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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
• Berry Consultants

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  

Is a PhD In Statistics Worth It?

At face value, a statistics PhD seems like a sound career investment, the ticket to higher paying jobs and career growth.

It’s no surprise, then, that one of the most common questions we hear is: Are statistics PhD programs worth it for data science jobs?

If we’re just looking at PhD in statistics salaries, sure, from a purely financial perspective, a PhD might be a good investment in your data science career. There’s a strong financial case you can make for earning one.

But beyond the great statistics PhD salary data, there are many other variables that make the answer a little less clear. When you think about the time commitment - almost 8 years - and the average salaries between master’s and PhD students in statistics, you’ll see that there are a number of trade-offs and that the bump in earnings isn’t so significant as to be a no-brainer.

That’s not to say there aren’t tons of great benefits of a PhD, because there are. For one, a PhD provides much more specialized knowledge, which can help you land competitive, more senior-level jobs. (It’s a preferred qualification for many Google jobs, in fact.) And of course, the average starting salaries for statistics PhDs are very enticing.

To help answer the question, “Is a PhD worth it?” we took a closer look at salaries for data scientists and statistics PhDs, as well as some of the pros and cons of pursuing a PhD for your data science career.

PhD In Stats: Salary Comparison

It’s probably not all that surprising that a PhD can increase your earnings, often by 2X or 3X. That’s really across the board, in all industries. For example, according to the Bureau of Labor Statistics, median weekly pay for a PhD ($1,885) was 45% higher than bachelor’s ($1,305) in 2020.

When you take a closer look at PhDs by field, though, PhDs in math and statistics have some of the best starting salaries in any industry. According to 2019 Survey of Doctorate Recipients data , recipients of a PhD in statistics have an average median starting salary of $140,000 (when pursuing a job in industry). That’s better than business administration, economics, and engineering:

A PhD also results in a pretty big bump in salary compared to just earning a bachelor’s or master’s degree. For instance, median salaries for statistics PhD are two times that of bachelor’s recipients and 1.5 times that of master’s of statistics recipients:

In other words, if you’re looking at the question through a purely financial lens, yes, a PhD in statistics is worth it.

But there’s one caveat. The lifetime earnings of a PhD vs a master’s recipient in statistics isn’t all too significant (on average about $3.6 million vs $3.45 million).

PhD in Stats: The Skills Bump

A big reason why starting salaries are so good for statistics PhDs is that your knowledge will be much more specialized.

Master’s in statistics programs tend to provide broad knowledge in the field. You’ll get a strong foundation of the fundamentals, and become well-versed in many different statistical concepts and methodologies. But you likely won’t get the depth of knowledge that you would from a PhD program.

A PhD differs quite a bit, and these programs are built around research. Here’s how it usually works: After completing initial coursework (usually 2 years), you’ll choose an area to focus your research. And then, you’ll spend 3-5 years researching that topic and preparing a dissertation on it.

The difference in focus, therefore, provides you with very specialized knowledge, and that’s a big reason why starting PhD salaries tend to be so high.

Is It Worth It? Delayed Earnings and Career Goals

Of course, the biggest trade-off in getting all this knowledge is the time commitment. PhD candidates in statistics spend nearly a decade – 7.75 years on average – earning the credential.

And that commitment is something you have to consider to really know if it’s worth it to you. Do you want to make this time commitment and spend the next 8 years researching a topic?

As a master’s recipient, you’ll gain a lot of useful professional skills and can jump right into a career. Sure, you might fully understand advanced statistical methodologies, but you will have a strong grasp of the fundamentals. And you can learn a lot to advance your career with professional development and on-the-job training.

Although they spend a lot of time researching a topic, PhDs do have one advantage: They’re often qualified for more senior-level data science jobs. At Google, for example, a PhD is a preferred qualification for many of their data science jobs, and that’s increasingly true for many FAANG companies.

A PhD Is a Good Investment, But With One Caveat

There’s a lot of reasons why you might consider a PhD in statistics. Salaries, for one, are some of the highest in data science , and job growth for statisticians is about 30% year-over-year. You’ll also have a lot of specialized knowledge that will increase your worth and prepare you for senior-level positions.

But here’s the caveat:

Even if you earn a PhD, you’ll still have a skills gaps that you need to fill, especially if you’re interested in a career in data science. There will skills - like coding or machine learning - that you might need to brush up on.

So if you’re expecting that a PhD is a ticket to a FAANG job, it’s not. But the specialized knowledge that it brings is, increasingly, a preferred qualification.

If you want to read more topics that are similar to this one, consider reading more through our blog where we dive into topics such as our PostgreSQL Interview Questions Guide , Machine Learning Case Studies , and even this article on ‘ Is Data Science a Good Career? ’

Learn and grow more by using resources here at Interview Query !

Doctoral Program

Program summary.

Students are required to

• master the material in the prerequisite courses ;
• pass the first-year core program;
• attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year);
• satisfy the depth and breadth requirements (2nd/3rd/4th year);
• successfully complete the thesis proposal meeting and submit the Dissertation Reading Committee form (winter quarter of the 3rd year);
• present a draft of their dissertation and pass the university oral examination (4th/5th year).

The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.

All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in our PhD handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).

Statistics Department PhD Handbook

All students are expected to abide by the Honor Code and the Fundamental Standard .

Doctoral and Research Advisors

During the first two years of the program, students' academic progress is monitored by the department's Graduate Director. Each student should meet at least once a quarter with the Graduate Director to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.

Qualifying Examinations

Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for Ph.D. Candidacy, a university milestone, by the end of spring quarter of their second year.

While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.

Thesis Proposal Meeting and Dissertation Reading Committee 

The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.

The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member. 

The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.

 For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.

University Oral Examinations

The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.

The Dissertation Reading Committee must also read and approve the thesis.

For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .

Dissertation

The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's dissertation reading committee.

