statistics courses for phd students

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Department of Statistics

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• PhD Program

The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the  basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.

PhD Program Requirements

PhD Program Admissions Process

• PhD Admissions FAQ
• AM in Statistics
• Stat 300: Research in Statistics
• Stat 303: The Art and Practice of Teaching Statistics

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Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students.  Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.  

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

• SDS 384.2 Mathematical Statistics I
• SDS 383C Statistical Modeling I
• SDS 387 Linear Models
• SDS 384.11 Theoretical Statistics
• SDS 383D Statistical Modeling II
• SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later. 

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

• Required: SDS 190 Readings in Statistics (1 credit hour)
• Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
• One or more elective courses selected from approved electives ; and/or
• One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning. 
• Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
• GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

• Discuss potential candidacy exam topics with advisor
• Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
•   Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

• 2023-24 Student Handbook
• Annual Review At the end of every spring semester, students in their second year and beyond are expected to fill out an annual review form distributed by the Graduate Program Administrator. 
• Seminar Series All students are expected to attend the SDS Seminar Series
• SDS 189R Course Description (when taken for internship)
• Internship Course Registration form
• Intel Corporation
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Attending Conferences 

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

• Request for Travel Authorization (both domestic and international travel)
• Request for Authorization for International Travel  

Fall 2024 Semester PhD Courses

For the most updated information on Statistics PhD courses, please go to Vergil . 

Spring 2024 Semester PhD Courses

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Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

Statistics PhD

The Department of Statistics offers the Master of Arts (MA) and Doctor of Philosophy (PhD) degrees.

Master of Arts (MA)

The Statistics MA program prepares students for careers that require statistical skills. It focuses on tackling statistical challenges encountered by industry rather than preparing for a PhD. The program is for full-time students and is designed to be completed in two semesters (fall and spring).

There is no way to transfer into the PhD program from the MA program. Students must apply to the PhD program.

Doctor of Philosophy (PhD)

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. The standard PhD program in statistics provides a broad background in probability theory and applied and theoretical statistics.

There are three designated emphasis (DE) tracks available to students in the PhD program who wish to pursue interdisciplinary work formally: Computational and Data Science and Engineering , Computational and Genomic Biology and Computational Precision Health .

Contact Info

[email protected]

367 Evans Hall

Berkeley, CA 94720-3860

At a Glance

Department(s)

Admit Term(s)

Application Deadline

December 3, 2024

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

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• Wharton Online

Statistics and Data Science

Wharton’s phd program in statistics and data science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. these include theoretical research in mathematical statistics as well as interdisciplinary research in the social sciences, biology and computer science..

Wharton’s PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include:

• analysis of observational studies;
• Bayesian inference, bioinformatics;
• decision theory;
• game theory;
• high dimensional inference;
• information theory;
• machine learning;
• model selection;
• nonparametric function estimation; and
• time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

For information on courses and sample plan of study, please visit the University Graduate Catalog .

Get the Details.

Visit the Statistics and Data Science website for details on program requirements and courses. Read faculty and student research and bios to see what you can do with a Statistics PhD.

Statistics and Data Science Doctoral Coordinator 

Dr. Bhaswar Bhattacharya Associate Professor of Statistics and Data Science Associate Professor of Mathematics (secondary appointment) Email: [email protected] Phone: 215-573-0535

PhD in Statistics

Program description.

The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry.  The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas. To enter, students need a bachelor’s degree in mathematics, statistics, or a closely related discipline. Students graduating with a PhD in Statistics are expected to:

• Demonstrate an understanding the core principles of Probability Theory, Estimation Theory, and Statistical Methods.
• Demonstrate the ability to conduct original research in statistics.
• Demonstrate the ability to present research-level statistics in a formal lecture

Requirements for the Ph.D. (Statistics Track)

Course Work A Ph.D. student in our department must complete sixteen courses for the Ph.D. At most, four of these courses may be transferred from another institution. If the Ph.D. student is admitted to the program at the post-Master’s level, then eight courses are usually required.

Qualifying Examinations First, all Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). Then, to qualify a student to begin work on a PhD dissertation, they must pass two of the following three exams at the PhD level: probability, mathematical statistics, and applied statistics. The probability and mathematical statistics exams are offered every September and the applied statistics exam is offered every April.

• PhD Exam in Probability: This exam covers the material covered in MA779 and MA780 (Probability Theory I and II).
• PhD Exam in Mathematical Statistics: This exam covers material covered in MA781 (Estimation Theory) and MA782 (Hypothesis Testing).
• PhD Exam in Applied Statistics: This exam covers the same material as the M.A. Applied exam and is offered at the same time, except that in order to pass it at the PhD level a student must correctly solve all four problems.

Note: Students concentrating in probability may choose to do so either through the statistics track or through the mathematics track. If a student wishes to do so through the mathematics track, the course and exam requirements are different. Details are available here .

Dissertation The dissertation is the major requirement for a Ph.D. student. After the student has completed all course work, the Director of Graduate Studies, in consultation with the student, selects a three-member dissertation committee. One member of this committee is designated by the Director of Graduate Studies as the Major Advisor for the student. Once completed, the dissertation must be defended in an oral examination conducted by at least five members of the Department.

The Dissertation and Final Oral Examination follows the   GRS General Requirements for the Doctor of Philosophy Degree .

Satisfactory Progress Toward the Degree Upon entering the graduate program, each student should consult the Director of Graduate Studies (Prof. David Rohrlich) and the Associate Director of the Program in Statistics (Prof. Konstantinos Spiliopoulos). Initially, the Associate Director of the Program in Statistics will serve as the default advisor to the student. Eventually the student’s advisor will be determined in conjunction with their dissertation research. The Associate Director of the Program in Statistics, who will be able to guide the student through the course selection and possible directed study, should be consulted often, as should the Director of Graduate Studies. Indeed, the Department considers it important that each student progress in a timely manner toward the degree. Each M.A. student must have completed the examination by the end of their second year in the program, while a Ph.D. student must have completed the qualifying examination by the third year. Students entering the Ph.D. program with an M.A. degree must have completed the qualifying examination by October of the second year. Failure to meet these deadlines may jeopardize financial aid. Some flexibility in the deadlines is possible upon petition to the graduate committee in cases of inadequate preparation.

Students enrolled in the Graduate School of Arts & Sciences (GRS) are expected to adhere to a number of policies at the university, college, and departmental levels. View the policies on the Academic Bulletin and GRS website .

Residency Post-BA students must complete all of the requirements for a Ph.D. within seven years of enrolling in the program and post-MA students must complete all requirements within five years. This total time limit is set by the Graduate School. Students needing extra time must petition the Graduate School. Also, financial aid is not guaranteed after the student’s fifth year in the program.

Financial Aid

As with all Ph.D. students in the Department of Mathematics and Statistics, the main source of financial aid for graduate students studying statistics is a Teaching Fellowship. These awards carry a stipend as well as tuition remission for six courses per year. Teaching Fellows are required to assist a faculty member who is teaching a course, usually a large lecture section of an introductory statistics course. Generally, the Teaching Fellow is responsible for conducting a number of discussion sections consisting of approximately twenty-five students each, as well as for holding office hours and assisting with grading. The Teaching Fellowship usually entails about twenty hours of work per week. For that reason, Teaching Fellows enroll in at most three courses per semester. A Teaching Fellow Seminar is conducted to help new Teaching Fellows develop as instructors and to promote the continuing development of experienced Teaching Fellows.

Other sources of financial aid include University Fellowships and Research Assistantships. The University Fellowships are one-year awards for outstanding students and are service-free. They carry stipends plus full tuition remission. Students do not need to apply for these fellowships. Research Assistantships are linked to research done with individual faculty, and are paid for through those faculty members’ grants. As a result, except on rare occasions, Research Assistantships typically are awarded to students in their second year and beyond, after student and faculty have had sufficient time to determine mutuality of their research interests.

Regular reviews of the performance of Teaching Fellows and Research Assistants in their duties as well as their course work are conducted by members of the Department’s Graduate Committee.

