phd statistics syllabus

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Doctoral Curriculum

This program is designed for students who desire academic research careers. The foundation is a sequence of courses in probability, mathematical statistics, linear models and statistical computing. The program also encourages study in a cognate area of application.

Up to four courses per semester may be counted toward the overall requirement of 13 courses. The six core courses are usually taken in the first year.

Electives must include suitable courses numbered 9000 and above, when offered.

More advanced students choose from among various elective courses offered by the faculty of the Statistics and Data Science Department and other departments at the University. There is also considerable opportunity to take individually-structured reading courses with faculty in the department.

Student Involvement in the Department

In addition to formal coursework, the student is expected to participate in the informal intellectual life of the Department of Statistics and Data Science. This includes attendance at departmental colloquia, where visiting speakers describe current research, plus participation in informal seminars investigating current topics of interest in a non-course setting.

Department of Statistics and Data Science

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

Phone: (215) 898-8222

PhD Program

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• William Bekerman , PhD Student
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• Abhinav Chakraborty , PhD Student
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• Abhinandan Dalal , PhD Student
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• Joseph Deutsch , PhD Student
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• Zirui Fan , PhD Student
• Ryan Gross , PhD Student
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• Zhihan Huang , PhD Student
• Kevin Jiang , PhD Student
• Dongwoo Kim , PhD Student
• Junu Lee , PhD Student
• Chris Lin , PhD Student
• Yuxuan Lin , PhD Student
• Kaishu Mason , PhD Student
• Sagnik Nandy , PhD Student
• Ziang Niu , PhD Student
• Manit Paul , PhD Student
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• Xiaomeng Wang , PhD Student
• Yangxinyu Xie , PhD Student
• Ziqing Xu , PhD Student
• Yachong Yang , PhD Student
• Jeffrey Zhang , PhD Student
• Zhaojun Zhang , PhD Student
• Zijie Zhuang , PhD Student

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Department of Statistics

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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

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PhD Program information

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests. Many students are involved in interdisciplinary research. Students may also have the option to pursue a designated emphasis (DE) which is an interdisciplinary specialization:  Designated Emphasis in Computational and Genomic Biology ,  Designated Emphasis in Computational Precision Health ,  Designated Emphasis in Computational and Data Science and Engineering . The program requires four semesters of residence.

Normal progress entails:

Year 1 . Perform satisfactorily in preliminary coursework. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity. Years 2-3 . Continue coursework. Find a thesis advisor and an area for the oral qualifying exam. Formally choose a chair for qualifying exam committee, who will also serve as faculty mentor separate from the thesis advisor.  Pass the oral qualifying exam and advance to candidacy by the end of Year 3. Present research at BSTARS each year. Years 4-5 . Finish the thesis and give a lecture based on it in a department seminar.

Program Requirements

• Qualifying Exam

Course work and evaluation

Preliminary stage: the first year.

Effective Fall 2019, students are expected to take four semester-long courses for a letter grade during their first year which should be selected from the core first-year PhD courses offered in the department: Probability (204/205A, 205B,), Theoretical Statistics (210A, 210B), and Applied Statistics (215A, 215B). These requirements can be altered by a member of the PhD Program Committee (in consultation with the faculty mentor and by submitting a graduate student petition ) in the following cases:

• Students primarily focused on probability will be allowed to substitute one semester of the four required semester-long courses with an appropriate course from outside the department.
• Students may request to postpone one semester of the core PhD courses and complete it in the second year, in which case they must take a relevant graduate course in their first year in its place. In all cases, students must complete the first year requirements in their second year as well as maintain the overall expectations of second year coursework, described below. Some examples in which such a request might be approved are described in the course guidance below.
• Students arriving with advanced standing, having completed equivalent coursework at another institution prior to joining the program, may be allowed to take other relevant graduate courses at UC Berkeley to satisfy some or all of the first year requirements

Requirements on course work beyond the first year

Students entering the program before 2022 are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.

