that intersect with data science or artificial intelligence. Students in the past have worked on projects in AI & finance, AI & medicine, creating software for educational resources, applications of computer science in gaming, and more.   Examples of more interesting projects students have worked on can be found here .

Cost : $1,490 for the AI Scholars program (The 10-week boot camp), $4,200 for the AI Fellowship (12–15 weeks, 1-1 mentorship). Need-based financial aid is available.

Location : Virtual

Application Deadline : The program runs in cohorts, applications  can be found here!

Program Dates : Rolling - Cohorts during spring, summer, fall, and winter

Program Selectivity : Open Enrollment for the AI Scholars Program, Moderately selective for the AI Fellowship

Eligibility : High school students.

2. Horizon Academic Research Program (HARP)

Horizon offers trimester-long research programs for high school students across subject areas such as data science, machine learning, political theory, biology, chemistry, neuroscience, psychology, and more! It is one of the very few research programs for high school students that offers a choice between quantitative and qualitative research!  

Once you select a particular subject track and type of research you’ll be paired with a professor or Ph.D. scholar (from a top university) who will mentor you throughout your research journey.  You’ll work to create a 20-page, university-level research paper that you can send to prestigious journals for publication as a high school student. 

This program is a solid opportunity for you to pursue a research program in highly specialized fields, under the guidance of a top scholar.  The program also provides a letter of recommendation for each student, as well as detailed project feedback that you can use to work on future projects and on college applications. Apply here !

Location : Virtual 

Application Date: May 21, 2024 for the summer cohort, and September 25, 2024 for the fall cohort 

Program Dates: 

Summer seminar - June 24, 2024 - September 2, 2024

Fall seminar - October 23, 2024 - February 19, 2025

Lab dates are flexible, but you must apply 4 weeks in advance.

Eligibility: High school students with good academic standing (>3.67/4.0 GPA) can apply. Most accepted students are 10th/11th graders! Only a couple of tracks require formal prerequisites, more details of which can be found here .

3. Stanford University’s Introduction to Data Science

As a part of its Pre-Collegiate Summer Institutes , Stanford conducts a 2-week course on data science . In the course, you are introduced to computer algorithms and the diversity of the models they can generate , each with its own pros and cons.

You will also gain the opportunity to use datasets from the natural and social sciences to answer real-world questions, pursuing questions and data relevant to your lives. Additionally, you will apply machine learning concepts through R programming exercises that are integrated into the course!

At the end of the program , you can expect to have developed a technical skill set that allows you to approach any dataset with strong coding abilities and a scientific approach.

Cost: $3,050

Location: Virtual

Application Deadline (Summer 2023): March 15th, 2023

Program Dates (Summer 2023):

Session 1: June 19th to June 30th, 2023

Session 2: July 10th to July 21st, 2023

Selection Rate : Highly Selection

Eligibility:

Must be either a freshman, sophomore, or junior at the time of application.

Must have completed AP Statistics or one computer science course.

4. Wharton Global Youth’s Data Science Academy

Wharton’s Data Science Academy is a summer school that allows high school students to be taught by full-time professors at Wharton while also having the opportunity to work on their own projects!

You will explore essential data science techniques such as data visualization and wrangling. However, you will also be exposed to modern machine learning methodologies , all of which are essential for today’s AI field. You can apply these concepts in your final projects, which you will present to peers at the end of the program !

An added bonus is interacting with Wharton undergraduate and graduate TAs who will share their experiences with studying data science in university!

Cost: $9,099 ( Scholarships available)

Location: Wharton Campus, Philadelphia

Application Deadline (Summer 2023):

Priority Deadline: January 25th, 2023

Final Deadline: April 5th, 2023

Program Dates (Summer 2023): July 16th - August 5th, 2023

Selection Rate : Selective

Must be in grades 10-11.

Must have a strong background in math, coding, and data analytics (Note. Previous understanding of statistics is preferred)

Must have a minimum of 3.3 unweighted GPA

5. The University of Chicago’s Data Science Institute Summer Lab

The DSI Summer Lab by the University of Chicago is a 10-week, paid summer research opportunity for high school and undergraduate students that focuses on applied, interdisciplinary data science research.

In this program, you will be paired with a data science mentor in various domains, including - data science, computer science, climate and energy policy, and more ! Your research mentor will engage and hone your skills in research methodologies, practices, and teamwork . You will develop an understanding of fundamental data science methodologies and be given specialized training within the application areas.

The program culminates with a presentation of your final videos - a video created by you that outlines your research findings throughout the summer. These videos will be presented in a professional conference format where you can interact with your peers and field questions on their projects and vice versa!