Ph.D. Program Milestones

The department considers it essential that each student progress in a timely manner toward completion of the degree. The following are the deadlines for achieving the milestones described in the Degree Requirements and constitute the basis for evaluating satisfactory progress towards the Ph.D. These deadlines are not to be construed as expected times to complete the various milestones, but rather as upper bounds. In other words,   a student in good standing expecting to complete   the degree within the five years of guaranteed funding will meet these milestones by the much e arlier target dates indicated below.   Failure to achieve these milestones in a timely manner may affectfinancial aid.

• Target: April of Year 1
• Deadline: April of Year 2
• Target: Spring of Year 2 post-BA/Spring of Year 1 post-MA
• Deadline: End of Year 3 post-BA/Fall of Year 2 post-MA
• Target: Spring of Year 2
• Deadline: End of Year 3
• Target: Spring of Year 2 or Fall of Year 3 post-BA/October of Year 2 post-MA
• Deadline: End of Year 3 post-BA/October of Year 2 post-MA
• Target: end of Year 3
• Deadline: End of Year 4
• Target: End of Year 5
• Deadline: End of Year 6

If you have any questions regarding our PhD program in Statistics, please reach out to us at [email protected]

Department of Technology, Operations, and Statistics | Doctoral Program in Statistics

Doctoral program in statistics.

• Program of Study

Program Requirements

• Doctoral Students and Their Research
• Statistics Faculty

Overview of the Doctoral Program in Statistics

The world’s financial markets produce an enormous stream of data, and the understanding of the techniques needed to analyze and extract information from this stream has become critical.   Doctoral work in statistics combines theory and methodology to deal with the large quantity of statistical data.  Here at Stern we use the theoretical and methodological orientation of a traditional statistics with a focus on the applications that are central to the concerns of a business school.  The PhD thesis work at Stern is a mathematically sophisticated enterprise that never loses sight of the real and practical problems of business.

Stern’s curriculum in statistics prepares students for academic positions by preparing them to conduct independent research.  The statistician must be knowledgeable of the basic issues of the intellectual areas in which his or her work will be applied. 

The most popular areas of student interest in the last few years have been mathematical finance, statistical modeling, data mining, stochastic processes, and econometrics.

Students have rigorous course work and participate in special topics seminars.  They work closely with the faculty and also present special PhD student seminars.

Clifford Hurvich Coordinator, Statistics Doctoral Program

Mission Our mission is the education of scholars who will produce first-rate statistics research and who will succeed as faculty members at first-rate universities.

Admissions and performance We enroll one or two students each year;  these are chosen from approximately 100 highly qualified applicants.

Advising and evaluation Each student will meet with a committee of faculty members yearly to assess progress through the program.

Research and interaction with faculty The Stern statistics faculty have a wide range of interests, but there is special emphasis on time series, statistical modeling, stochastic processes, and financial modeling.

PhD students in statistics take courses in statistical inference, stochastic processes, time series, regression analysis, and multivariate analysis.

In addition to course work, doctoral students also participate in research projects in conjunction with faculty members.  The students attend seminars, present seminars on their own work, and submit their work for publication.

The program culminates with the creation of the PhD thesis, through the stages of proposal, writing, and defense.

Most students finish in four to five years.

Statistics Program of Study

Statistics PhD students take their course work in the first two years of study.  These courses are taken within the Statistics Group (both as formal courses and also as independent study), within other departments at the Stern School, at NYU's Courant Institute, and at Columbia University.

In addition to their statistics courses, doctoral students in Statistics often take courses in mathematics, finance, market research, and econometrics.  The individual curriculum will be planned with the help of faculty advisers.

Questions about the PhD Program in Statistics?

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PhD Program

Advanced undergraduate or masters level work in mathematics and statistics will provide a good background for the doctoral program. Quantitatively oriented students with degrees in other scientific fields are also encouraged to apply for admission. In particular, the department has expanded its research and educational activities towards computational biology, mathematical finance and information science. The doctoral program normally takes four to five years to complete.

Doctoral Program in Statistics

Statistics phd minor.

PhD Admissions

The Berkeley Statistics Department is a community of researchers and educators studying diverse topics within statistics, data science, and probability. We believe that individuals from diverse backgrounds offer unique perspectives that intellectually enrich our field. We are central to research life on campus and have forged strong interdisciplinary links with other departments, particularly Biostatistics, Mathematics, Electrical Engineering and Computer Science, Political Science and Biology. We address data problems in molecular biology, geophysics, astronomy, epidemiology, neurophysiology, sociology, political science, education, demography, and the U.S. Census.

Our PhD program welcomes students from a broad range of theoretical, applied, and interdisciplinary backgrounds, and provides rigorous preparation for a future career in statistics, probability, or data science. Our top-ranked program usually takes 5 years to complete. PhD theses are diverse and varied, reflecting the scope of faculty research interests, with many students involved in interdisciplinary research. There are also Designated Emphases in Computational and Genomic Biology; Computational Precision Health; and Computational Science and Engineering if one chooses to take a more concentrated approach.

Our department has been a leader in embracing machine learning and data science. We helped found the Division of Computing, Data Science, and Society (CDSS) , which was launched in 2019 under Associate Provost Jennifer Chayes and continues to strengthen both our interdisciplinary ties and foundational research. Our graduates go on to solve impactful problems in academia, industry, and non-profits, informing consequential decisions such as election auditing, medical treatment, police reform, and scientific reproducibility, and developing elegant mathematical tools for understanding networks, genetics, and language, among other areas.

Financial Support

Program information,  important dates for fall 2025 phd applications.

📅 Application Opens: September 12, 2024

🗓️ Application Deadline: December 3, 2024

Additional Information:

GRE Requirements for Fall 2025:

General GRE: Not required and will not be accepted

Subject Tests: Optional

Learn more about the PhD Program and Application Below!

• Youth Program
• Wharton Online

Descriptions of Graduate Level Courses

Stat9150 - nonparametric inference (course syllabus).

Statistical inference when the functional form of the distribution is not specified. Nonparametric function estimation, density estimation, survival analysis, contingency tables, association, and efficiency.

Prerequisites: STAT 5200

STAT9200 - Sample Survey Methods (Course Syllabus)

This course will cover the design and analysis of sample surveys. Topics include simple random sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias.

Prerequisites: STAT 5200 OR STAT 9610 OR STAT 9700

STAT9210 - Observational Studies (Course Syllabus)

This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching and propensity score methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.

STAT9220 - Advanced Causal Inference (Course Syllabus)

This course will provide an in depth investigation of statistical methods for drawing causal inferences from complex observational studies and imperfect randomized experiments. Formalization will be given for key concepts at the foundation of causal inference, including: confounding, comparability, positivity, interference, intermediate variables, total effects, controlled direct effects, natural direct and indirect effects for mediation analysis, generalizability, transportability, selection bias, etc.... These concepts will be formally defined within the context of a counterfactual causal model. Methods for estimating total causal effects in the context of both point and time-varying exposure will be discussed, including regression-based methods, propensity score techniques and instrumental variable techniques for continuous, discrete, binary and time to event outcomes. Mediation analysis will be discussed from a counterfactual perspective. Causal directed acyclic graphs (DAGs) and associated nonparametric structural equations models (NPSEMs) will be used to formalize identification of causal effects for static and dynamic longitudinal treatment regimes under unconfoundedness and unmeasured confounding settings. This formalization will be used to define, identify and make inferences about the joint effects of time-varying exposures in the presence of (possibly hidden) time-dependent covariates that are simultaneously confounders and intermediate variables. These methods include g-estimation of structural nested models, inverse probability weighted estimators of marginal structural models, and g-computation algorithm estimators. Credible quasi-experimental causal inference methods will be described, leveraging auxiliary variables such as instrumental variables, negative control variables, or more broadly confounding proxy variables. Quasi-experimental methods discussed will include the control outcome calibration approach, proximal causal inference, difference-in-differences and related generalizations of these methods. Semiparametric efficiency and the prospects for doubly robust inference will feature prominently throughout the course, including methods that combine modern semiparametric theory and machine learning techniques.