Starting with the cohort entering in the 2022-23 academic year , students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge. 

For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U).  Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the course should be numbered 204-272 to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.

First year course work: For the purposes of satisfactory progression in the first year, grades in the core PhD courses are evaluated as: A+: Excellent performance in PhD program A: Good performance in PhD program A-: Satisfactory performance B+: Performance marginal, needs improvement B: Unsatisfactory performance First year and beyond: At the end of each year, students must meet with his or her faculty mentor to review their progress and assess whether the student is meeting expected milestones. The result of this meeting should be the completion of the student’s annual review form, signed by the mentor ( available here ). If the student has a thesis advisor, the thesis advisor must also sign the annual review form.

Guidance on choosing course work

Choice of courses in the first year: Students enrolling in the fall of 2019 or later are required to take four semesters of the core PhD courses, at least three of which must be taken in their first year. Students have two options for how to schedule their four core courses:

• Option 1 -- Complete Four Core Courses in 1st year: In this option, students would take four core courses in the first year, usually finishing the complete sequence of two of the three sequences.  Students following this option who are primarily interested in statistics would normally take the 210A,B sequence (Theoretical Statistics) and then one of the 205A,B sequence (Probability) or the 215A,B sequence (Applied Statistics), based on their interests, though students are allowed to mix and match, where feasible. Students who opt for taking the full 210AB sequence in the first year should be aware that 210B requires some graduate-level probability concepts that are normally introduced in 205A (or 204).
• Option 2 -- Postponement of one semester of a core course to the second year: In this option, students would take three of the core courses in the first year plus another graduate course, and take the remaining core course in their second year. An example would be a student who wanted to take courses in each of the three sequences. Such a student could take the full year of one sequence and the first semester of another sequence in the first year, and the first semester of the last sequence in the second year (e.g. 210A, 215AB in the first year, and then 204 or 205A in the second year). This would also be a good option for students who would prefer to take 210A and 215A in their first semester but are concerned about their preparation for 210B in the spring semester.  Similarly, a student with strong interests in another discipline, might postpone one of the spring core PhD courses to the second year in order to take a course in that discipline in the first year.  Students who are less mathematically prepared might also be allowed to take the upper division (under-graduate) courses Math 104 and/or 105 in their first year in preparation for 205A and/or 210B in their second year. Students who wish to take this option should consult with their faculty mentor, and then must submit a graduate student petition to the PhD Committee to request permission for  postponement. Such postponement requests will be generally approved for only one course. At all times, students must take four approved graduate courses for a letter grade in their first year.

After the first year: Students with interests primarily in statistics are expected to take at least one semester of each of the core PhD sequences during their studies. Therefore at least one semester (if not both semesters) of the remaining core sequence would normally be completed during the second year. The remaining curriculum for the second and third years would be filled out with further graduate courses in Statistics and with courses from other departments. Students are expected to acquire some experience and proficiency in computing. Students are also expected to attend at least one departmental seminar per week. The precise program of study will be decided in consultation with the student’s faculty mentor.

Remark. Stat 204 is a graduate level probability course that is an alternative to 205AB series that covers probability concepts most commonly found in the applications of probability. It is not taught all years, but does fulfill the requirements of the first year core PhD courses. Students taking Stat 204, who wish to continue in Stat 205B, can do so (after obtaining the approval of the 205B instructor), by taking an intensive one month reading course over winter break.

Designated Emphasis: Students with a Designated Emphasis in Computational and Genomic Biology or Designated Emphasis in Computational and Data Science and Engineering should, like other statistics students, acquire a firm foundation in statistics and probability, with a program of study similar to those above. These programs have additional requirements as well. Interested students should consult with the graduate advisor of these programs. 

