phd statistics reddit

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Should I pursue a PhD in Statistics? [closed]

I am trying to decide whether or not I should take a PhD program in Statistics. I am not inclined to get a position at a university after my PhD, my goal is rather to get hired by Contract Research Organizations involved in Clinical Trials, either as a Biostatistician or Statistical Programmer. At the moment, I like working with SAS so I am more inclined to become a Statistical Programmer.

Would the Clinical Trials companies hire a PhD holder for their Statistical Programmer position? From what I see on the job postings, companies typically asks for Master's degree in Statistics (or sometimes even just a Bachelor's degree) for Statistical Programmer positions, but I just wanted to hear opinions of people who actually works in Clinical Trials for private companies.

• biostatistics
• clinical-trials

• 1 $\begingroup$ I worked for some pharmaceutical companies and a CRO during my career. In my experience the statistical programmers did not have PhDs. $\endgroup$ –  Michael R. Chernick Commented Mar 22, 2018 at 0:51
• $\begingroup$ Unless it's PhD in UK or some other country where it can realistically be done in 3 years, I'm not sure it's worth the opportunity cost $\endgroup$ –  Aksakal Commented Mar 22, 2018 at 0:52
• $\begingroup$ Thank you for your comments! I have a follow up question -- if I ever choose to become a Biostatistician rather than Statistical Programmer, would it be more advisable to take a PhD program in Stat? Would CROs consider new PhD graduates to be just as qualified as someone with Master's degree with years of experience, when it comes to Biostatistician positions? $\endgroup$ –  jschnieder Commented Mar 22, 2018 at 1:02
• $\begingroup$ My impression is that something beyond a Bachelor is usually expected Biostatistician roles, but beyond that the technical strength / background / degree / interests etc. will mostly affect what one gets to work on. It may also affect hiring/interview decisions (and attitudes vary - some pharmaceutical companies may occasionally go through "we only hire PhDs"-phases (even of that is not truly needed for most of three work), I have the impression that this is a lot less common at CROs. $\endgroup$ –  Björn Commented Mar 22, 2018 at 6:31

I have a PhD in statistics, where I specialised in Bayesian theory. These days I am doing work as lead statistician on RCT research in a couple of health projects. This involves RCT planning and execution, analysis of data, and reporting outcomes of trials. On the basis of my experience, this is what I think:

For the vast majority of work doing statistical programming in health, a Masters-level education in statistics would be sufficient. The main skills you will need for practice are good skills in using statistical computing (e.g., SAS, R, etc.) to organise, clean, and analyse data, and create routines for automated reproducible analysis. Most of the models you use are likely to be commonly used model forms that have already been programmed into statistical software (e.g., regression, GLMs, GLMMs), and it is rare that you need custom models. You should put in the time to understand these basic model categories deeply, and learn the implementation of these models in statistical programming.

A PhD is likely give you a deeper knowledge of theory than most others in your field who lack that background, and better mathematical skills. It also gives you practice at the process of research leading to peer-reviewed published work. This level of study gives you very solid "first principles" knowledge of statistical theory and models, which gives you an advantage when you encounter problems requiring some variation of standard models or custom models.

Aside from general theory knowledge, and improved mathematical ability, the value of a PhD depends a great deal on the relevance of your research topic to your future field. If you undertake a research project in the field of statistical programming for RCTs, that will be very helpful for a future career in that field. If your topic is not relevant to your future field (as in my case) the value you will obtain will just be a general improvement in your theory and mathematical abilities, and broader knowledge of statistics.

Even with an irrelevant project, a PhD in statistics is going to give you some training that is useful in a general sense (better theory knowledge, better maths, etc.). Although there is value in this program, there is also a big opportunity cost . If you spend a standard full-time period of four years doing a PhD, that is going to be at the expense of four less years of professional experience in the industry. Using the equivalent amount of time practicing in the field is likely to give you much more skill in the day-to-day operations in that field than undertaking a PhD.

If you're even moderately undecided about a PhD, I suggest you don't do it. A PhD candidature is a major commitment requiring a hard slog through a lot of road-blocks. It is rarely smooth, and the academic landscape is littered with the bodies of PhD drop-outs. For what you want to do, I would suggest trying to get industry experience as early as possible, and then consider later postgraduate education once you have a bit of experience.

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Any risks down the line choosing maths vs. stats PhD programme?