STAT9250 - Multivariate Analy: Theo (Course Syllabus)

This is a course that prepares PhD students in statistics for research in multivariate statistics and high dimensional statistical inference. Topics from classical multivariate statistics include the multivariate normal distribution and the Wishart distribution; estimation and hypothesis testing of mean vectors and covariance matrices; principal component analysis, canonical correlation analysis and discriminant analysis; etc. Topics from modern multivariate statistics include the Marcenko-Pastur law, the Tracy-Widom law, nonparametric estimation and hypothesis testing of high-dimensional covariance matrices, high-dimensional principal component analysis, etc.

Prerequisites: STAT 9300 OR STAT 9700 OR STAT 9720

STAT9260 - Multivariate Analy: Meth (Course Syllabus)

This is a course that prepares PhD students in statistics for research in multivariate statistics and data visualization. The emphasis will be on a deep conceptual understanding of multivariate methods to the point where students will propose variations and extensions to existing methods or whole new approaches to problems previously solved by classical methods. Topics include: principal component analysis, canonical correlation analysis, generalized canonical analysis; nonlinear extensions of multivariate methods based on optimal transformations of quantitative variables and optimal scaling of categorical variables; shrinkage- and sparsity-based extensions to classical methods; clustering methods of the k-means and hierarchical varieties; multidimensional scaling, graph drawing, and manifold estimation.

Prerequisites: STAT 9610

STAT9270 - Bayesian Statistics (Course Syllabus)

This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. We will cover fundamental topics in Bayesian probability modeling and implementation, including recent advances in both optimization and simulation-based estimation strategies. Key topics covered in the course include hierarchical and mixture models, Markov Chain Monte Carlo, hidden Markov and dynamic linear models, tree models, Gaussian processes and nonparametric Bayesian strategies.

Prerequisites: STAT 4300 OR STAT 5100

STAT9280 - Stat Learning Theory (Course Syllabus)

Statistical learning theory studies the statistical aspects of machine learning and automated reasoning, through the use of (sampled) data. In particular, the focus is on characterizing the generalization ability of learning algorithms in terms of how well they perform on "new" data when trained on some given data set. The focus of the course is on: providing the fundamental tools used in this analysis; understanding the performance of widely used learning algorithms; understanding the "art" of designing good algorithms, both in terms of statistical and computational properties. Potential topics include: empirical process theory; online learning; stochastic optimization; margin based algorithms; feature selection; concentration of measure. Background in probability and linear algebra recommended.

STAT9300 - Probability Theory (Course Syllabus)

Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

Prerequisites: STAT 4300 OR STAT 5100 OR MATH 6080

STAT9310 - Stochastic Processes (Course Syllabus)

Continuation of MATH 6480/STAT 9300, the 2nd part of Probability Theory for PhD students in the math or statistics department. The main topics include Brownian motion, martingales, Ito's formula, and their applications to random walk and PDE.

Prerequisites: MATH 5460 OR STAT 9300

STAT9550 - Stoch Cal & Fin Appl (Course Syllabus)

Selected topics in the theory of probability and stochastic processes.

Prerequisites: STAT 9300

STAT9600 - Stat Algorithms & Comp (Course Syllabus)

This course aims to prepare students for graduate work in the design, analysis, and implementation of statistical algorithms. The target audience is Ph.D. students in statistics or in adjacent fields, such as computer science, mathematics, electrical engineering, computational biology, economics, and marketing. We will take a fundamental approach and focus on classes of algorithms of primary importance in statistics and statistical machine learning. Some meta-classes of algorithms that may receive significant attention are optimization, sampling, and numerical linear algebra. I aim to make the content complementary rather than overlapping with other courses at Penn, such as ESE6050, CIS6770, and the CIS7000 series. While there may be some overlap in the portions of the course that cover optimization, the sampling (Monte Carlo and related) aspects of the course are, to my knowledge, hard to find elsewhere at Penn. The course is fast paced and I expect a certain degree of mathematical preparation. Most students in the above mentioned programs will have the requisite mathematics background. I also expect familiarity with an appropriate programming language such as R, python, or matlab. The course will be mostly language agnostic. However, I may at times give example code in one of these languages, and you will be expected to be able to read the code even if it is not in your "primary" language. We may make use of various open-source toolboxes and packages for these environments, such as the Stan probabilistic programming language (best used with R) and the cvx toolbox for convex programming (available for multiple platforms but perhaps best used with matlab).

STAT9610 - Statistical Methodology (Course Syllabus)

This is a course that prepares 1st year PhD students in statistics for a research career. This is not an applied statistics course. Topics covered include: linear models and their high-dimensional geometry, statistical inference illustrated with linear models, diagnostics for linear models, bootstrap and permutation inference, principal component analysis, smoothing and cross-validation.

Prerequisites: STAT 4310 OR STAT 5200

STAT9620 - Adv Methods Applied Stat (Course Syllabus)

This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.

STAT9700 - Mathematical Statistics (Course Syllabus)

Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.

STAT9710 - Intro To Linear Stat Mod (Course Syllabus)

Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.

Prerequisites: STAT 9700

STAT9720 - Adv Topics in Math Stat (Course Syllabus)

A continuation of STAT 9700.

Prerequisites: STAT 9700 AND STAT 9710

STAT9740 - Modern Regression (Course Syllabus)

Function estimation and data exploration using extensions of regression analysis: smoothers, semiparametric and nonparametric regression, and supervised machine learning. Conceptual foundations are addressed as well as hands-on use for data analysis.

Prerequisites: STAT 1020 OR STAT 1120

STAT9800 - Intro to Biomed Data Science (Course Syllabus)

This course offers a comprehensive introduction to biomedical data science research, tailored for graduate students from Statistics and various interdisciplinary domains. Aimed at facilitating end-to-end data science research capabilities, this course covers the development and application of computational methods and statistical techniques for analyzing voluminous datasets, particularly in biology, healthcare, and medicine. Students will gain insights into various data types prevalent in biomedical research, emerging large-scale data resources, and the art of formulating scientific questions. The course encompasses methodology research, scientific research, collaborative research, computing tools, software development, as well as scientific writing, including both research papers and grant proposals. By the end of the course, students will be equipped with the foundational skills and knowledge required to excel as statisticians and research scientists, whether they choose to pursue a career in industry or academia. Prerequisite: For students from the STAT department, this course is tailored for those who have successfully completed the qualifying exam and are ready to embark on their research journey. Exceptions for first-year students will be considered on an individual basis. For master's or Ph.D. students from other departments or programs, such as AMCS, the prerequisites will differ based on their specific curriculum. At a minimum, students should have master-level expertise in one or more of the following areas: applied mathematics and probability, computing and software development, web development, bioinformatics, biostatistics, epidemiology, computational biology, genetics/genomics, neuroscience, radiology, and medical imaging.

STAT9910 - Sem in Adv Appl of Stat (Course Syllabus)

This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

STAT9950 - Dissertation (Course Syllabus)

Stat9990 - independent study (course syllabus).

Written permission of instructor and the department course coordinator required to enroll.

STAT9999 - Independent Study (Course Syllabus)

Department of statistics and data science.