Cost: No cost; stipend provided ($6,000)

Location: University of Chicago Campus, IL

Application Deadline (Summer 2024): While the application deadline has not been specified, you can check their website in the fall-winter 2023 for updates.

Program Dates (Summer 2023): June 12th - August 18th, 2023

Open to all high school students.

Familiarity with at least one programming language and relevant coursework (e.g., computer science, statistics, and math) is preferred.

Note: International students are welcome to apply but are responsible for gaining authorization to work in the U.S. and providing all the necessary documentation to earn their stipend.

6. Code Connects Summer Research Program (with Columbia University)

Code Connects Summer Research Program at Columbia is a 5-week research program where students develop their skill sets in data science and apply them as research assistants to Ph.D. researchers !

The program is split into two components. The first two weeks are spent on courses where you receive intensive training in data science. You will learn skills such as coding skills in R, data visualizations, data cleaning, data analysis, and more! The last weeks are spent putting these concepts into action by assisting PhD researchers with their real-life research projects.

An added bonus is the possibility of publication in academic journals, depending on your findings!

Cost: $3,995

Location: Virtual (with live instruction)

Application Deadline: Rolling admissions

Program Dates (Summer 2023): July 5th - August 4th, 2023

Eligibility: Open to rising sophomores and recent graduates.

7. University of Washington’s A14ALL

Hosted by the Taskar Center for Accessible Technology at the University of Washington, A14ALL is a 2-week introductory course on data science and artificial intelligence. You will learn how to understand, analyze, interpret, and discuss real-world applications of data science and machine learning to understand impactful technology decisions.

A key focus of this program is to enable dialogue and learning in small-group settings . The Center focuses on anti-bias in AI teachings on non-ableist AI, bringing a disability studies lens to interrogate bias and fairness in AI.

Note. If you are interested in pursuing a non-STEM major in university, this is still a great option, as a critical focus of the program is the interdisciplinary applications of data science! Plus, it also focuses on the ethical use of technology, which is a significant debate across STEM and non-STEM areas.

Cost: No cost

Application Deadline: April 15th, 2023 (tentatively, based on the previous year’s schedule)

Program Dates (Summer 2023): August 08, 2023 - August 28, 2023 (tentatively, based on the last year’s schedule)

Students must be rising juniors, seniors, or recently graduated.

Must be from an underrepresented background

8. Syracuse University’s Data Visualization and Analysis Course

This course by Syracuse University is a hands-on course centered around taking raw data and converting it into visuals. You will learn how data is captured, how to assess the quality of data, and how to clean data.

Additionally, you will use tools such as Microsoft Excel and Tableau, where you will have discussions on design principles on dashboards and the science behind choosing charts.

The course objectives for the Summer 2023 sessions are:

Understanding what to do first when an unknown data set is presented.

Visually explore data.

Perform data cleanup and get it ready for analysis.

Perform basic statistics to generate insights from data.

Present the data to stakeholders to make decisions quickly and in an informed manner.

Apart from the lectures, you will spend your afternoons participating in group activities and labs!

Cost: $2,020 (Residential), $1,543 (Commuter)

Location: Syracuse University Campus, NY

Application Deadline: May 1st, 2023

Note. There is an application extension until May 15th, 2023. Also, the financial aid and scholarship deadline is March 1st, 2023!

Program Dates (Summer 2023): July 16th to July 21st, 2023

Students must be rising sophomores, juniors, seniors, or recent graduates.

9. Columbia’s Pre-College Program - Data Science Track

Columbia University conducts a summer immersion program in data science and machine learning , called “Big Data, Machine Learning, and their Real World Applications”.

In this program, you will be taught by esteemed faculty to e xplore the advancement of data, cloud computing, and machine learning , and how they have had impacts on various industries.

You will focus on the strategic use of data and innovative technologies used to derive business insights . You are also introduced to different technologies used for data analytics and gain familiarity with programming technology.

At the end of the program, you will:

Develop a strong foundation in data-driven thinking for solving real-world problems.

Become experienced with importing, exporting, manipulating, transforming, and visualizing data.

Learn how to use statistical summaries and machine learning models.

Implement common machine-learning techniques and develop and evaluate analytical solutions.

Cost: $12,449 (3-week residential), $6,300 (3-week commuter), $2,825 (1-week commuter)

Note. The fees have not been finalized for Summer 2023. The fees listed above are from the previous term. You can look here for updates!