It seems funding for a PhD (at least in the UK) is a lot easier to get for a PhD in statistics rather then maths. Provided one can find a supervisor who has the background and research interestes that would allow one to keep extending ones pure mathematical toolbox are there any major risks in going for a PhD in stats ?

It seems to me that at any given stage there is a lot of flexibility with a degree in mathematics. (For instance people seem very open minded to let a maths MSc do a stats PhD). Is it justified to be worried about losing this flexibility if one opts for the PhD in stats rather then maths?

In particular I am worried about whether it is possible to transition back to the maths departement for a postdoctoral position or something equivalent. So essentially I want to know whether one will get branded to an extend that would make it difficult to go on and work in pure mathematics after the PhD.

• graduate-school
• career-path

• Looks much better. There are a few mathematicians on this site who can hopefully provide a good answer now. –  eykanal Commented Oct 24, 2012 at 13:11

2 Answers 2

Provided one can find a supervisor who has the background and research interestes that would allow one to keep extending ones pure mathematical toolbox are there any major risks in going for a PhD in stats?

There's a small risk, but it can be managed. The first issue is that you need to make the mathematical content of your work very clear, for example by publishing in journals that could be considered both math journals and statistics journals (e.g., IMS journals). However, if you're interested in math departments I assume you'd be doing that anyway.

The slightly more subtle issue is how mathematicians view statisticians. There's sometimes a mild prejudice that people in mathematical statistics are overly specialized and outside of the mainstream of mathematics. For example, it's possible to get a Ph.D. in mathematical statistics while having considerably less breadth of mathematical background than would be considered acceptable for a math grad student. (Of course, the flip side is that you are expected to know other things instead.) There's sometimes a fear that a statistician would be unlikely to talk much with other math department members, or might be unwilling or unable to teach anything but statistics.

Plenty of statisticians have found jobs in math departments, so I don't want to be discouraging. However, I'd recommend focusing on mathematical breadth. For example, if you work with people in combinatorics or algebra, then it will be clear to everyone why a math department is a natural fit. If you talk only to statisticians, it will be less clear. It can still work out even then, but generally when the department either has a thriving statistics group or has decided they really need a statistician (and either way this cuts down on the flexibility of your job search).

I cannot answer about stats/maths directly, but in general the department you get your PhD in matters less than who it is with. This is especially true in places like the UK were there is no course work component. In my current department, psychology, about 50% of the faculty, including myself, did not get our PhD from Psychology departments. That said, if you are only willing to teach in a Maths department, then you should probably go to a Maths department. If you are willing to teach in either Maths or Stats, then it doesn't matter too much.

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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  

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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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• 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 three specialized tracks that take advantage of UW’s interdisciplinary environment: Statistical Genetics (StatGen), Statistics in the Social Sciences (CSSS), and Machine Learning and Advanced Data Science (MLADS). 

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).  
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• 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, and two other, numerically graded 500-level courses approved at the discretion of the Graduate Program Coordinator. Preapproved courses include STAT 516, STAT 517, GENOME 540, GENOME 541, and GENOME 562. These four courses may be counted as the four required Ph.D.-level electives. Additionally, students must complete at least three quarters of participation (one credit per quarter) in the Statistical Genetics Seminar (BIOST 581). 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. These courses may be counted as the four required Ph.D.-level electives. 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 Advanced Data Science (MLADS) Track

Students in the Machine Learning and Advanced Data Science (MLADS) Ph.D. track are required to take four numerically graded courses approved at the discretion of the Ph.D. Graduate Program Coordinator. Pre-approved courses include STAT 535, STAT 538, STAT 548/CSE 547, CSE 512, CSE 515, CSE 544 and EE 578. These four courses may be counted as part of the four required Ph.D.-level electives. This is not a transcriptable option, ie., the fact that the student completed the requirements will not be noted in their transcript. 

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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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Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

• Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
• Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
• Our students receive job offers from top academic and non-academic institutions .
• Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
• Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
• Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
• Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
• GRE requirement: Waived for Fall 2024.
• Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
• The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
• Deadline: Jan 8, 2024 .
• Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
• Timeline: P.hD students begin the program in September only.  Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

• What is the application deadline? What is the deadline for financial aid? Our application deadline is January 6, 2025
• 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. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• 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 PhD 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. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
• Can I contact the department with specific scores and get feedback on my competitiveness for the program? 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? No. The general GRE exam is waived for the Fall 2025 admissions 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 the Graduate School of Arts and Sciences.
• 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 Graduate School of Arts and Sciences 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 the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

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 of Arts and Sciences.  Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences 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; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
• What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University  107 Low Library, MC 4303 535 West 116th Street  New York, NY 10027
• How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
• Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
• 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] .