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

PhD Program

• Contact Information
• Course Descriptions
• Course Schedule
• Doctoral Inside: Resources for Current PhD Students
• Penn Career Services
• Apply to Wharton
• Financial Aid
• William Bekerman , PhD Student
• Jinho Bok , PhD Student
• Abhinav Chakraborty , PhD Student
• Anirban Chatterjee , PhD Student
• Sayak Chatterjee , PhD Student
• Abhinandan Dalal , PhD Student
• Mauricio Daros Andrade , PhD Student
• Joseph Deutsch , PhD Student
• Wei Fan , PhD Student
• Zirui Fan , PhD Student
• Ryan Gross , PhD Student
• Yu Huang , PhD Student
• Zhihan Huang , PhD Student
• Kevin Jiang , PhD Student
• Dongwoo Kim , PhD Student
• Junu Lee , PhD Student
• Chris Lin , PhD Student
• Yuxuan Lin , PhD Student
• Kaishu Mason , PhD Student
• Ziang Niu , PhD Student
• Manit Paul , PhD Student
• Joseph Rudoler , PhD Student
• Henry Shugart , PhD Student
• Kevin Tan , PhD Student
• Hwai-Liang Tung , PhD Student
• Xiaomeng Wang , PhD Student
• Yangxinyu Xie , PhD Student
• Ziqing Xu , PhD Student
• Jeffrey Zhang , PhD Student
• Zhaojun Zhang , PhD Student
• Zijie Zhuang , PhD Student

Department of Statistics

Last update: 11/10/23

PhD Degree in Statistics

The Department of Statistics offers an exciting and recently revamped PhD program that involves students in cutting-edge interdisciplinary research in a wide variety of fields. Statistics has become a core component of research in the biological, physical, and social sciences, as well as in traditional computer science domains such as artificial intelligence and machine learning. The massive increase in the data acquired, through scientific measurement on one hand and through web-based collection on the other, makes the development of statistical analysis and prediction methodologies more relevant than ever.

Our graduate program prepares students to address these issues through rigorous training in scientific computation, and in the theory, methodology, and applications of statistics. The course work includes four core sequences:

• Probability (STAT 30400, 38100, 38300)
• Mathematical statistics (STAT 30400, 30100, 30210)
• Applied statistics (STAT 34300, 34700, 34800)
• Computational mathematics and machine learning (STAT 30900, 31015/31020, 37710).

All students must take the Applied Statistics and Theoretical Statistics sequence. In addition it is highly recommended that students take a third core sequence based on their interests and in consultation with the Department Graduate Advisor (DGA). At the start of their second year, the students take two preliminary examinations. All students must take the Applied Statistics Prelim. For the second the students can choose to take either the Theoretical Statistics or the Probability prelim. Students planning to take the Probability prelim should take the Probability sequence as their third sequence.

Incoming first-year students have the option of taking any or all of these exams; if an incoming student passes one or more of these, then he/she will be excused from the requirement of taking the first-year courses in that subject. During the second and subsequent years, students can take more advanced courses, and perform research, with world-class faculty in a wide variety of research areas .

In recent years, a large majority of our students complete the PhD within four or five years of entering the program. Students who have significant graduate training before entering the program can (and do) obtain their doctor's degree in three years.

Most students receiving a doctorate proceed to faculty or postdoctoral appointments in research universities. A substantial number take positions in government or industry, such as in research groups in the government labs, in communications, in commercial pharmaceutical companies, and in banking/financial institutions. The department has an excellent track record in placing new PhDs.

Prerequisites for the Program

A student applying to the PhD program normally should have taken courses in advanced calculus, linear algebra, probability, and statistics. Additional courses in mathematics, especially a course in real analysis, will be helpful. Some facility with computer programming is expected. Students without background in all of these areas, however, should not be discouraged from applying, especially if they have a substantial background, through study or experience, in some area of science or other discipline involving quantitative reasoning and empirical investigation. Statistics is an empirical and interdisciplinary field, and a strong background in some area of potential application of statistics is a considerable asset. Indeed, a student's background in mathematics and in science or another quantitative discipline is more important than his or her background in statistics.

To obtain more information about applying, see the Guide For Applicants .

Students with questions may contact Yali Amit for PhD Studies, Mei Wang for Masters Studies, and Keisha Prowoznik for all other questions, Bahareh Lampert (Dean of Students in the Physical Sciences Division), or Amanda Young (Associate Director, Graduate Student Affairs) in UChicagoGRAD.

Handbook for PhD Students in Statistics

Information for first and second year phd students in statistics.

Statistics, PHD

On this page:, at a glance: program details.

• Location: Tempe campus
• Second Language Requirement: No

Program Description

Degree Awarded: PHD Statistics

As a science, statistics focuses on data collection and data analysis by using theoretical, applied and computational tools. The PhD program in statistics reflects this breadth in tools and considerations while allowing students sufficient flexibility to tailor their program of study to reflect individual interests and goals. Research can be of a disciplinary or transdisciplinary nature.

Degree Requirements

Curriculum plan options.

• 84 credit hours, a written comprehensive exam, a prospectus and a dissertation

Required Core (3 credit hours) STP 526 Theory of Statistical Linear Models (3)

Other Requirements (15 credit hours) IEE 572 Design Engineering Experiments (3) or STP 531 Applied Analysis of Variance (3) IEE 578 Regression Analysis (3) or STP 530 Applied Regression Analysis (3) STP 501 Theory of Statistics I: Distribution Theory 3 (3) STP 502 Theory of Statistics II: Inference (3) STP 527 Statistical Large Sample Theory (3)

Electives (42 credit hours)

Research (12 credit hours) STP 792 Research (12)

Culminating Experience (12 credit hours) STP 799 Dissertation (12)

Additional Curriculum Information Electives are chosen from statistics or related area courses approved by the student's supervisory committee.

Other requirements courses may be substituted with department approval.

Students must pass:

• one qualifying examination and coursework in analysis
• a written comprehensive examination
• a dissertation prospectus defense

Students should see the department website for examination information.

Each student must write a dissertation and defend it orally in front of five dissertation committee members.

Admission Requirements

Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in mathematics, statistics or a closely related area from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

• graduate admission application and application fee
• official transcripts
• statement of education and career goals
• three letters of recommendation
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

Completion of the following courses (equivalents at ASU are given in parentheses) is required. Applicants who lack any of these prerequisite courses must complete them before being considered for admission.

• calculus (MAT 270, 271 and 272)
• advanced calculus (MAT 371)
• linear algebra (MAT 342)
• computer programming (CSE 100)
• introductory applied statistics (STP 420)

Next Steps to attend ASU

Learn about our programs, apply to a program, visit our campus, application deadlines, learning outcomes.

• Able to complete original research in statistics.
• Proficient in applying advanced statistical methods in coursework and research.
• Address an original research question in statistics.

Career Opportunities

Statistical analysis and data mining have been identified as two of the most desirable skills in today's job market. Data, and the analysis of data, is big business, and the Department of Labor projects that overall employment of mathematicians and statisticians will grow 33% between 2020 and 2030, much faster than the average for all occupations.

For graduates of the doctoral program in statistics, that means a broad variety of career opportunities in fields as diverse as business, finance, engineering, technology, education, marketing, government and other areas of the economy.

These are just a few of the top career opportunities available for a graduate with a doctoral degree in statistics:

• business consultant or analyst
• data science professor, instructor or researcher
• data scientist
• faculty-track academic
• financial analyst
• market research analyst
• software engineer
• statistician

Program Contact Information

If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below.

• [email protected]
• 480/965-3951

• Graduate Studies

Ph.D. Program

The PhD program prepares students for research careers in theory and application of probability and statistics in academic and non-academic (e.g., industry, government) settings.  Students might elect to pursue either the general Statistics track of the program (the default), or one of the four specialized tracks that take advantage of UW’s interdisciplinary environment: Statistical Genetics (StatGen), Statistics in the Social Sciences (CSSS), Machine Learning and Big Data (MLBD), and Advanced Data Science (ADS). 

Admission Requirements

For application requirements and procedures, please see the graduate programs applications page .

Recommended Preparation

The Department of Statistics at the University of Washington is committed to providing a world-class education in statistics. As such, having some mathematical background is necessary to complete our core courses. This background includes linear algebra at the level of UW’s MATH 318 or 340, advanced calculus at the level of MATH 327 and 328, and introductory probability at the level of MATH 394 and 395. Real analysis at the level of UW’s MATH 424, 425, and 426 is also helpful, though not required. Descriptions of these courses can be found in the UW Course Catalog . We also recognize that some exceptional candidates will lack the needed mathematical background but succeed in our program. Admission for such applicants will involve a collaborative curriculum design process with the Graduate Program Coordinator to allow them to make up the necessary courses. 