Location: Columbia University Campus, NY

Application Deadline: Rolling basis

Program Dates (Summer 2023): There are multiple sessions for both residential and commuters:

Residential

3 Weeks - Session 1: June 26 - July 14, 2023

3 Weeks - Session 2: July 18 - August 4, 2023

6 Weeks - Sessions 1 & 2: June 26 - August 4, 2023

1 Week - Session 3: August 7 - August 11, 2023

4 Weeks - Sessions 1 & 3: June 26 - July 14 and August 7 - August 11, 2023

4 Weeks - Sessions 2 & 3: July 18 - August 4 and August 7 - August 11, 2023

7 Weeks - Sessions 1, 2, & 3: June 26 - August 11, 2023

Open to all students between grades 8-12.

Note. Residential programs are only available to students who are 16 or older. Younger students must choose the commuter or online option to be eligible.

10. Quinnipiac University’s Data Science Summer Camp

Quinnipiac University conducts a 2-week program where you will be introduced to the field of data science. You will get the opportunity to learn foundational data science theory from experienced Quinnipiac faculty and apply these concepts by working on hands-on data science projects.

In the program, you will:

Develop a foundational knowledge of Big Data and data science theory and methodology.

Analyze data sets to uncover patterns and solve problems.

Gain exposure to career possibilities within the data science field.

Understand how data scientists use their skills and knowledge to solve problems across diverse industries.

Cost: $3,360 (Residential), $2,400 (Commuter)

Location: Quinnipiac University Campus, CT

Application Deadline (Summer 2023): May 1st, 2023

Program Dates (Summer 2023): July 10th to July 21st, 2023

Open to all high school students (ideally between 15-18 years of age).

11. NextGen DATA Leadership Camp

NextGen DATA Leadership Camp fosters interest in information science and data science within local communities and aims to develop the next generation of leaders within these fields through their interactive camps.

This 5-day camp will allow you to explore data analytics and technological advancement under UNT faculty and PhD. students who currently conduct research in the fields of Information and Data Science. Additionally, the program also focuses on key principles such as leadership, teamwork, and social inclusion.

Some prospective topics discussed in the camp are:

Basic concepts of data science

Interaction with AI/Chatbots

Human-technology interaction

And Fake news.

Location: Frisco Landing, TX

Program Dates: July 24th to July 28th, 2023

Open to rising freshmen to high school seniors.

Bonus program + internship:

Ladder internships.

Ladder Internship is a selective program for high school students to work with startups.

Ladder Startups work in fields including technology, data science, machine learning and A.I., finance, environmental science and sustainability, healthcare and medicine, business and marketing, media and journalism, and more. You can explore all the options here on their application form .

As part of their internship, each student will work on a real-world project and present their work at the end of their internship. In addition to working closely with their manager from the startup, each intern will also work with a Ladder Coach throughout their internship - the Ladder Coach serves as a second mentor and a sounding board, guiding students through the internship and helping them navigate the startup environment.

Note that as a Ladder Intern, you will engage in a coaching+work-based model - a component that sets it apart from most internships in this list. The virtual internship is usually 8 weeks long.

Cost : $1490 (Financial Aid Available)

Location: Remote! You can work from anywhere in the world.

Application Deadline: April 16 and May 14

Program Dates: 8 weeks, June to August

Selection Rate: Selective

Eligibility: Students who can work 10-20 hours/week for 8-12 weeks. Open to high school students, undergraduates, and gap year students!

One other option - Lumiere Research Scholar Program

If you’re interested in pursuing independent research in data science, you could also consider applying to one of the   Lumiere Research Scholar Programs , selective online high school programs for students founded with researchers at Harvard and Oxford. Last year, we had over 4000 students apply for 500 spots in the program! You can find the application form   here .

Also check out the   Lumiere Research Inclusion Foundation , a non-profit research program for talented, low-income students. Last year, we had 150 students on full need-based financial aid!

Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a Ph.D. student at Harvard Business School. Lumiere is a selective research program where students work 1-1 with a research mentor to develop an independent research paper.

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8 - 12 July 2024, Athens, Greece 5th ACM Europe Summer School on Data Science

Be part of this established and exciting learning experience!

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• Data Science

ACM Summer School on Data Science 2024

The 5th ACM Europe Summer School in Data Science will take place in Athens in July 8th-12th, 2024 . Young Computer Science researchers from all over the world will have the opportunity to attend summer courses in the topics of Data Science, Data Management, Deep Learning, and Big Data Processing.