For more information please contact us at  [email protected]

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DEPARTMENT OF STATISTICS Columbia University Room 1005 SSW, MC 4690 1255 Amsterdam Avenue New York, NY 10027 Phone: 212.851.2132 Fax: 212.851.2164

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Doctoral program statistics.

Use this page to explore summary statistics for research doctoral programs administered by the Graduate School. Methodology and definitions are provided at the bottom of the page.   

For additional graduate statistics, survey results, and career outcomes data, see program metrics .

Methodology and Definitions

Admissions counts.

Applied, admitted and matriculated counts are reported for new, external applications only. Current students who transfer into a different graduate program at Cornell without submitting a new application are not counted here.

Individuals may defer enrollment and/or be admitted to a program that differs from the one to which they originally applied. This can cause admitted and matriculated counts to be higher than application counts in some fields. 

Admission cycles start in the summer and continue through the following spring. For example, the 2020-21 admissions year includes data from summer 2020 through spring 2021. Because these dashboards are updated annually in the fall, the most recent year will not include data from the spring.

Average Admit Rate

Admit rate is the percentage of applicants who were admitted. Highly selective programs tend to have low admit rates. The five year average provides a good indicator of typical admit rates.

Enrollment numbers are derived from the student enrollment snapshot that is captured the sixth week of each fall term. Only students who are enrolled on the census date are counted. Students on an approved leave of absence are not included.

Average Completion Rate

Completion rate is the percentage of entering doctoral students who successfully completed the degree. Completion rates are reported by entering cohort, which is defined by the first term in which a student is enrolled in their doctoral program, regardless of any prior enrollment in a master’s program. The cohorts included here entered their programs seven to twelve years ago, and thus have had adequate time to finish a doctoral degree.

Status of Students in Each Recent Entering Cohort

This graph shows the current status of students who began the doctoral program in each of the last ten academic years. Students listed as completed have received the doctoral degree. Students are considered current in their program if they are still actively pursuing the doctoral degree or are on an approved temporary leave of absence. Students listed as discontinued have either left the university without a degree or switched to a different type of degree program (in many cases a master’s degree).

Time to Degree (TTD)

Time-to-degree degree measures the time in years from the first day of a student’s initial enrollment in their doctoral program to the day of their degree conferral. Time-to-degree measures elapsed time only, not enrolled time. It does not stop and start if a student takes a leave of absence. For Master’s/PhD students, time-to-degree starts when they begin the PhD phase of their studies. If a student was enrolled in a master’s program prior to matriculating in the doctoral program, the separate time in the master’s program is not included. Because of this, time-to-degree may appear shorter in some doctoral programs where it is common to complete a master’s prior to matriculation in the doctoral program.

The median time to degree can be thought of as the “mid-point”, where half of the students completed in a time period that is less than or equal to this value. The median is not affected by extreme values or outliers. 

Department of Statistics and Data Science

Ph.d. program.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. See the list of alumni for examples.

PhD Program

The PhD Statistics program provides excellent training in the modern theory, methods, and applications of statistics to prepare for research and teaching careers in academia or industry, including interdisciplinary research in a wide array of disciplines. The median time to degree is five years.

Students will take courses in modern theory, methods, and applications of statistics, demonstrate mastery of this material via a qualifying examination, and then conduct statistical research under the supervision of one of the many regular or affiliate faculty members in the department, resulting in a dissertation.

The PhD qualifying examination is primarily based on the first-year curriculum. Most students pass at the end of the summer after the first year of the program. Students select between two versions of the examination, one with questions from mathematical statistics and probability or the second with questions from mathematical statistics and statistical methods.

Graduates are prepared for positions in academia, business, or government. They have taken positions at leading universities such as UC-Berkeley, Penn, and Yale and at top companies such as Google, Facebook, and Eli Lilly. The department strives to support students in the PhD program as teaching, research, or project assistants.

Questions about our Statistics PhD Programs can be directed to our graduate program coordinator at  [email protected] .

Resources, Regulations, and Policies

• Statistics PhD Handbook 2024-2025 More
• Criteria for Satisfactory Progress More
• Current PhD Regulations More
• 2014 PhD Regulations More

PhD Statistics Program Options

There are two program options students can select from – PhD Statistics, Statistics Option or PhD Statistics, Biostatistics Option . 