While not a requirement, prior background in computing and data analysis is advantageous for admission to our program. In particular, programming experience at the level of UW’s CSE 142 is expected.  Additionally, our coursework assumes familiarity with a high-level programming language such as R or Python. 

Graduation Requirements 

This is a summary of the department-specific graduation requirements. For additional details on the department-specific requirements, please consult the  Ph.D. Student Handbook .  For previous versions of the Handbook, please contact the Graduate Student Advisor .  In addition, please see also the University-wide requirements at  Instructions, Policies & Procedures for Graduate Students  and  UW Doctoral Degrees .  

General Statistics Track

• Core courses: Advanced statistical theory (STAT 581, STAT 582 and STAT 583), statistical methodology (STAT 570 and STAT 571), statistical computing (STAT 534), and measure theory (either STAT 559 or MATH 574-575-576).  
• Elective courses: A minimum of four approved 500-level classes that form a coherent set, as approved in writing by the Graduate Program Coordinator.  A list of elective courses that have already been pre-approved or pre-denied can be found here .
• M.S. Theory Exam: The syllabus of the exam is available here .
• Research Prelim Exam. Requires enrollment in STAT 572. 
• Consulting.  Requires enrollment in STAT 599. 
• Applied Data Analysis Project.  Requires enrollment in 3 credits of STAT 597. 
• Statistics seminar participation: Students must attend the Statistics Department seminar and enroll in STAT 590 for at least 8 quarters. 
• Teaching requirement: All Ph.D. students must satisfactorily serve as a Teaching Assistant for at least one quarter. 
• General Exam. 
• Dissertation Credits.  A minimum of 27 credits of STAT 800, spread over at least three quarters. 
• Passage of the Dissertation Defense. 

Statistical Genetics (StatGen) Track

Students pursuing the Statistical Genetics (StatGen) Ph.D. track are required to take BIOST/STAT 550 and BIOST/STAT 551, GENOME 562 and GENOME 540 or GENOME 541. These courses may be counted as the four required Ph.D.-level electives. Additionally, students are expected to participate in the Statistical Genetics Seminar (BIOST581) in addition to participating in the statistics seminar (STAT 590). Finally, students in the Statistics Statistical Genetics Ph.D. pathway may take STAT 516-517 instead of STAT 570-571 for their Statistical Methodology core requirement. This is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript.

Statistics in the Social Sciences (CSSS) Track

Students in the Statistics in the Social Sciences (CSSS) Ph.D. track  are required to take four numerically graded 500-level courses, including at least two CSSS courses or STAT courses cross-listed with CSSS, and at most two discipline-specific social science courses that together form a coherent program of study. Additionally, students must complete at least three quarters of participation (one credit per quarter) in the CS&SS seminar (CSSS 590). This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript.

Machine Learning and Big Data Track

Students in the Machine Learning and Big Data (MLBD) Ph.D. track are required to take the following courses: one foundational machine learning course (STAT 535), one advanced machine learning course (either STAT 538 or STAT 548 / CSE 547), one breadth course (either on databases, CSE 544, or data visualization, CSE 512), and one additional elective course (STAT 538, STAT 548, CSE 515, CSE 512, CSE 544 or EE 578). At most two of these four courses may be counted as part of the four required PhD-level electives. Students pursuing this track are not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript. 

Advanced Data Science (ADS) Track

Students in the Advanced Data Science (ADS) Ph.D. track are required to take the same coursework as students in the Machine Learning and Big Data track. They are also not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. The only difference in terms of requirements between the MLBD and the ADS tracks is that students in the ADS track must also register for at least 4 quarters of the weekly eScience Community Seminar (CHEM E 599). Also, unlike the MLBD track, the ADS is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript. 

DEPARTMENT OF STATISTICS AND DATA SCIENCE

• For Current PhD Students

Required Courses for PhD

Required statistics and data science coursework:.

View requirements prior to 2021

The required Statistics and Data Science courses are:

• STAT 344 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression
• STAT 415-0 Introduction to Machine Learning ( was STAT 435-0 Mathematical Foundations of Machine Learning in 2021-2022 )
• STAT 420-1 Introduction to Statistical Theory and Methodology 1
• STAT 420-2 Introduction to Statistical Theory and Methodology 2
• STAT 420-3 Introduction to Statistical Theory and Methodology 3
• STAT 457-0 Applied Bayesian Inference
• At least 4 electives (300- and 400-level graduate courses in Statistics), among which 2 must be 400 level. See STAT courses approved for the PhD coursework below. (STAT graduate level courses excluded for Statistics PhD students: STAT 301-1,2,3, STAT 303-1,2,3, STAT 320-1,2,3, STAT 330-1, and STAT 357) Independent Study registrations cannot be used to fulfill the coursework requirements. 

Additional Required Coursework:

In addition to the 12 courses listed above, PhD students must take:

• STAT 430-1 Probability for Statistical Inference 1
• STAT 430-2 Probability for Statistical Inference 2
• STAT 440 Stochastic Processes for Statistical Modeling and Inference

Approved STAT elective courses for PhD:

at least 2 elective courses must be 400 level

• STAT 302 Data Visualization
• STAT 328-0 Causal Inference
• STAT 348-0 Applied Multivariate Analysis
• STAT 351-0 Design Analysis of Experiments
• STAT 352-0 Nonparametric Statistical Methods
• STAT 354-0 Applied Time Series Modeling (currently would register for the STAT 359 section of this course)
• STAT 356-0 Hierarchical Linear Models
• STAT 359-0 Topics in Statistics
• STAT 365-0 Intro Analysis Financial Data
• STAT 435-0 Mathematical Foundations of Machine Learning
• STAT 439-0 Meta-Analysis
• STAT 455-0 Advanced Qualitative Data Analysis
• STAT 456-0 Generalized Linear Models
• STAT 461-0 Advanced Topics in Statistics
• STAT 465-0 Statistical Methods for Bioinformatics and Computational Biology

STAT 519 Requirement:

All PhD students are required to take STAT 519 Responsible Conduct of Research Training, typically in their second year.

Prior to 2021

The required Statistics courses are:

• STAT 350 Regression Analysis
• STAT 351 Design and Analysis of Experiments or IEMS 463 Statistical Analysis of Designed Experiments (DGS will specify)
• STAT 425 Sampling Theory and Applications
• 6 other 300 and 400 graduate level courses in Statistics to complete the 12 course requirement. Of these six, at least two should be 400 level courses. Independent Study registrations cannot be used to fulfill the coursework requirements. See STAT courses approved for the PhD coursework below.

In addition to the 12 courses listed above, PhD students must take either:

• MATH 450-1 Probability 1 and MATH 450-2 Probability 2
• MATH 450-1 Probability 1 and IEMS 460-1 Stochastic Processes 1 and IEMS 460-2 Stochastic Processes 2
• STAT 344-0 Statistical Computing
• STAT 370-0 Human Rights Statistics

PhD in Statistics

The Doctor of Philosophy (PhD) program in Statistics is designed to prepare you to work on the frontiers ofthe discipline of Statistics, whether your career choice leads you into research and teaching or into leadership roles in business, industry and government.

The program is very flexible particularly in the choice of electives and of research topic. You may even choose to do research on the interface of Statistics and some other discipline, such as Computer Science, Genetics, Forestry, Bioinformatics, Economics, etc. The course requirements are designed to ensure that you have sufficient training in Probability, Statistical Inference, Computing, and Applications to prepare you for research on the cutting edge of Statistics.

Many items in this section, with some modifications for the Department’s purposes, are taken from the Graduate Bulletin .

Prerequisite and Application Information

Guidelines for PhD Program 

Detailed Program Information

NEED MORE DIRECTION?

Jaxk Reeves Graduate Coordinator I [email protected]

Liang Liu Graduate Coordinator II [email protected]

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Doctor of Philosophy in Data Science and Statistics

Program description.