Admission FAQs

• What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
• Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
• What are the required application materials? Specific admission requirements for our programs can be found here .
• Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. GSAS offers a variety of application fee waivers .
• How many students do you admit each year? It varies year to year. We finalize our numbers between December – early February.
• What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
• How many accepted students receive financial aid? All students in the Ph.D. program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. Ph.D. students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes up to $750 of travel expenses for students who make presentations at scientific meetings. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
• Below are my grades and standardized test scores. Can I apply to your program? What are my chances of being admitted? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
• What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
• Is there a minimum GRE requirement? The general GRE exam is waived for the 2023 – 2024 application cycle.
• Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to GSAS.
• Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
• What is the minimum TOEFL or IELTS  requirement? The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
•  I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
• I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from GSAS by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here .
• My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

You may apply concurrently to a program housed at GSAS and to programs housed at other divisions of the University. However, since GSAS does not share application materials with other divisions, you must complete the application requirements for each school.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously?  For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School.  Applicants who attempt to submit more than one GSAS application for the same entry term will be required to withdraw one of the applications.

The Graduate School permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

• This option is only available for fall-term applicants.
• Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
• Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
• An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
• Programs may stop considering second-choice applications at any time during the season; GSAS cannot guarantee that your application will receive a second review.

GSAS Office of Admissions

Columbia University 

107 Low Library, MC 4303

535 West 116th Street 

New York, NY 10027

Academic FAQs

• How many years does it take to pursue a Ph.D. degree in your program? Our students usually graduate in 4‐6 years.
• Can the Ph.D. be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
• What’s the minimum hours/week commitment needed for the successful completion of the Ph.D.? Once a student receives admission, if the student has an external fellowship, then the student will be exempted from teaching responsibilities, for the duration of the fellowship. Students can use their extra time to engage in other activities. However, please note that our program has course enrollment plus research requirements and these cannot be waived.
• One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
• Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
• How do I find out which professors are taking on new students to mentor this year?  Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

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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. 

Fully Funded PhD Programs in Statistics

Last updated May 14, 2022

As part of our series  How to Fully Fund Your PhD , here is a list of universities that fully fund PhD students in Statistics. PhD in Statistics can lead to a variety of careers in consulting, academia, a variety of industries, and more.

“Full funding” is a financial aid package for full-time students that includes full tuition remission as well as an annual stipend or salary during the entire program, which is usually 3-6 years. Funding usually comes with the expectation that students will teach or complete research in their field of study. Not all universities fully fund their doctoral students, which is why researching the financial aid offerings of many different programs, including small and lesser-known schools both in the U.S. and abroad, is essential.

The  ProFellow database  for graduate and doctoral study also spotlights external funding opportunities for graduate school, including dissertation research, fieldwork, language study, and summer work experiences.

Would you like to receive the full list of more than 1000+ fully funded programs in 60 disciplines? Download the FREE Directory of Fully Funded Graduate Programs and Full Funding Awards !

Columbia University, PhD in Statistics

(New York, NY): All students in the Ph.D. program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided.

Ohio State University, PhD in Statistics

(Columbus, OH): Students who are offered the funding at the time of admission either via a Fellowship or Graduate Teaching Associateship are typically guaranteed funding through the duration of their program (up to five years if needed for a Ph.D. student or two years for a master’s student) provided that the student continues to make appropriate progress toward the degree and carries out assigned duties satisfactorily.

Stanford University, PhD in Statistics

(Stanford, CA): Students accepted to the Ph.D. program are offered financial support. All tuition expenses are paid and there is a fixed monthly stipend determined to be sufficient to pay living expenses. Financial support can be continued for five years, department resources permitting, for students in good standing.

University of Chicago, PhD in Statistics

(Chicago, IL): In recent years our department has been able to provide full support (tuition, most fees, health insurance, and a stipend) for most of its Ph.D. students, and we expect to do so for the foreseeable future. Ordinarily, students are supported for at least four years. Support is not tied to working with a particular faculty member. At present, most fifth-year students receive full support, and most Ph.D. students receive summer support.

University of Nevada, Reno, PhD in Statistics and Data Science

(Reno, NV): All students accepted to the Statistics and Data Science Ph.D. program receive an annual stipend of $17,000, a tuition waiver, and a subsidized medical plan. Students may also pursue departmental and University-wide scholarships.

University of Texas at Austin, PhD in Statistics

(Austin, TX): It is our intention that each PhD Statistics student will be fully financially supported for four academic years, the duration of his/her program of study. There are in general three types of support: academic employment, graduate fellowships, and grants.

University of Texas at San Antonio, PhD in Applied Statistics

(San Antonio, TX): Full-time students admitted to the Ph.D. program are usually awarded fellowships that include a waiver of tuition, a stipend to help cover living expenses, and some health care benefits. The stipend is likely to vary but could be in an amount up to $25,000 annually.

Duke University, PhD in Statistical Science

(Durham, NC): About half of the financial aid specified in your acceptance letter will be given to you without you having to do anything except maintain good academic standing. The other half is contingent upon you being a teaching assistant (TA) or research assistant (RA) within the department.

Need some tips for the application process? See my article  How To Get Into a Fully Funded PhD Program: Contacting Potential PhD Advisors .

Also, sign up to discover and bookmark more than 1800 professional and academic fellowships in the  ProFellow database .

© Victoria Johnson 2020, all rights reserved.

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Gen X, millennials face higher risk of 17 cancers than older generations

Younger generations have a heightened risk of some cancers, new research found. 

A study published Wednesday in Lancet Public Health found that Gen X and millennials are more likely to be diagnosed with 17 types of cancer, including nine that had been declining in older adults. Researchers aren’t sure why, but say obesity is likely a leading cause.

“What is happening in these generations can be considered a bellwether for future cancer trends,” said Hyuna Sung, a cancer epidemiologist at the American Cancer Society, who led the research. 