The summer school is addressed to early-stage Computer Science researchers and engineers, i.e. early-career faculty, postdocs, PhD candidates, MSc students, and engineers from industry and in some special cases senior undergraduate students. In terms of its format, the school will be mostly a mix of formal lectures in the morning and practical exercises in the afternoon. Keynote lectures by major figures in the field will also be given at key points of the program. All courses will take place in the Royal Olympic Hotel , which is just in front of the famous Temple of Zeus and National Garden, underneath the Acropolis.

Applications are open! Check how to participate

Data Science Summer School 2023

Meet the Speakers & Lecturers

Social Events

Organizers & Committee

School Sponsors

HeKKSaGOn-GPDS

Data Science Summer School 2024

SENDAI, JAPAN ・September 9-20, 2024

Application Deadline ( For Tohoku University Student ): Monday , September 2 , 2024

Only 10 Spots Open!

As a part of HeKKSaGOn program, Graduate Program in Data Science of Tohoku University will host an International Summer School on Data Science in September 9-20, 2024.

About the Event

Data Science has become essential for researchers across all fields, with young scholars standing to benefit greatly from understanding its scope and applications. Recognizing this need, the Graduate School of Information Sciences at Tohoku University is excited to launch its annual International Summer School on Data Science. 

This program offers master's and doctoral students the opportunity to delve into the world of data science, gaining insights into its relevance across various disciplines and the skills needed to navigate the data-rich landscape of modern research. Join us for an enriching experience at this year's Summer School!

The Summer School is a part of  data science summer school conducted by University of Goettingen so far as annual activity of data science group of HeKKSaGOn .

The speakers will give an overview on the multi-layered topics and methods belonging to Data Science. Such as

Data Exploration

Basic Classification & Regression

Deep Learning

Text Mining and Basic NLP

Time Series Forecasting

Model Optimization

Cutting-edge AI

The Lecturer

Our Data Science Summer School is privileged to have lectures delivered by the esteemed members of the Graduate School of Information Sciences at Tohoku University. Our lecturers are seasoned professionals, each with over five years of distinguished success in Data Science education. 

This summer, our program is further enriched by the inclusion of special guest lecturers—senior professors from Tohoku University, who are renowned experts in specific fields within Data Science. Together, they bring a wealth of knowledge and practical expertise to the forefront of our educational offerings.

Special Lecturers

We also proudly presents special lectures from top-class AI and Data Science professors. In Tohoku University, these professors held a high-status of being the pioneer in many cutting edge data science research and development.

Prof. Kengo Kinoshita

Prof. Jun Suzuki

Prof. Takayuki Okatani

Prof. Masayuki Ohzeki

Assoc. Prof. Chiba Naoya

Target group

The Summer School will welcome up to 35 participants representing mainly international partner universities of Tohoku University. 

The prerequisite for an application is a completed Bachelor’s degree programme.

We expect our applicants to have knowledge of the programming language Python as well as basic knowledge in statistics.

Venue Information

Graduate school of information sciences.

980-0845 Miyagi, Sendai, Aoba Ward, Aramaki, Aoba-6-3

Main Event will be held in Tohoku University Aobayama Campus, GSIS Main building. This campus is a very green campus, surrounded by rich botany and exquisite views.

The building is located right outside the subway stop "Aobayama Station". 

Accomodation

For a number of excellent applications we are able to offer free accommodation.

The Organisation

The Summer School is organized by Graduate Program of Data Science, under Graduate School of Information Sciences (GSIS) Tohoku University

Organizational Team

Prof. Mitsuyuki Nakao (GSIS, Unprecedented Data Analytics Center )

Prof. Kazunori Yamada (GSIS, Unprecedented Data Analytics Center)

Assist. Prof. Samy Baladram

Assist. Prof. Michael Zielewski

This Summer School is supported financially by GP-DS, Tohoku University within the HeKKSaGOn network .

Past Summer School

The Summer School is a part of  data science summer school conducted by University of Goettingen so far as annual activity of data science group of HeKKSaGOn.

We are excited to hold the event this year at Tohoku University, Sendai, Japan.

PAST SUMMER SCHOOL HOMEPAGES

Summer Schools

Data Science Summer School 2017

Data Science Summer School 2018

Data Science Summer School 2019

Data Science Summer School 2020

Data Science Summer School 2023

Graduate Program in Data Science Graduate School of Information Sciences, Tohoku University Phone: +81-22-795-4691 Email: gpds_office[at] grp.tohoku.ac.jp

Microsoft Research Blog

The summer of data science.

Published October 8, 2015

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While we live and breathe data science year-round at Microsoft Research, this summer, we offered a broad range of data science education opportunities for young researchers. Participation in these events was extremely rewarding—for both the students and the organizers.