The Data Science and Statistics PhD degree curriculum at The University of Texas at Dallas offers extensive coursework and intensive research experience in theory, methodology and applications of statistics. During their study, PhD students acquire the necessary skills to prepare them for careers in academia or in fields that require sophisticated data analysis skills.

The PhD program is designed to accommodate the needs and interests of the students. The student must arrange a course program with the guidance and approval of the graduate advisor. Adjustments can be made as the student’s interests develop and a specific dissertation topic is chosen.

Some of the broad research areas represented in the department include: probability theory, stochastic processes, statistical inference, asymptotic theory, statistical methodology, time series analysis, Bayesian analysis, robust multivariate statistical methods, nonparametric methods, nonparametric curve estimation, sequential analysis, biostatistics, statistical genetics, and bioinformatics.

Career Opportunities

Statisticians generally find employment in fields where there is a need to collect, analyze and interpret data — including pharmaceutical, banking and insurance industries, and government — and also in academia. The job of a statistician consistently appears near the top in the rankings of 200 jobs by CareerCast’s Jobs Rated Almanac based upon factors such as work environment, income, hiring outlook and stress.

For more information about careers in statistics, view the career page of American Statistical Association. UT Dallas PhD graduates are currently employed as statisticians, biostatisticians, quantitative analysts, managers, and so on, and also as faculty members in universities.

The  NSM Career Success Center  is an important resource for students pursuing STEM and healthcare careers. Career professionals are available to provide strategies for mastering job interviews, writing professional cover letters and resumes and connecting with campus recruiters, among other services.

Marketable Skills

Review the marketable skills for this academic program.

Application Deadlines and Requirements

The university  application deadlines apply with the exception that, for the upcoming Fall term, all application materials must be received by December 15 for first-round consideration of scholarships and fellowships. See the  Department of Mathematical Sciences graduate programs website  for additional information. 

Visit the  Apply Now  webpage to begin the application process. 

Contact Information

For more information, contact [email protected]

School of Natural Sciences and Mathematics The University of Texas at Dallas 800 W. Campbell Road Richardson, TX 75080-3021 Phone: 972-883-2416

nsm.utdallas.edu

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Statistics Data Science: Ph.D.

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How to apply >

Our Ph.D. in Statistics Data Science program offers you the opportunity to hone your skills in mathematical reasoning, statistical modeling, computation, and methodology development.

Through this new doctoral program, you will gain a thorough understanding of probability and statistics as well as machine learning methods. You’ll apply statistical methods and theory to real-world data challenges in an interdisciplinary manner. This program will expose you to cutting-edge research and developments in statistics, machine learning, artificial intelligence and data sense, preparing you for statistics and data science careers in academia, the public sector and industry.

Jump to:   Admission & Degree Requirements   |  Application Deadlines   |  Research Areas   |  Faculty  

Admission & Degree Requirements

Admission to this program is highly competitive and selective. We require you to submit the following with your application. 

All transcripts from undergraduate and graduate (if applicable) institutions. 

Three letters of recommendation.

Personal statement: Include research interests;do not exceed three pages.

GRE General test score (required but can be waived for students currently enrolled in or have already earned the MS in Statistics degree in UD)

GRE subject test in Mathematics or other STEM fields (optional).

Language scores (for international students whose native language is not English, and who have not received a degree at a U.S. college or university). A score of 100 or higher on the Test of English as a Foreign Language (TOEFL), or equivalently 7.5 or higher on the International English Language Testing System (IELTS). 

A department graduate committee will decide who is admitted to the program in compliance with University policies and procedures. The committee reserves the right to interview the applicants.

Students with an MS degree in Statistics or related fields are eligible for a 4-year accelerated track with a reduced course load. Eligibility is determined by the admission committee.

Degree Requirements

You must have, or expect to have a bachelor’s degree or higher in statistics, mathematics or a related field from an accredited college of university, by the date of admission.

Ready to Apply?

Apply now >, view course and exam requirements >, email the program director >.

* Disclaimer: The customized GPT is an experimental tool designed to provide real-time answers based on the official curriculum and commonly asked questions. GPT-generated answers may not always be accurate. Please verify all information through the official University of Delaware website.

Application deadlines

Regular admission is for each fall semester. Applicants must submit their application via the online link no later than February 1 .

Research Areas

The Statistics faculty within the department engage in a broad range of research topics. Our expertise spans classical statistical problems, such as hypothesis testing, high-dimensional data analysis, dimension reduction, time-series analysis, and nonparametric statistics, as well as contemporary topics, including network modeling, graph learning, neural networks, computational statistics, and optimization. Additionally, our faculty are actively involved in data-driven research applications across diverse fields, such as large language models, image data analysis, financial forecasting, health sciences, biology, and animal science.

The program also offers students the flexibility to pursue research in collaboration with our affiliated faculty or any other University of Delaware faculty whose work is closely aligned with statistics and data science. This interdisciplinary approach provides a unique opportunity for students to tailor their research experience to their academic and professional interests.

Core Faculty  

Dr. Shanshan Ding

Dr. Wei Qian

Dr. Jing Qiu

Dr. Cencheng Shen

Dr. Peng Zhao

Affiliated faculty

Dr. Austin Brockmeier

Dr. Rahmat Beheshiti

Dr. Yin Bao

Dr. Jeff Buler

Dr. Kyle Davis

Dr. Vu Dinh

Dr. Dominique Guillot

Dr. David Hong

Dr. Mokshay Madiman

Dr. Xi Peng

Dr. Guangmo (Amo) Tong

Dr. Xu Yuan

College of Agriculture & Natural Resources

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Psychological Research Methods with Advanced Statistics

Explore this course:.

Applications for 2024 entry closed at 5pm on Friday 6 September. Applications for 2025 entry open on Monday 16 September.

School of Psychology, Faculty of Science

Course description

Through our advanced statistical training program, you’ll learn the latest research methods that are needed to handle and interpret large datasets documenting human behaviour, preparing you for clinical training, a PhD or an exciting psychological career.

Our advanced statistical training program will equip you with the latest modelling techniques, ranging from generalised and multilevel models to the intricacies of structural equation modelling. We'll teach you essential skills and provide hands-on opportunities to apply these techniques using the R statistical environment.

Whether your interests lie in cognitive and developmental psychology, or you're drawn to social and clinical psychology, our course is tailored so you can apply advanced statistical methods across the breadth of the discipline.

Alongside your statistical training you'll learn a broad range of research techniques such as neuroimaging (EEG, fMRI), behavioural genetics, clinical trial design, qualitative interview, diary study methodologies and specialist methods for working with infants, children and clinical populations.

We’ll also train you in a range of skills that are important for psychologists in academia and professional roles: you'll understand ethical issues in research, learn how to write a grant proposal, and develop your presentation skills ready to take part in our summer postgraduate students' conference

The research project and literature review elements of the course, which include coverage of meta-analysis, give you the opportunity to focus on a chosen psychological research question in detail under the supervision of one of our world-class researchers. You can choose a supervisor from an area of psychology that matches your research interests and future career aspirations within cognitive, developmental, social or clinical psychology.

This project gives you the opportunity to put your new statistical skills and research methods knowledge into practice while addressing an issue at the cutting edge of psychological research.

MSc research projects and literature reviews often form the basis of publications in peer-reviewed journals.

• Identifying subtypes of autism
• Relationships between drinking motives and alcohol consumption: secondary data analysis of the Offending, Crime and Justice Survey
• Comparing the characteristics of child psychopathology reported by self, parent and teacher: Analysis of the British Child and Adolescent Mental Health Survey.
• Simmonds-Buckley, M., Osivwemu, E. O., Kellett, S., & Taylor, C. (2022). The acceptability of cognitive analytic therapy (CAT): Meta-analysis and benchmarking of treatment refusal and treatment dropout rates . Clinical Psychology Review , 96 , 102187.
• Griffin, B., Conner, M., & Norman, P. (2022). Applying an extended protection motivation theory to predict Covid-19 vaccination intentions and uptake in 50–64 year olds in the UK . Social Science & Medicine, 298 , 114819.  
• Tait, J., Edmeade, L., & Delgadillo, J. (2022). Are depressed patients’ coping strategies associated with psychotherapy treatment outcomes? Psychology and Psychotherapy: Theory, Research and Practice, 95 , 98–112.
• Vaci, N., Stafford, T., Ren, Y., & Habgood, J. (2024). Experiments in games: Modding the Zool Redimensioned warning system to support players’ skill acquisition and attrition rate . Proceedings of the Annual Meeting of the Cognitive Science Society, 46(0). 