Rates of colorectal cancer — one of the 17 types — have been rising among younger people for decades, a troubling trend that sparked investigation into other types of cancer. 

Sung and her colleagues used cancer diagnosis and mortality data from two databases –– the North American Association of Central Cancer Registries and the U.S. National Center for Health Statistics –– to analyze cancer trends in people born between 1920 and 1990, who were diagnosed with cancer between 2000 and 2019. 

The data included 34 types of cancer, nearly 24 million diagnoses and more than 7 million deaths. To get a better view of how cancer diagnoses and mortality rates changed in groups of people born around the same year — called a birth cohort — the researchers grouped people by birth year in five-year intervals. For example, people born in 1920 through 1924 were all one birth cohort. 

Seventeen of the 34 cancers had increasing incidence in younger people. The risk was two to three times higher in people born in 1990 for pancreatic, kidney and small intestine cancers, compared to people born in 1955. Liver cancer diagnoses in women followed the same pattern. 

“The most important thing it tells us is there is something that changed for the group of individuals born after this period of time. They have been exposed to some environmental or lifestyle factor that is leading to this shift,” said Dr. Andrea Cercek, a gastrointestinal medical oncologist at the Memorial Sloan Kettering Cancer Center, who was not involved with the research. 

After declining for decades, these types of cancers ​​have begun to climb again:

• Endometrial
• Non-cardia gastric
• Gallbladder
• Estrogen-receptor positive breast cancers
• HIV-linked cancer called Kaposi sarcoma 

While the study found that mortality declined or was stable in younger generations for most cancers, mortality rate increased among younger age groups for endometrial, intrahepatic bile duct, gallbladder, colorectal and testicular cancers, as well as liver cancer among women. 

Endometrial cancer was the fastest growing for both diagnoses and mortality.

“That was a sobering finding,” said Sung. “Although many cancer rates are rising, we don’t necessarily see this increase in mortality because we are treating them a lot better than before.”

Many of the cancers found to be on the rise are still rare in young people and, while rates have increased, the overall number of cases is comparatively low. 

“It is clearly happening. Almost all the oncologists I know of can say they see it,” Brawley said, adding that despite the alarming increases, it’s important to keep in mind that most cancer diagnoses still happen in people older than 50 years. 

“In the 1990s, 10% of people diagnosed with colon cancer were under age 50. Now it’s 20%, but we should not forget the 80% that are still over the age of 50,” he said. 

Looking at people born within a specific time period can give important clues into why certain types of cancer are rising among younger generations. 

“All of these cancers are linked to the obesity epidemic. We know that’s the second-leading cause of cancer right now, behind tobacco use,” said Dr. Otis Brawley, Bloomberg Distinguished Professor of Oncology and Epidemiology Johns Hopkins University, who was not involved with the new study. 

About 20% of cancer diagnoses in the U.S. are linked to excess body weight, according to the American Cancer Society . Obesity rates in the nation changed little in the 1960s and 1970s but increased sharply after that. About 13% of adults had obesity in 1980, compared to 34% in 2008, according to data from the Surgeon General . 

Among children, obesity rates grew from 5% to 17% in the same period. Today, more than 40% of American adults and about 20% of children and adolescents are obese, the Centers for Disease Control and Prevention reports . 

If obesity is a culprit, it’s likely one of several lifestyle and environmental factors that is leading to the rise. Other factors could be more sedentary behavior or something in the food or the water, common medications or chemical exposures or chemical agents, the experts said.

The overuse of antibiotics is another possible link under scrutiny. Antibiotics are known to change the gut microbiome, which has been linked to colorectal cancer. While antibiotics are needed to treat many bacterial infections, they’re often misused and taken for issues that are not bacterial, or that do not require antibiotics.  

“The list of things we are potentially investigating is very long,” Cercek said. “Antibiotics are one of the top culprits on the list.”

Researchers still don’t understand what’s behind the rise in certain types of cancers among younger generations. Although obesity and antibiotics are primary suspects, “we can’t rule out other chemical exposures or chemical agents,” Brawley said.

Kaitlin Sullivan is a contributor for NBCNews.com who has worked with NBC News Investigations. She reports on health, science and the environment and is a graduate of the Craig Newmark Graduate School of Journalism at City University of New York.

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What are the benefits of getting a PhD in statistics over a MS in statistics?

What are the benefits of getting a PhD in statistics over a MS in statistics (other than being a professor)? Do people with PhDs in statistics earn significantly more than people with MS degrees in statistics?

More generally, does a PhD in a quantitative field provide a salary advantage over a MS in a quantitative field?

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• 2 What do you want to do with your statistics degree, apart from earn money? –  Dave Clarke Commented Aug 21, 2012 at 15:53
• @Dave Clarke: Work on practical and relevant problems. –  james tones Commented Aug 21, 2012 at 15:57
• Possibly of interest is this thread on the Stats SE (also possible crosspost) –  Macro Commented Aug 21, 2012 at 16:28
• Related: To work in statistics for industry and research centers, is a masters sufficient or is there a major advantage to having a PhD? –  Stephan Kolassa Commented Jan 8, 2015 at 15:36

2 Answers 2

Actually, this greatly depends on where you get your degree from. Sometimes people who have done just MS are able to get to the bottom of the thing and can use their knowledge of statistics on a practical level. It depends on how solid your concepts are. Once you are in some position earned through your sound academic record you can grow quite fast.

So, if you have good record (not just grades but grasp), you may be able to get to the same level of salary as a PhD would. This is because statistics is an applied branch and is in demand.

Well, there's something different to consider. As a MS student you pay to study. As a PhD, you are paid to study. I know that depends between countries but where I am, a MS is terribly expensive.

The only reasons I see to do a MS instead of a PhD is when someone doesn't have good enough grades to get into a PhD or because they want to shift their area a lot: for example, a chemist taking a master in Biochemistry because he wants a PhD in Cell biology.