Students and advisors at the National Water Center for the Summer Institute

GigaPath: Whole-Slide Foundation Model for Digital Pathology

Digital pathology helps decode tumor microenvironments for precision immunotherapy. In joint work with Providence and UW, we’re sharing Prov-GigaPath, the first whole-slide pathology foundation model, for advancing clinical research.

DS3 announcement leads the way

The educational blitz really began in the spring, when we announced openings for the upcoming Data Science Summer School (DS3) (opens in new tab) , an eight-week course (June 15 to August 7) taking place at the Microsoft Research New York City Laboratory. Limited to just eight top-level undergrads, DS3 provided hands-on training and a deep level of understanding of data science. The students not only learned how to acquire, clean, and utilize the “messy” real-world data that is the raw material of today’s research, they were also introduced to problems in applied statistics and machine learning.

National Flood Interoperability Experiment Summer Institute 2015

While DS3 may have been the first program announced, the first student summer event to actually get under way was a Summer Institute (opens in new tab) in association with the National Flood Interoperability Experiment (opens in new tab) on June 1, in Tuscaloosa, Alabama. Fifty students from around the world spent seven weeks learning about and analyzing US hydrology data collated together for the first time ever. In the run-up to the event, my Microsoft colleagues and I helped with the data architecture and cyberinfrastructure on Microsoft Azure. During the event, we trained and continued to mentor the students on how to leverage the cloud and Azure ML for their research projects.

The “flood institute” culminated at the 3 rd CUASHI Conference on Hydroinformatics (opens in new tab) at the University of Alabama, where students presented their group projects during talks and poster sessions. Several participants went on to submit papers and show their outcomes at other events, including the NSF Data Science Workshop (opens in new tab) , hosted by the University of Washington and New York University from August 5 to 7. This Seattle-based event invited students to submit a white paper on data science research. The students who submitted the top 100 papers were invited to participate in the event and present posters on their research. Several Microsoft employees—including two from Microsoft Research—participated in the event as panelists, speakers, and mentors. What struck me the most was that all of the posters at the NSF Data Science Workshop relied on multidisciplinary collaborations to drive research projects.

Heidelberg Laureate Forum

The last data science outreach happened at the Heidelberg Laureate Forum (opens in new tab) , where 200 young researchers came to the Heidelberg Institute of Technology and Science to interact directly with Abel Prize, Fields Medal, Nevanlinna , and Turing Award laureates. It was a once-in-a-lifetime opportunity for these students and new faculty to have direct access to the minds that have shaped computing and mathematics for our generation. It was an honor to present the outcomes of the National Flood Interoperability Experiment to the assembly, which included Turing Award winners from Microsoft Research—Butler Lampson, Leslie Lamport, and Tony Hoare—as well as Jennifer Chayes and Christian Borgs from the Microsoft Research New York and New England Laboratories.

A commitment to growing the next generation

Whether they include 8 or 200 young researchers, these events have the potential to shape the future of data science. Interacting with these young researchers and guiding them toward future success is one of the most rewarding aspects of my job. My advice to these young minds was to do something that really matters and don’t leave the science out of data science.

And while the 2015 summer of data science is behind us, we are jumping into autumn with equal vigor. Stay tuned for announcements on the Data Science webpage (opens in new tab) .

— Kristin Tolle (opens in new tab) , Director, Data Science Initiative , Microsoft Research

• Data science at Microsoft (opens in new tab)
• Data Science Summer School (DS3) (opens in new tab)
• National Flood Interoperability Experiment Summer Institute 2015 (opens in new tab)
• Heidelberg Laureate Forum (opens in new tab)
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DSI Celebrates Culmination of 2024 Summer Programs

For eight weeks this summer, 36 students from across the country engaged in intensive research projects, deepening their understanding of data science applications across various disciplines. Ranging from high school to undergraduate upperclassmen, the UChicago Data Science Institute’s summer program students embarked on an intellectual journey filled with personal and professional growth. The students brought a diversity of experiences, academic backgrounds, and research interests to their projects. Majoring in physics, political science, chemical engineering, computer science, robotics, mathematics, and more,  students came together to advance data science research. This brought about unique opportunities to broaden their technical skills, learn about new fields of study, and explore their scholastic and career options in new ways. 