We have a single admissions process for both options and we encourage applicants to select only one of the options and not list both when applying. Selection of the program to which you apply has very little influence on the admissions decision. If you are unsure of which program option to choose, students who enter our PhD program may readily switch between the programs. 

Please note that the Department of Biostatistics and Medical Informatics has a separate PhD program in Biomedical Data Science that is distinct from the programs in the Department of Statistics.

Statistics Option

Career Outcomes : Students will be prepared for research and teaching careers in academia, industry, and other disciplines.

Coursework : Students will take courses in several broad areas of statistical methods and theory. This includes two-semester sequences in mathematical statistics and in statistical methods, either a course in probability theory or a course in statistical computing, a statistical consulting course, and a wide variety of elective options.

Biostatistics Option

Career Outcomes : Students will be prepared for careers in clinical research, genetics, drug testing, and experimental design in academia, government, and private sector.

Coursework : Students in the Biostatistics named option complete the same required courses as are in the Statistics named option, but have additional required coursework in biostatistics and biology and fewer elective course requirements.

Applying to the PhD Statistics Program

The application deadline is December 1 for a fall term start (no spring admissions).   A reminder to only list either the Statistics Option or Biostatistics Option in your application, not both. Again, students who enter the PhD program in Statistics can readily switch between the programs.

We welcome applications from around the world and strive to admit well-qualified applicants who are interested in the diverse array of research interests of our faculty. We do not make preliminary evaluations of any applicant inquiry based on email communication. No decision will be made until after the deadline has passed and a completed file (including the application fee) has been received.

Before applying to the Statistics Department, please read the Graduate School Frequently Asked Questions. Note that there is a non-refundable application fee. Applicants whose native language is not English, or whose undergraduate instruction was not in English, must provide an English proficiency test score.

To be considered for financial assistantship, all required application materials listed below should be submitted via the electronic application at https://apply.grad.wisc.edu/ by the December 1 deadline.

• Letters of Recommendation
• Transcripts
• Statement of Purpose
• CV or Resumé
• Supplemental Application (Including a List of Courses)
• English Proficiency
• A minimum of three (3) letters of recommendation to be submitted electronically by the recommenders.
• The online application for admission asks for the name and email contact information of the references from whom you request recommendations. A recommendation request will be sent, by email, to each of your references. The email will include your name with a link to each department’s electronic recommendation form. The request can be sent at any time providing you meet department deadlines. You can change references or send a reminder through your application.
• It is common practice to contact your references ahead of time so that they expect your request.
• After you have submitted your application, you can view receipt of your recommendations through the online status system.
• As part of the online application, please upload a clear and easy-to-read PDF copy of your transcript from each institution of higher learning (post High School) that you have attended. Unofficial transcripts are acceptable. If we offer you admission, you will be asked to provide an official copy of your transcript to the Graduate School at that time. Admission will be contingent upon receiving the official transcript.
• If courses at the institution were not taught in English, we will need an electronic copy of both the transcript in the original language, and the transcript in English.
• Your statement of purpose should include why you feel that the UW-Madison program is a good fit for you, and conversely, why you are a good fit for our program. What are you hoping to work on in the field with your degree? Are there any professors here that you would particularly like to work with? Any research areas in statistics that particularly excite you?
• The overall length of the statement is usually about 2 pages, single or double spaced. You can use whatever font and formatting you are comfortable with.

Please upload a PDF copy of your CV or Resumé to the online application.

A supplemental application is required as part of the online application. You will be asked to answer the following questions and provide the following information:

• Are you applying to the Biostatistics option? Yes/No (There is no advantage to applying to both programs.)
• List any major competitive honors, awards, and/or fellowships you have received.
• List any undergraduate or graduate research experiences.
• Provide a table with all courses you have taken, are currently taking, or plan to take prior to coming to UW-Madison that contain substantial mathematical, statistical, quantitative, or computational content. Include courses from other disciplines such as economics, physics, or engineering, if applicable. Use one row per course with columns for the course number, course title, textbook used (if possible), and grade received (if already completed). Upload this document as a pdf.

The GRE is not required.

• For all international degree-seeking students, see the  Graduate School requirements page  for additional information.

Consult the Graduate School for general information about graduate admissions to the University of Wisconsin-Madison.

If you have any further questions, please email [email protected] . Please include your full name and what semester you are interested in applying for.