• They are different tracks and pursuing one doesn't mean you failed or would fail at the other. You have to pay for an MS. But, money shouldn't be the motivation for a PhD. An MS, from my understanding, earns a comparable salary in industry and most people in industry earn more than academics. Well, "earn" in some ways. :-) –  mac389 Commented Aug 25, 2012 at 1:03
• Yes. Doing a PhD for money is bound to be terrible experience indeed ;) But someone that goes to university already has some passion for learning (or should) and his question suggests that money is important at the moment. About the "earning", it's no good earning more money earlier, but start in debt. Assuming that he can get the same position after doing a MS or a PHD, unless what he earns in the 2 years difference between MS and PhD minus the cost of the MS, is more than what he gains as PhD, he still loses money in the end. And that's assuming that he can maintain a debt. –  carandraug Commented Aug 25, 2012 at 1:18
• Perhaps my cynical bias and personal experience makes me think that attending college is not tightly correlated with intellectual curiosity. I imagine, then, that working in industry with an MS is the best financial option. –  mac389 Commented Aug 25, 2012 at 1:22

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Anyone with MD/PhD with PhD in biostatistics / statistics / mathematics?

• Thread starter cdpiano27
• Start date Feb 18, 2008

Senior Member

• Feb 18, 2008

I would agree with you. http://en.wikipedia.org/wiki/National_Institute_of_Biomedical_Imaging_and_Bioengineering Signed into law by Bill Clinton. How could you not like it ?  

Full Member

• Sep 21, 2011

pantouka said: I am a college pre-med sophomore interested in MAYBE going the MD/PHD route with an emphasis in biostatistics, but I'm having difficulty imagining exactly what the job would be like with the intersection of the two degrees. Click to expand...

pantouka said: 1) Would an MD/PHD-stats person have a huge role in running clinical trials? Would they ever be involved in analysis? Click to expand...

pantouka said: I2) How would the PHD portion of the joint degree be different from an MPH joint degree in practice? Click to expand...

pantouka said: I3) Are there any fields of medicine better suited for a stats PHD? I can honestly see relevance in disciplines ranging from psychiatry to infectious diseases. Click to expand...

I'm planning to pursue mathematics/statistics for my PhD and future career. There aren't many of us--maybe one or two graduate with it a year in the US... 1) Would an MD/PHD-stats person have a huge role in running clinical trials? Would they ever be involved in analysis? Yes, I'm planning to be involved in analyses and study design in clinical trials, as well as basic science research (hopefully genomics and population health). 2) How would the PHD portion of the joint degree be different from an MPH joint degree in practice? The MPH will not teach you enough statistics to do your own analyses very easily. Typically, MPH students only learn analysis up to multiple regression. The emphasis is public health, not statistics. In statistics/mathematics, you'll learn how to create new ways to analyse data that you can test in clinical trials and basic science research (probability theory, generalized linear models, network-based methodology...). An MS in statistics would probably give you a good overview, though. 3) Are there any fields of medicine better suited for a stats PHD? I can honestly see relevance in disciplines ranging from psychiatry to infectious diseases. Genetics, public health, and neuroscience are big areas in stats these days, so specialties relating to these areas of research might be a good starting point. I'm planning on a career in academics and government with medical service work abroad. A few things to consider in undergrad: -Most stats PhD programs require 3 semesters of calculus, linear algebra, several statistics courses (preferably with calculus), and a probability course. -You'll probably need the GRE in addition to the MCAT (not GRE math subject exam, though). GRE math should be substantially over 700. -There aren't a lot of schools offering this. U Minnesota, MUSC, U Miami, U Florida, UIC MSP, Stanford, and U of Chicago were the ones I found offering math/stats options. Bioinformatics is also a good option that's offered at more schools...  

what exactly is the difference between bioinformatics and biostatistics?  

• Sep 22, 2011

tortuga87 said: what exactly is the difference between bioinformatics and biostatistics? Click to expand...

Bioinformatics uses computer science to gather data in biology (genomics technology, algorithms to compute things for statisticians). Biostatistics is the application and development of new methods of data analysis from the field of mathematics (usually have someone in computer science to help you write the programs you need to analyze data by the new method).  

It seems like: biostatician + learning some programming > bioinformatician because you can develop more elegant methods with biostatistics?  

• Sep 23, 2011

tortuga87 said: It seems like: biostatician + learning some programming > bioinformatician because you can develop more elegant methods with biostatistics? Click to expand...

Biostaticians develop tools for analyzing data. Bioinformaticians develop tools for gathering data. Generally people in one will have some grounding in the other, but it's silly to say one is better than the other...  