Students participating in the Summer Lab were paired with faculty mentors across the university. The program is designed to provide high school and undergraduate students with hands-on experience in data science research, fostering their skills in computational analysis, data management, and interdisciplinary collaboration. Participants engage in cutting-edge projects that address real-world problems, working closely with their faculty mentors and peers. 21 students took on wide-ranging projects, with examples including neutrino research, programming robots that teach social and emotional learning to children, and identifying climate infrastructure in Chicago. One of the most important aspects of Summer Lab is the cohort structure. Dr. Kyle Chard , Summer Lab program director and Research Associate Professor, said that “the cohort environment is integral to the Summer Lab program — it fosters a collaborative environment where students learn from one another and builds strong camaraderie that supports students through the challenges and successes of research.”

Learning alongside the Summer Lab students, participants in the Data Science for Social Impact (DSSI) summer program focused on natural language processing research projects to benefit external partners in social impact organizations spanning climate, health, policy, human rights, and finance. Partners for this year’s research projects were sourced among our 11th Hour Project partners, including BankTrack , the Center for Good Food Purchasing , and the mBio Project . The DSSI program welcomes students from UChicago and a consortium of diverse higher education partners, including historically Black colleges and universities (HBCUs), minority-serving institutions (MSIs), and Hispanic-serving institutions (HSIs), The consortium is a collaborative effort to broaden participation in the talent pipeline, serve communities of highest need, and introduce students to social impact data science career opportunities. This year, fifteen students from seven colleges and universities across the nation joined the DSSI program. The institutions included North Carolina State University; Howard University; City Colleges of Chicago; California State University; the University of Chicago; the University of Texas at San Antonio; and the University of Illinois Chicago. Over the 8-week program, students engaged with rigorous curriculum and technical training to perform analysis and produce project-specific deliverables. 

Participants in both summer programs engaged in several professional development activities, including a weekly speaker series featuring UChicago faculty, program alumni, and industry representatives; career seminars; and public speaking workshops. Additionally, students enjoyed various social activities, including a tour focused on the history of Bronzeville, a White Sox game, and a volunteer activity with the Chicago Park District. 

While troubleshooting code and problem-solving with analytical techniques, participants found time for communal fun amidst their hard work, forming career-long friendships. “ I believe the students were able to grow their professional network while participating in the summer programs, as well as make significant additions to their resume that could help them in their future professional pursuits,” said Satadisha Saha Bhowmick, Research Lead of the DSSI program.

A few projects from the DSI’s Summer Programs are highlighted below:

Implementing Large Language Models From Farm to Fork

The Center for Good Food Purchasing reviews receipts and invoices to track the kind of foods that public institutions typically purchase. The Center staff categorizes food items and rates them on factors like sustainability, worker treatment, and animal welfare. Institutions can then review these ratings when making future purchasing decisions. Using different methods of natural language processing, students worked to automate this food labeling task. 

Yannick Tanyi (UChicago), Joanna John (North Carolina State University), and Atuwatse Okorodudu (Howard University) took on this task using prompt generation. The team employed different prompting methods to train their large language models in classifying food products, such as zero-shot prompting and few-shot prompting with Retrieval-Augmented Generation (RAG) implementation. They also utilized Llama3 and OpenAI GPT models to compare cost-effectiveness and performance. While students observed that the use of OpenAI GPT provided higher accuracy in grouping food products, RAG implementation greatly increased the performance of the Llama3 model. Engagement in this project helped to inform students’ academic and career goals. “It was really beneficial to be able to understand how to incorporate my Political Science lens through a technology field and how I could work on social impact projects where I could incorporate the best of both worlds,” said Atuwatse Okorodudu.

Extracting Financial Information for Fiscal and Social Accountability

BankTrack is an international tracking, campaigning, and civil society support organization targeting private-sector commercial banks and the activities they finance. BankTrack combines twenty years of critical yet constructive engagement with banks and banking initiatives. Form 8-K is a required report that companies must file with the SEC to announce major events that shareholders should know about, such as incurring debt. Using data from these forms, BankTrack aims to illuminate pertinent information regarding the monetary expenditures of financial institutions and the corresponding ramifications in order to inform the public.

Jonathan Garcia (California State University, Fresno), Diego Sarria (North Carolina State University), Zaina Khalil (University of Illinois Chicago), and Jack Sanderson (UChicago) worked together to distill complex text in approximately 100 8-K forms into informative summaries. The team constructed the training label summaries through heuristically-generated summaries and the labels and text were then used to train Long Encoder Decoder (LED) and LLaMa large language models (LLMs). After training, refining, and adjusting parameters for these models, the group found that both models were able to create summaries that were much more brief than the original text while retaining approximately 90% of the necessary information. The students overcame different hurdles in order to utilize models that efficiently summarize dense financial documents. While discussing challenging aspects of the research project, rising sophomore Zaina Khalil said “I never worked with natural language processing before so it was definitely a learning curve developing models and getting acclimated to Github, but the project allowed me to gain a new skill set and learn how to work with a team to make progress.”