Psychological Research Methods at Sheffield

In addition to Psychological Research Methods with Advanced Statistics, at Sheffield we offer two other specialist masters courses in this area that allow you to specialise further and develop the skills you need for a successful career:

• MSc Psychological Research Methods
• MSc Psychological Research Methods with Data Science

Book a 15-minute online meeting with our director of postgraduate recruitment to find out more information and ask further questions.

Book an appointment with Dr Vanessa Loaiza

An open day gives you the best opportunity to hear first-hand from our current students and staff about our courses.

You may also be able to pre-book a department/school visit as part of a campus tour. Open days and campus tours

• 1 year full-time
• 2 years part-time

You’ll learn through hands-on laboratory sessions, problem-solving classes, lectures, seminars and individual projects.

Your individual research project is the biggest part of your course, where you’ll be working alongside PhD students and experienced postdoctoral researchers. Here you’ll gain extensive first-hand experience as a researcher, and will have access to the outstanding research facilities in Sheffield.

You'll be assessed through formal examinations and coursework which may include essays, poster presentations, coding assignments, and a dissertation.

Regular feedback is also provided, so you can understand your own development throughout the course.

Your career

This course is great preparation for a PhD, and our graduates have gone on to PhD training with an advanced quantitative dimension in neuroimaging, health psychology and social psychology. Others have started their career in the higher education, health or charity sectors working as:

• Graduate Statistical Analyst or Programme Analyst in Higher Education.
• Psychological Wellbeing Practitioner, Assistant Psychologist or Research Assistant in NHS trusts or other public health organisations.
• Psychological Researcher or Lecturer in academia.

Learn more about where your psychology masters could take you here .

By choosing the School of Psychology for your postgraduate study, you'll join our global alumni network, where hundreds of our employed graduates are working across academia, healthcare, and related fields, and completing further study around the world. Explore our interactive map of graduate destinations:

School of Psychology

The School of Psychology at Sheffield is focused on exploring the science behind the human brain and human behaviour.

Our teaching is informed by cutting-edge scientific research, which ranges from cognitive and neural processes across the lifespan to the wellbeing of individuals and society . All of this has an impact on the population.

Our work explores child development, psychological therapies, health and wellbeing, lifestyle choices, cognitive behavioural therapy, safe driving, mother-baby interaction, autism, Parkinson's disease, and reducing prejudice and inequality. It’s research like this that our students are able to get involved in throughout their course.

At Sheffield, we have a range of practical teaching and research facilities where you can get hands-on, applying the knowledge you’ve gained in your masters.

For your statistical training, we have computer labs where you can access industry standard statistical analysis software SPSS, computational modelling software MATLAB, as well as flexible programming languages Python and R.

You’ll also have the chance to access a range of tools for testing participants during your research projects. Depending on your project, these may include eye-tracking technology used in perception studies, TMS and TDCS equipment for experiments involving brain stimulation, and our state-of-the-art EEG suite for measuring brain activity. Individual and group testing rooms are also available.

Student profiles

My dissertation supervisor was enthusiastic and engaging, providing guidance and support

Peter Carr Statistical Analyst in Higher Education, MSc Psychological Research Methods with Advanced Statistics

Peter began studying the MSc Psychological Research Methods with Advanced Statistics course to help him to develop the strong statistical and data science skills to be able to pursue a career in this area.

Entry requirements

Minimum 2:1 undergraduate honours degree in a relevant subject with relevant modules.

Subject requirements

We accept degrees in the following subject areas: 

• Experimental Psychology
• Psychology with Research Methods
• Quantitative Psychology

We may be able to consider degrees relating to Statistics for Psychology.

Module requirements 

You should have studied at least one module from the following areas:

• Advanced Research Methods in Psychology
• Data Analysis in Psychology
• Experimental Design
• Psychology of Research
• Quantitative Research Methods
• Research Ethics in Psychology
• Research Methods in Psychology
• Research Skills for Psychology
• Scientific Writing for Psychology
• Statistics for Psychology

IELTS 6.5 (with 6 in each component) or University equivalent

If you're an international student who does not meet the entry requirements for this course, you have the opportunity to apply for a pre-masters programme in Science and Engineering at the University of Sheffield International College . This course is designed to develop your English language and academic skills. Upon successful completion, you can progress to degree level study at the University of Sheffield.

If you have any questions about entry requirements, please contact the school/department .

Fees and funding

Each year we offer a select number of bursaries to students on our courses. If you're awarded a bursary you'll receive a £1,500 reduction in your tuition fees. These bursaries are awarded on a competitive basis, based on:

• academic performance as indicated by a grade point average and transcript
• other relevant skills and knowledge (for example, programming courses outside the degree or relevant work experience)
• research activity (co-authoring papers, conference presentations, etc)
• personal statement, which should include information on why you want to do the course you have applied for and how it fits with your aspirations

To be considered for a bursary in the year that you intend to start your course, submit your application to study with us by 31 May. All applications received before this deadline will automatically be considered for a bursary.

Applications for 2024 entry closed at 5pm on Friday 6 September. Applications for 2025 open on Monday 16 September.

More information

[email protected] +44 114 222 6533

Quantitative Methodology: Measurement and Statistics, M.S.

Fall, Spring

Full-time Part-time

• September 27, 2024 (Spring 2025)
• December 3, 2024 (Fall 2025)

June 30, 2025

In-State - $12,540 Out-of-State - $26,490 More Info

This Quantitative Methodology: Measurement and Statistics, Master of Science (M.S.) program provides you with advanced training in quantitative research methods and statistical analysis. You will learn to design and conduct research studies, analyze data using sophisticated statistical techniques, and interpret and present research findings effectively. We emphasize both theoretical knowledge and practical skills, preparing you for careers in any industry. Whether pursuing further graduate studies or entering the workforce directly, you will be well-prepared to contribute to the advancement of knowledge in your chosen field.

Key Features

• Balanced Training : Gain comprehensive skills in quantitative methods suitable for various professional settings.
• Proximity to Washington, D.C. : Access diverse academic and professional opportunities in the nation's capital.
• Rigorous Core Curriculum : Master key concepts in applied measurement, statistical modeling, and evaluation methods.
• Flexibility : Choose from a range of elective courses to deepen your expertise in specific areas of interest.
• Demonstrate proficiency in applied measurement, statistical analysis, and research design.
• Apply quantitative methods to address complex research questions in diverse contexts.
• Evaluate and critique research literature and methodologies in the field of quantitative methodology.
• Communicate quantitative findings effectively to diverse audiences through written reports and presentations.

This program offers a wide range of career pathways, including:

• Research Associate
• Data Analyst
• Policy Analyst 
• Evaluation Specialist

Click on admissions button below to swap url

Admission Requirements           Guide to Applying

You are required to submit all required documents before submitting the application.

Program Specific Requirements

• Letters of Recommendation (3)
• Graduate Record Examination (GRE)
• Writing Sample (1)

Marieh Arnett, student, Quantitative Methodology: Measurement and Statistics

Courses in this program are carefully selected and highly customizable to give you the best possible experience. Your specific program of study will be structured to take into account your background and aspirations. Both thesis and non-thesis options are available. 