I'm currently a 2nd year MSPH in Biostatistics who deferred medical school to complete my masters degree. So here are the things that I've learned that I think would be most helpful: 1. Courses The main difference between MSPH/MPH and PhD courses is that the masters degree will ultimately be in public health, and biostats is just a concentration. Therefore, as a masters student you will be required to take public health courses such as epidemiology, behavioral science and health education, health policy management, and environmental sciences. For PhD students, these courses are optional although most choose to take these anyways. In terms of biostat courses, masters and PhD students have the same coursework the first year, with the PhD student also having to take an additional class to prepare them to be teaching assistants in the following years. The second year, PhD students go on to take more advanced statistical and probability theory classes. They may also be required as part of their stipend package to TA introductory biostat classes. 2. Thesis The masters thesis takes about a semester and a half to complete, and is significantly less involved than a PhD dissertation. The masters thesis is mainly explanation and application of a statistical model to a data set, whereas a PhD dissertation is expected to introduce new statistical theory and/or methods to a very specific area of research. Starting from about the third year of the PhD program, the students start to devote the majority of their time to working on their research and holding outside jobs. 3. Jobs Overall, masters programs have more of an emphasis on application and practical experience, and PhD programs have more of an emphasis on theory. Jobs that prefer masters degrees to PhD degrees are the ones that need someone who is more involved in the entire research process and study design. This often requires the biostatistician to physically go into, for example, a hospital and monitor the accuracy of which data is being collected. PhD-level biostatisticians would be over-qualified to work in this type of role, so they serve mostly as the lead data analyst. Outside of academia, PhD degrees are more often hired by biostat consulting firms and pharmaceutical companies to handle complex data analysis. All in all, if you want to be involved in the actual study and not just the data analysis, masters degrees are probably the way to go. 4. MD/MPH or MD/PhD When you're a biostatistician analyzing data, its always helpful, if not necessary, to understand the context of the problem you're working with. If you start working on a study about degenerative lumbar spine diseases, it would be really helpful if you already have working knowledge about the spine going into the project. However, if you don't have that knowledge, you can always do the background research yourself, although it takes a lot more energy and work. In this case, having an MD would definitely be a leg up. Also, if you hold an additional MD degree, you are most likely the one that is asking the research questions rather than working on other people's trials. This is a HUGE advantage because understanding your research backwards and forwards means that you are able to look at your data from multiple perspectives. For example, as a clinician you may know that certain biological mechanisms can affect your outcome and you actually know the statistical methods that allow you to identify and analyze those variables. On the other hand, a stastician without such an in-depth background in medicine and/or biology would not be able to reach the same conclusions. 5. Future Many studies have pointed out that PhD programs don't place enough of an emphasis on application of theory. Case in point: I am currently working on a study that has a lot of missing data due to patients not coming in for follow-up. To perform an accurate analysis, I need to know how to account for this missing data. I go a PhD-level professor who gives me literature to read on some theories on how the missing data might affect my conclusions. I go to a masters-level professor who tells me exactly what to do with my data to get a more accurate conclusion. As the field evolves, biostatisticians need to have both a firm grasp of theory as well as how to apply it in real-life situations. I hope all this helps. As you can tell, I'm very passionate about biostats and I really do wish there were more people that are interested in this field. Whatever path you decide, even if you decide to just pursue a masters degree, there is such a demand for biostats that your future will be bright now matter what.  

• Sep 26, 2011

I personally am not getting a PhD in stats, but I did work for an MD/PhD when I was an undergrad who was a dermatologist whose research was skin cancer epidemiology. He was one of the archetypal 75/20/5 guys who spent most of his time doing research, two half days a week in general derm clinic, and taught a few lectures a semester. He was heavily involved in clinical trials and evaluating public health programs for skin cancer.  

prolixity29

• Feb 20, 2012

Bump. I have a few questions regarding pursuing an MD/PhD in biostatistics... Really look forward to hearing your responses. I am currently pursuing my MSPH in Biostatistics in the Philippines (though I am from the US). After finishing my degree, I will attend medical school in the US. Originally, I had decided to study biostatistics out-of-interest and because I received a scholarship for my glide year, but I am truly enjoying the theoretical coursework and am now considering studying biostatistics when I enroll in medical school. The applied problem areas also interest me deeply, particularly genomics and environmental health. Since I will already have a master's degree by the time I matriculate into med school, it seems that the next logical step would be to shoot for a PhD. The problem is though that I am a non-traditional student in my late 20's. I want to have a family and need to start making an income and this would prolong my non-income-generating years... My eventual career goal in getting an MD/PhD would be either working/researching biostatistics while practicing in an academic hospital or consulting for industry (e.g. pharma or biotech) while practicing. Questions: 1) Is pursuing an MD/PhD at my age impractical? 2) Can you do a fellowship in biostatistics that would get you to the same place as an MD/PhD? I have tried searching for "biostatistics fellowships" and the ones I have seen are for post-docs. 3) Assuming I did pursue a PhD, would I be able to bypass the master's level courses given my MSPH? If so and assuming that I work diligently and do not burn out, what is a reasonable estimate for the number of years I could shave off the dual degree? Could it be done in 6 years? Thanks so much for your responses.  

• Feb 21, 2012

prolixity29 said: Bump. I have a few questions regarding pursuing an MD/PhD in biostatistics... Really look forward to hearing your responses. I am currently pursuing my MSPH in Biostatistics in the Philippines (though I am from the US). After finishing my degree, I will attend medical school in the US. Originally, I had decided to study biostatistics out-of-interest and because I received a scholarship for my glide year, but I am truly enjoying the theoretical coursework and am now considering studying biostatistics when I enroll in medical school. The applied problem areas also interest me deeply, particularly genomics and environmental health. Since I will already have a master's degree by the time I matriculate into med school, it seems that the next logical step would be to shoot for a PhD. The problem is though that I am a non-traditional student in my late 20's. I want to have a family and need to start making an income and this would prolong my non-income-generating years... My eventual career goal in getting an MD/PhD would be either working/researching biostatistics while practicing in an academic hospital or consulting for industry (e.g. pharma or biotech) while practicing. Questions: 1) Is pursuing an MD/PhD at my age impractical? 2) Can you do a fellowship in biostatistics that would get you to the same place as an MD/PhD? I have tried searching for "biostatistics fellowships" and the ones I have seen are for post-docs. 3) Assuming I did pursue a PhD, would I be able to bypass the master's level courses given my MSPH? If so and assuming that I work diligently and do not burn out, what is a reasonable estimate for the number of years I could shave off the dual degree? Could it be done in 6 years? Thanks so much for your responses. Click to expand...

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It’s only a matter of weeks before BU students start returning to campus. Where has the summer gone? July marked the official arrival of BU’s new president, Melissa L. Gilliam, who spent much of her first weeks here touring the campus and meeting with faculty, staff, and students. The University also announced a new dean of Wheelock. And there was a surprise visit by a couple of local turkeys, who spent a hot July day standing sentry outside the George Sherman Union.