Socially Fair Regionalization

Yassir Atlas (University of Illinois at Chicago) was mentored by Dr. Yue Lin at the UChicago Center for Spatial Data Science. For his Summer Lab project, he studied regionalization algorithms, which are used to group areas into continuous regions for school districting, political districting, habitat delineation, etc. Regionalization algorithms inform major societal decisions, so it is important to ensure they are socially fair. Yassir’s Summer Lab research showed that commonly used regionalization algorithms can favor certain racial subgroups. He also proposed a solution to this problem that minimizes the maximum subgroup cost.

Learn more about Yassir’s project in the below video presentation.

Navigating the Wild: Classifying and Detecting Animals in Low-Quality Camera Trap Image

For her Summer Lab project, Elva Lu (University of California, Berkeley) worked with Dr. Kyle Chard and his PhD student Matt Baughman. Camera traps are used to monitor wildlife and have important conservation uses, but the images they capture are often low-quality and difficult for computer image models to analyze. Elva developed an efficient approach to improving the detection and classification of animals in these low-quality camera trap images. She utilized a combination of existing computer vision models and pre-processing techniques to enhance image quality, achieving a significant increase in detection accuracy. By integrating the CLIP vision-language model for zero-shot classification, Elva and her mentors were able to further refine accuracy without the need for extensive manual labeling. The results, developed and tested with data from Wellington, New Zealand, show promise for aiding conservation efforts by making the analysis of camera trap images more efficient and reliable. Elva said, “DSI Summer Lab gave me the opportunity to learn more about object detection models, develop solutions to address challenges of working with low-quality images, and explore my interest in vision-language models. I also enjoyed presenting my findings to different audiences and collaborating with my mentors.”

Learn more about Elva’s project in the below video presentation.

DSI Summer Lab Students Present Wide-Ranging Research

Can social impact summer experience symposium showcases impactful student research, more on this topic, grocery gap atlas: new data tool visualizes market concentration and food access trends across the u.s., nsf awards $20 million to build ai models that predict scientific discoveries and technological advancements, dsi hosts third annual ai+science summer school, unique data science partnership with city colleges of chicago offers rising professors, and their students, a more inclusive learning experience.

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Meet students who spent their summer pursuing sustainability research

Through programs offered by the Stanford Doerr School of Sustainability, undergraduate students from Stanford and institutions across the U.S. worked on projects that tackled pressing environmental challenges and advanced fundamental knowledge about our planet. Here’s an inside look at their experiences.

This year, more than 70 undergraduate students engaged in summer research to develop new skills and deepen their understanding of Earth, climate, and society. Through five programs part of the Stanford Doerr School of Sustainability , undergraduates explored sustainability-related issues in disciplines ranging from energy and civil engineering to oceans and social sciences.

The five programs include Mentoring Undergraduates in Interdisciplinary Research (MUIR), organized by the Woods Institute for the Environment ; Summer Undergraduate Program on Energy Research (SUPER), organized by the Precourt Institute for Energy ; Sustainability, Engineering and Science - Undergraduate Research (SESUR); Hopkins Internships - Summer Undergraduate Research Funds (HI-SURF); and Sustainability Undergraduate Research in Geoscience and Engineering Program (SURGE).

The SURGE program is funded by the National Science Foundation and welcomes students from other U.S. institutions, especially those from underrepresented backgrounds doing research for the first time. The other programs receive funding from the Vice Provost for Undergraduate Education (VPUE).

Across all the programs, undergraduates contributed directly to research projects under the guidance of Stanford scholars. They also participated in shared group activities such as research seminars and graduate school workshops.

The large cohort allowed participants to learn from each other in addition to a variety of mentors. Building this community of support, in contrast with the sometimes isolating nature of individual research, was one of the main goals of bringing the five programs together last year.

Whether pursuing a scientific interest, trying out new tools, or discerning a potential career path, students used this summer to grow both academically and personally. Many hope to expand on the work they started, while others are moving forward with newfound clarity on their discipline. As they wrapped up their projects, three undergraduates shared insights about their research, personal growth, and how they made the most of the experience.

Evelyn Pung, ’27, SESUR participant

For Evelyn Pung, the motivation to research the link between environmental quality and human health was a personal one.

She grew up 10 minutes away from the ocean in Long Beach, California, but she rarely took trips to the beach. “The pollution at our beaches had gotten so bad, my parents didn’t want me to go, out of health concerns,” she said.