QMMS Graduate Student Handbook

There is a common core of courses comprised of:

• EDMS 623 Applied Measurement: Issues and Practices (3) 
• EDMS 646 General Linear Models I (3) 
• EDMS 647 Causal Inference and Evaluation Methods (3)
• EDMS 651 General Linear Models II (3) 
• EDMS 655 Introduction to Multilevel Modeling (3) 
• EDMS 657 Exploratory Latent and Composite Variable Methods (3) 
• EDMS 724 Modern Measurement Theory (3)

Additional elective coursework completes the program. A written comprehensive examination based on the first four courses of the core is required. The Graduate School allows transfer of up to six credits of appropriate prior graduate work. 

Sep 17 Graduate Fair Expo Sep 17, 2024 4:00 – 6:00 pm

• Office of Financial Assistance /
• Financial Aid Process /
• Graduate Students

GRADUATE STUDENTS FINANCIAL AID PROCESS

How to Apply

Learn how to apply for federal loans, eligibility criteria and financing options.

We recommend completing the financial aid process at least two months prior to the semester start date.

Eligibility Criteria

The Office of Financial Assistance reviews requests for financial assistance once a student is registered AND all financial aid applications have been submitted.

To receive financial aid, graduate students are required to meet the following criteria:

• Must be accepted and matriculated into a degree-seeking program
• Remain enrolled on at least a half-time basis
• U.S. Citizen or Eligible Non-Citizen
• Maintain Satisfactory Academic Progress*

*Satisfactory Academic Progress (SAP) is defined as maintaining a 3.0 cumulative GPA and completing the total number of credit hours attempted in an academic year.  SAP is reviewed at the end of each spring semester.  Please take the time to review the full  SAP Policy for Graduate Students.

Complete the FAFSA

• Complete the Student Aid FAFSA
• Villanova School Code: 003388

Review Your Financial Aid Notice

After you have successfully submitted the FAFSA, our office will determine and award your Federal Direct Unsubsidized Loan eligibility for the year.  You will receive an email advising you to log onto your MyNova account to view your offered aid.  

You will need to accept, decline, or modify the loan amounts that have been offfered.  If you are offered loan funding for a semester in which you will not attend, please e-mail to Financial Aid  verifying which semester(s) you will be attending and the number of credits you plan to take per semester, and the total amount of loan funding (not to exceed $20,500) of Federal Direct Unsubsidized Loan funding you wish to accept. We will then be able to make the necessary adjustments and send you a revised aid notice.

Please read the Financial Aid Booklet for Graduate Students , carefully in its entirety for it contains important information regarding eligibility, financing options, etc.

Complete Federal Loan Requirements

If this is the first time you are receiving a Federal Direct Loan at Villanova University as a Graduate Student, you must complete  Entrance Counseling  and  Sign the Master Promissory Note .

Please Note: Your Federal Direct Loan will NOT disburse to your account until BOTH of these requirements are complete.

Financing Options

William d. ford direct student loan program.

The Federal Direct Unsubsidized Stafford Loan is a federal loan borrowed directly from the US Department of Education that you can use for your educational expenses.

• Award: $20,500 per academic year**
• Disbursements: loan funds are disbursed equally among the semesters you attend to your student account at the beginning of each semester
• Lender: US Department of Education
• The interest rate, once established, will be a fixed rate for the life of the loan.
• For All loans 1st disbursed between 7/1/23 through 6/30/24 the fixed interest rate is 7.05%.
• For All loans 1st disbursed between 7/1/24 through 6/30/25 the fixed interest rate is 8.08%.
• The interest accrues once loan funds are disbursed, however principal and interest can be deferred while enrolled on at least a half-time basis in a degree-seeking program.   
• For loans first disbursed on or after 10/1/20 the fee is 1.057%
• Aggregate Loan Limit: $138,500, which includes amounts borrowed as an undergraduate
• Grace period: 6 months after you graduate, leave school, or drop below half-time status
• Repayment period: 10 to 25 years depending on one of the many repayment plans you can select
• Estimated Repayment Calculator    

  **Borrower Based Loan

If you choose to take courses during the summer session and enroll in at least a half-time status, you may be eligible to borrow the Direct Unsubsidized Loan during the summer, which is referred to as a Borrower Based Direct Loan.

**Please note that if the full Borrower Based Direct Unsubsidized Loan is borrowed in the Spring semester, eligibility for the Summer semester would be exhausted.  Students receive an e-mail from the Office of Financial Assistance's Loan Department to confirm their Spring request.

While the maximum amount you can borrow is $20,500, we encourage you to borrow  only the amount that you will need  to finance your program. You will need to indicate the amount of Direct Unsubsidized Loan that you wish to borrow for each semester on the Villanova University Graduate Institutional Financial Aid Application, again noting that the Direct Loan must be certified in equal amounts for each semester. 

Payment Plan

For graduate students, the Villanova Tuition Payment Plan, offered through   Nelnet , allows you to spread your semester's balance out over 2-3 months for a small fee. With this option, you have the freedom to use your money to earn your own interest or investment income while you pay in small installments.

For additional information, please visit My College Payment Plan .

Federal Direct Graduate PLUS Loan

The Federal Direct Graduate PLUS Loans provide graduate students with a viable alternative to private loans in situations where a student's Direct Unsubsidized Loan has not covered all costs. Some basic facts about the Graduate Direct PLUS Loan are:

• Students can borrow up to "cost less aid"
• Student is the borrower - no cosigner required
• Unlimited in-school deferment
• For all loans 1st disbursed between 7/1/23 through 6/30/24 the fixed interest rate is 8.05%.
• For all loans 1st disbursed between 7/1/24 through 6/30/25 the fixed interest rate is 9.08%.
• For all loans 1st disbursed after 10/1/20 the origination fee is 4.228%.
• Deferred payment while enrolled at least half time in a degree seeking program
• No aggregate or annual loan limits
• Based on simplified credit criteria regardless of income or employment status
• Endorser option available
• May reduce high-cost alternative borrowing
• Able to consolidate through the  Direct Consolidation Loan site .

To apply for the Direct Graduate PLUS loan, please follow these two easy steps:

• Go to  https://studentaid.gov  to complete the Direct Graduate PLUS Loan application.
• Complete and electronically sign the MPN at  https://studentaid.gov . Upon approval of the Direct Graduate PLUS application and credit check, our office will be notified and will certify your loan.

*NOTE: Your loan is not complete and will not be certified by the Office of Financial Assistance until ALL of the above steps have been completed.

Private Educational Loan Programs

Private Educational Loans are administered by private lenders and there are a variety of lending institutions that offer these.

The Office of Financial Assistance has selected a group of Preferred Lenders based on quality customer service, borrower benefits, and financing options.  ELM Select  is an external webpage where you will be able to review and compare Villanova University’s Preferred Lender information, and apply for private loans.  However, if you wish to use another lender that is not on this list, you may.  As a borrower, you have the right to select any lender you choose.  You may want to contact the bank, savings and loan, or credit union with whom you do business.

• It is suggested that you start the application process at least four weeks prior to the start of the semester to ensure the funds are disbursed in a timely manner due to numerous disclosures now required by lending institutions.
• Once approved for an alternative loan, the Office of Financial Assistance will review the loan for school certification.  This process includes confirming enrollment, verifying the eligibility for the type of loan requested and the borrowing amount requested.
• Each aid applicant will receive a notice of eligibility from the Office of Financial Assistance.
• Loan funds will be disbursed in accordance with the disbursement schedule once all necessary paperwork has been completed.

The Office of Financial Assistance will continue to meet with lenders on a regular basis to examine new products, services, and benefits for our students to make sure we are providing the best options.

Notes: Due to the numerous disclosures required by the Department of Education, students need to remain in contact with their lender to ensure timely disbursement of funds. Applying for the loan late, or failure to complete required disclosures may delay your funds and possibly cause you to incur University late fees, if your bills are not paid on time.

Click here for more information on the  Office of Financial Assistance Code of Conduct .

Do I need to submit the FAFSA to get the Grad PLUS Loan?

Yes, you must submit the FAFSA in order to apply for and receive the Graduate PLUS Loan.

Can I use my student loans to help pay my rent?

Yes, living expenses such as housing and food are items in the student's cost of attendance and therefore the student can receive loan funding to help cover the costs while enrolled in school.