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Things to consider about masters programs in statistics

It is admission season for graduate schools. I (and many students like me) am now trying to decide which statistics program to pick.

• What are some things those of you who work with statistics suggest we consider about masters programs in statistics?
• Are there common pitfalls or mistakes students make (perhaps with regard to school reputation)?
• For employment, should we look to focus on applied statistics or a mix of applied and theoretical statistics?

Edit: Here is some additional information about my personal situation: All of the programs I am now considering are in the United States. Some focus on the more applied side and give masters degrees in "applied statistics" while others have more theoretical coursework and grant degrees in "statistics". I'm personally not that intent on working in one industry over another. I have some programming background and know the tech industry a little better than, say, the genomics or bioinformatics industry. However, I'm primarily looking for a career with interesting problems.

Edit : Tried to make the question more generally applicable.

• machine-learning
• mathematical-statistics
• 8 $\begingroup$ This depends very much on a lot of personal factors, making it hard to give good advice. We don't know what part of the world your programs are from, how focused your interests already are or what they are. The question is stated too broadly to be answered authoritatively, but would be at risk of being closed as too localized if it was geared solely toward giving advice to just one person. I suggest providing some more context, but not making it specific only to your particular case. $\endgroup$ –  cardinal Commented Apr 2, 2012 at 17:42
• 1 $\begingroup$ Fair enough. All the programs I am now considering are in the United States. Some focus on the more applied side and give masters degrees in "applied statistics" while others have more theoretical coursework and grant degrees in "statistics". I'm personally not that intent on working in one industry over another. I have some programming background and know the tech industry a little better than say the genomics or bioinformatics industry. However, I'm primarily looking for a career with interesting problems. $\endgroup$ –  AttemptedStudent Commented Apr 2, 2012 at 17:53
• $\begingroup$ Thank you. That is very helpful. I still think community wiki would be best, but this makes it possible for there to be a more productive conversation here. (deleting my previous comment.. ) $\endgroup$ –  gung - Reinstate Monica Commented Apr 2, 2012 at 22:25

3 Answers 3

Here is a somewhat blunt set of general thoughts and recommendations on masters programs in statistics. I don't intend for them to be polemic, though some of them may sound like that.

I am going to assume that you are interested in a terminal masters degree to later go into industry and are not interested in potentially pursuing a doctorate. Please do not take this reply as authoritative, though.

Below are several points of advice from my own experiences. I've ordered them very roughly from what I think is most important to least. As you choose a program, you might weigh each of them against one another taking some of the points below into account.

Try to make the best choice for you personally . There are very many factors involved in such a decision: geography, personal relationships, job and networking opportunities, coursework, costs of education and living, etc. The most important thing is to weigh each of these yourself and try to use your own best judgment. You are the one that ultimately lives with the consequences of your choice, both positive and negative, and you are the only one in a position to appraise your whole situation. Act accordingly.

Learn to collaborate and manage your time . You may not believe me, but an employer will very likely care more about your personality, ability to collaborate with others and ability to work efficiently than they will care about your raw technical skills. Effective communication is crucial in statistics, especially when communicating with nonstatisticians. Knowing how to manage a complex project and make steady progress is very important. Take advantage of structured statistical-consulting opportunities, if they exist, at your chosen institution.

Learn a cognate area . The greatest weakness I see in many masters and PhD graduates in statistics, both in industry and in academia, is that they often have very little subject-matter knowledge. The upshot is that sometimes "standard" statistical analyses get used due to a lack of understanding of the underlying mechanisms of the problem they are trying to analyze. Developing some expertise in a cognate area can, therefore, be very enriching both statistically and professionally. But, the most important aspect of this is the learning itself: Realizing that incorporating subject matter knowledge can be vital to correctly analyzing a problem. Being competent in the vocabulary and basic knowledge can also aid greatly in communication and will improve the perception that your nonstatistician colleagues have of you.

Learn to work with (big) data . Data sets in virtually every field that uses statistics have been growing tremendously in size over the last 20 years. In an industrial setting, you will likely spend more time manipulating data than you will analyzing them. Learning good data-management procedures, sanity checking, etc. is crucial to valid analysis. The more efficient you become at it, the more time you'll spend doing the "fun" stuff. This is something that is very heavily underemphasized and underappreciated in academic programs. Luckily, there are now some bigger data sets available to the academic community that one can play with. If you can't do this within the program itself, spend some time doing so outside of it.

Learn linear regression and the associated applied linear algebra very, very well . It is surprising how many masters and PhD graduates obtain their degrees (from "top" programs!), but can't answer basic questions on linear regression or how it works. Having this material down cold will serve you incredibly well. It is important in its own right and is the gateway to many, many more advanced statistical and machine-learning techniques.

If possible, do a masters report or thesis . The masters programs associated with some of the top U.S. statistics departments (usually gauged more on their doctorate programs) seem to have moved away from incorporating a report or a thesis. The fact of the matter is that a purely course-based program usually deprives the student of developing any real depth of knowledge in a particular area. The area itself is not so important, in my view, but the experience is. The persistence, time-management, collaboration with faculty, etc. required to produce a masters report or thesis can pay off greatly when transitioning to industry. Even if a program doesn't advertise one, if you're otherwise interested in it, send an email to the admissions chair and ask about the possibility of a customized program that allows for it.

Take the most challenging coursework you can manage . While the most important thing is to understand the core material very, very well, you should also use your time and money wisely by challenging yourself as much as possible. The particular topic matter you choose to learn may appear to be fairly "useless", but getting some contact with the literature and challenging yourself to learn something new and difficult will make it easier when you have to do so later in industry. For example, learning some of the theory behind classical statistics turns out to be fairly useless in and of itself for the daily work of many industrial statisticians, but the concepts conveyed are extremely useful and provide continual guidance. It also will make all the other statistical methods you come into contact with seem less mysterious.