This summer through the SESUR program, Pung got involved in a project in the lab of civil and environmental engineering Professor Nick Ouellette . With her mentor, PhD student Sophie Bodek , she studied the movement of tiny plastic particles in bodies of water. Understanding how these pollutants travel through water in different environments can inform efforts to limit their spread.

Pung said that the freedom to actively control the experiment, combined with supportive mentorship from Bodek, made the research especially fulfilling.

“This whole experience has been a gratifying learning opportunity,” she said.

Read more about Evelyn Pung .

Trent La Sage, ’25, SURGE participant

Trent La Sage, an undergraduate student at the University of Florida, conducted research that brings together physics, Earth science, and materials science.

His project tackled a common problem in materials science: Insights about certain materials are not easily accessible to researchers. While findings about materials at ambient conditions can be uploaded to a public database for other scientists to reference, no such platform exists for materials at extreme conditions.

To address this, La Sage and other scholars worked on a program that uses computer vision and large language models like Chat GPT to pull data from published research papers, which can then be applied to work on future computational models.

The opportunity to collaborate on a large team was a highlight for La Sage, who appreciated the variety of viewpoints. He brought his own distinct perspectives to the group – both in discipline, as the only physics and astrophysics major, and in experience, having started his undergraduate education after several years in the workforce.

“It was very helpful to have people from other backgrounds. And we’ve been able to get a lot of things done that I wouldn’t have been able to get done myself,” he said.

Read more about Trent La Sage .

Juan Martín Cevallos López, ’26, HI-SURF participant

After recurring moments of awe and discovery in his oceans-related classes at Stanford, Juan Martín Cevallos López, who prefers to be referenced by his first and middle name, discovered a passion for ocean science. He knew he wanted to get involved in research at the Stanford Doerr School of Sustainability’s Hopkins Marine Station in Pacific Grove and applied to the HI-SURF program.

Juan Martín contributed to three different projects – studying the impacts of ocean acidification on a particular species of seaweed, the development of bat star larvae in various temperatures, and the role of crustose coralline, a key component of coral reefs, in temperate environments such as Monterey Bay.

Throughout his research, Juan Martín was thrilled to be able to combine his knowledge of oceanography with other scholars’ expertise in marine biology and ecology, and he is eager to continue studying the ocean.

“I’m excited to see where it takes me, because it can literally take you anywhere,” he said.

Read more about Juan Martín .

Learn more about Stanford Doerr School of Sustainability summer undergraduate research programs and how to apply.

Explore More

Lower-carbon concrete floors could pave the way to a health solution

Soil-packed floors common in rural, low-income households in developing countries are breeding grounds for intestinal diseases. Stanford epidemiologists and engineers are developing a lower-emission concrete flooring that could improve families’ well-being with less environmental impact.

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Spotlight: Christine Baker

"I remember daycare trips to coastal parks, and for most of my childhood I fell asleep at night to a sound machine playing the sound of breaking waves. My parents are geologists who really enjoy nature, so we spent a lot of time outdoors. Most families have family portraits hanging on the walls, but we had vials of sand samples clustered along ours."

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Scientists seek to invent a safe, reliable, and cheap battery for electricity grids

Stanford, SLAC, and 13 other research institutions, funded by the U.S. Department of Energy, seek to overcome the major limitations of a battery using water as the primary component of its electrolyte.

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2019 CODATA-RDA School of Research Data Science, ICTP, Trieste

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• 2019 CODATA-RDA School of...

The CODATA-RDA Research Data Science Summer School will take place on 5-16 August 2019.   Apply here!  

Deadline: 18/04/2019.

The material covered here is fundamental to all areas of Data Science and hence open to researchers from all disciplines that deal with significant amounts of data. The focus is to provide a practical introduction to these topics with extensive labs and seminars.

• Open Science
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• Computational Infrastructures
• A limited number of grants are available to support the attendance of selected participants from developing countries.
• Professionals and corporate entities must register and apply via the ITU Academy platform.

About the CODATA-RDA Research Data Science Summer School, 5-16 August 2019

Ictp school poster for datatrieste 2019.

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from Irkusk train station to Taltsy, and then to Listvjanka - Irkutsk Oblast Forum

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from Irkusk train station to Taltsy, and then to Listvjanka

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on first day of august I and my family will arrive in Irkusk by Transiberian train in the morning,

We'll need to move to Taltsly museum and, after visit, to Listvjanka.

I read that I can move to bus station with tram n.4a, and from there by bus to museum.

Have I to book bus or can i wait for it in bus station?

Or is it better a taxi for 4 persons?

From museum how can we go to Listvjanka?

Thank you very much

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