A program's reputation only matters for your first job . Way too much emphasis is put on a school's or program's reputation. Unfortunately, this is a time- and energy-saving heuristic for human-resource managers. Be aware that programs are judged much more by their research and doctoral programs than their masters ones. In many such top departments, the M.S. students often end up feeling a bit like second-class citizens since most of the resources are expended on the doctoral programs.

One of the brightest young statistical collaborators I've worked with has a doctorate from a small foreign university you've probably never heard of. People can get a wonderful education (sometimes a much better one, especially at the undergraduate and masters level!) at "no-name" institutions than at "top" programs. They're almost guaranteed to get more interaction with core faculty at the former.

The name of the school at the top of your resume is likely to have a role in getting you in the door for your first job and people will care more about where your most advanced degree came from than where any others did. After that first job, people will care substantially more about what experience you bring to the table. Finding a school where lots of interesting job opportunities come to you through career fairs, circulated emails, etc., can have a big payoff and this happens more at top programs.

A personal remark : I personally have a preference for somewhat more theoretical programs that still allow some contact with data and a smattering of applied courses. The fact of the matter is that you're simply not going to become a good applied statistician by obtaining a masters degree. That comes only with (much more) time and experience in struggling with challenging problems and analyses on a daily basis.

• 14 $\begingroup$ +1. Sometimes, as here, a good answer makes a question worth keeping. $\endgroup$ –  whuber ♦ Commented Apr 2, 2012 at 20:31
• 4 $\begingroup$ I know this is a very individual decision. However, your thoughtful reply helps a lot. It is particularly interesting to see how highly you ranked learning a cognate area. Some programs allow me to take courses in other departments. I'm now starting to think that breadth is a particularly valuable characteristic of the program. $\endgroup$ –  AttemptedStudent Commented Apr 2, 2012 at 20:51
• $\begingroup$ (+1) Very nice response. I particularly liked Point 3. $\endgroup$ –  chl Commented Apr 2, 2012 at 21:55
• 2 $\begingroup$ @AttemptedStudent: Traditionally, I think most graduate students (PhD, in particular) in statistics have undergraduate math backgrounds and have had little contact with actual applied problems that require statistical concepts and thinking. That may be part of the reason learning a cognate area ended up so high on my list. But, as I mentioned in the body, the ordering is a bit rough. :) $\endgroup$ –  cardinal Commented Apr 4, 2012 at 14:01
• 1 $\begingroup$ +1, nice answer. I liked points 3-5. Observation on data manipulation is spot on. $\endgroup$ –  mpiktas Commented Apr 4, 2012 at 14:03

I would advise to either get in the best school possible with a brand name (like MIT), or the best overall deal (e.g. a decent public school with in-state tuition). I would not waste money on second rate private schools.

The brand name schools payoff. The price difference between a school like MIT and second tier schools like GWU is not big enough to justify the difference in the brand power.

On the other hand, some public schools, e.g. William and Mary, while being dirt cheap offer decent education. Some of them even have comparable brand power, e.g. Berkeley vs. Stanford. Thus due to the significant cost difference, they're an alternative to best private schools.

Take a look at Pharmacoepidemiology. In particular as it relates to Drug safety. This is a very new area of research with a lots of very interested questions.

• 1 $\begingroup$ Was this accidentally posted in the wrong place? $\endgroup$ –  Macro Commented May 14, 2012 at 22:28

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[Q] Those of you who didn't become professors, was a phd in Statistics worth it at the end?

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School of Engineering welcomes new faculty

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The School of Engineering welcomes 15 new faculty members across six of its academic departments. This new cohort of faculty members, who have either recently started their roles at MIT or will start within the next year, conduct research across a diverse range of disciplines.

Many of these new faculty specialize in research that intersects with multiple fields. In addition to positions in the School of Engineering, a number of these faculty have positions at other units across MIT. Faculty with appointments in the Department of Electrical Engineering and Computer Science (EECS) report into both the School of Engineering and the MIT Stephen A. Schwarzman College of Computing. This year, new faculty also have joint appointments between the School of Engineering and the School of Humanities, Arts, and Social Sciences and the School of Science.

“I am delighted to welcome this cohort of talented new faculty to the School of Engineering,” says Anantha Chandrakasan, chief innovation and strategy officer, dean of engineering, and Vannevar Bush Professor of Electrical Engineering and Computer Science. “I am particularly struck by the interdisciplinary approach many of these new faculty take in their research. They are working in areas that are poised to have tremendous impact. I look forward to seeing them grow as researchers and educators.”

The new engineering faculty include:

Stephen Bates joined the Department of Electrical Engineering and Computer Science as an assistant professor in September 2023. He is also a member of the Laboratory for Information and Decision Systems (LIDS). Bates uses data and AI for reliable decision-making in the presence of uncertainty. In particular, he develops tools for statistical inference with AI models, data impacted by strategic behavior, and settings with distribution shift. Bates also works on applications in life sciences and sustainability. He previously worked as a postdoc in the Statistics and EECS departments at the University of California at Berkeley (UC Berkeley). Bates received a BS in statistics and mathematics at Harvard University and a PhD from Stanford University.

Abigail Bodner joined the Department of EECS and Department of Earth, Atmospheric and Planetary Sciences as an assistant professor in January. She is also a member of the LIDS. Bodner’s research interests span climate, physical oceanography, geophysical fluid dynamics, and turbulence. Previously, she worked as a Simons Junior Fellow at the Courant Institute of Mathematical Sciences at New York University. Bodner received her BS in geophysics and mathematics and MS in geophysics from Tel Aviv University, and her SM in applied mathematics and PhD from Brown University.

Andreea Bobu ’17 will join the Department of Aeronautics and Astronautics as an assistant professor in July. Her research sits at the intersection of robotics, mathematical human modeling, and deep learning. Previously, she was a research scientist at the Boston Dynamics AI Institute, focusing on how robots and humans can efficiently arrive at shared representations of their tasks for more seamless and reliable interactions. Bobu earned a BS in computer science and engineering from MIT and a PhD in electrical engineering and computer science from UC Berkeley.

Suraj Cheema will join the Department of Materials Science and Engineering, with a joint appointment in the Department of EECS, as an assistant professor in July. His research explores atomic-scale engineering of electronic materials to tackle challenges related to energy consumption, storage, and generation, aiming for more sustainable microelectronics. This spans computing and energy technologies via integrated ferroelectric devices. He previously worked as a postdoc at UC Berkeley. Cheema earned a BS in applied physics and applied mathematics from Columbia University and a PhD in materials science and engineering from UC Berkeley.

Samantha Coday joins the Department of EECS as an assistant professor in July. She will also be a member of the MIT Research Laboratory of Electronics. Her research interests include ultra-dense power converters enabling renewable energy integration, hybrid electric aircraft and future space exploration. To enable high-performance converters for these critical applications her research focuses on the optimization, design, and control of hybrid switched-capacitor converters. Coday earned a BS in electrical engineering and mathematics from Southern Methodist University and an MS and a PhD in electrical engineering and computer science from UC Berkeley.

Mitchell Gordon will join the Department of EECS as an assistant professor in July. He will also be a member of the MIT Computer Science and Artificial Intelligence Laboratory. In his research, Gordon designs interactive systems and evaluation approaches that bridge principles of human-computer interaction with the realities of machine learning. He currently works as a postdoc at the University of Washington. Gordon received a BS from the University of Rochester, and MS and PhD from Stanford University, all in computer science.

Kaiming He joined the Department of EECS as an associate professor in February. He will also be a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research interests cover a wide range of topics in computer vision and deep learning. He is currently focused on building computer models that can learn representations and develop intelligence from and for the complex world. Long term, he hopes to augment human intelligence with improved artificial intelligence. Before joining MIT, He was a research scientist at Facebook AI. He earned a BS from Tsinghua University and a PhD from the Chinese University of Hong Kong.

Anna Huang SM ’08 will join the departments of EECS and Music and Theater Arts as assistant professor in September. She will help develop graduate programming focused on music technology. Previously, she spent eight years with Magenta at Google Brain and DeepMind, spearheading efforts in generative modeling, reinforcement learning, and human-computer interaction to support human-AI partnerships in music-making. She is the creator of Music Transformer and Coconet (which powered the Bach Google Doodle). She was a judge and organizer for the AI Song Contest. Anna holds a Canada CIFAR AI Chair at Mila, a BM in music composition, and BS in computer science from the University of Southern California, an MS from the MIT Media Lab, and a PhD from Harvard University.

Yael Kalai PhD ’06 will join the Department of EECS as a professor in September. She is also a member of CSAIL. Her research interests include cryptography, the theory of computation, and security and privacy. Kalai currently focuses on both the theoretical and real-world applications of cryptography, including work on succinct and easily verifiable non-interactive proofs. She received her bachelor’s degree from the Hebrew University of Jerusalem, a master’s degree at the Weizmann Institute of Science, and a PhD from MIT.

Sendhil Mullainathan will join the departments of EECS and Economics as a professor in July. His research uses machine learning to understand complex problems in human behavior, social policy, and medicine. Previously, Mullainathan spent five years at MIT before joining the faculty at Harvard in 2004, and then the University of Chicago in 2018. He received his BA in computer science, mathematics, and economics from Cornell University and his PhD from Harvard University.

Alex Rives  will join the Department of EECS as an assistant professor in September, with a core membership in the Broad Institute of MIT and Harvard. In his research, Rives is focused on AI for scientific understanding, discovery, and design for biology. Rives worked with Meta as a New York University graduate student, where he founded and led the Evolutionary Scale Modeling team that developed large language models for proteins. Rives received his BS in philosophy and biology from Yale University and is completing his PhD in computer science at NYU.

Sungho Shin will join the Department of Chemical Engineering as an assistant professor in July. His research interests include control theory, optimization algorithms, high-performance computing, and their applications to decision-making in complex systems, such as energy infrastructures. Shin is a postdoc at the Mathematics and Computer Science Division at Argonne National Laboratory. He received a BS in mathematics and chemical engineering from Seoul National University and a PhD in chemical engineering from the University of Wisconsin-Madison.

Jessica Stark joined the Department of Biological Engineering as an assistant professor in January. In her research, Stark is developing technologies to realize the largely untapped potential of cell-surface sugars, called glycans, for immunological discovery and immunotherapy. Previously, Stark was an American Cancer Society postdoc at Stanford University. She earned a BS in chemical and biomolecular engineering from Cornell University and a PhD in chemical and biological engineering at Northwestern University.

Thomas John “T.J.” Wallin joined the Department of Materials Science and Engineering as an assistant professor in January. As a researcher, Wallin’s interests lay in advanced manufacturing of functional soft matter, with an emphasis on soft wearable technologies and their applications in human-computer interfaces. Previously, he was a research scientist at Meta’s Reality Labs Research working in their haptic interaction team. Wallin earned a BS in physics and chemistry from the College of William and Mary, and an MS and PhD in materials science and engineering from Cornell University.

Gioele Zardini joined the Department of Civil and Environmental Engineering as an assistant professor in September. He will also join LIDS and the Institute for Data, Systems, and Society. Driven by societal challenges, Zardini’s research interests include the co-design of sociotechnical systems, compositionality in engineering, applied category theory, decision and control, optimization, and game theory, with society-critical applications to intelligent transportation systems, autonomy, and complex networks and infrastructures. He received his BS, MS, and PhD in mechanical engineering with a focus on robotics, systems, and control from ETH Zurich, and spent time at MIT, Stanford University, and Motional.

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