Starting in the Fall of 2019, PhD students are required in their first year to take four semesters of the core PhD courses. Students intending to specialize in Probability, however, have the option to substitute an advanced mathematics class for one of these four courses. Such students will thus be required to take Stat 205A/B in the first year,  at least one of Stat 210A/B or Stat 215A/B in the first year, in addition to an advanced mathematics course. This substitute course will be selected in consultation with their faculty mentor, with some possible courses suggested below. Students arriving with advanced coursework equivalent to that of 205AB can obtain permission to substitute in other advanced probability and mathematics coursework during their first year, and should consult with the PhD committee for such a waiver.

During their second and third years, students with a probability focus are expected to take advanced probability courses (e.g., Stat 206 and Stat 260) to fulfill the coursework requirements that follow the first year. Students are also expected to attend at least one departmental seminar per week, usually the probability seminar. If they are not sufficiently familiar with measure theory and functional analysis, then they should take one or both of Math 202A and Math 202B. Other recommended courses from the department of Mathematics or EECS include:

Math 204, 222 (ODE, PDE) Math 205 (Complex Analysis) Math 258 (Classical harmonic analysis) EE 229 (Information Theory and Coding) CS 271 (Randomness and computation)

The Qualifying Examination 

The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department.  At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.

Qualifying Exam Chair. For qualifying exam committees formed in the Fall of 2019 or later, the qualifying exam chair will also serve as the student’s departmental mentor, unless a student already has two thesis advisors. The student must select a qualifying exam chair and obtain their agreement to serve as their qualifying exam chair and faculty mentor. The student's prospective thesis advisor cannot chair the examination committee. Selection of the chair can be done well in advance of the qualifying exam and the rest of the qualifying committee, and because the qualifying exam chair also serves as the student’s departmental mentor (unless the student has co-advisors), the chair is expected to be selected by the beginning of the third year or at the beginning of the semester of the qualifying exam, whichever comes earlier. For more details regarding the selection of the Qualifying Exam Chair, see the "Mentoring" tab.  

Paperwork and Application. Students at the point of taking a qualifying exam are assumed to have already found a thesis advisor and to should have already submitted the internal departmental form to the Graduate Student Services Advisor ( found here ).  Selection of a qualifying exam chair requires that the faculty member formally agree by signing the internal department form ( found here ) and the student must submit this form to the Graduate Student Services Advisor.  In order to apply to take the exam, the student must submit the Application for the Qualifying Exam via CalCentral at least three weeks prior to the exam. If the student passes the exam, they can then officially advance to candidacy for the Ph.D. If the student fails the exam, the committee may vote to allow a second attempt. Regulations of the Graduate Division permit at most two attempts to pass the oral qualifying exam. After passing the exam, the student must submit the Application for Candidacy via CalCentral .

The Doctoral Thesis

The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division. See Alumni if you would like to view thesis titles of former PhD Students.

Graduate Division offers various resources, including a workshop, on how to write a thesis, from beginning to end. Requirements for the format of the thesis are rather strict. For workshop dates and guidelines for submitting a dissertation, visit the Graduate Division website.

Students who have advanced from candidacy (i.e. have taken their qualifying exam and submitted the advancement to candidacy application) must have a joint meeting with their QE chair and their PhD advisor to discuss their thesis progression; if students are co-advised, this should be a joint meeting with their co-advisors. This annual review is required by Graduate Division.  For more information regarding this requirement, please see  https://grad.berkeley.edu/ policy/degrees-policy/#f35- annual-review-of-doctoral- candidates .

Teaching Requirement

For students enrolled in the graduate program before Fall 2016, students are required to serve as a Graduate Student Instructor (GSI) for a minimum of 20 hours (equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.

Effective with the Fall 2016 entering class, students are required to serve as a GSI for a minimum of two 50% GSI appointment during the regular academic semesters prior to graduation (20 hours a week is equivalent to a 50% GSI appointment for a semester) for Statistics courses numbered 150 and above. Exceptions to this policy are routinely made by the department.

Each spring, the department hosts an annual conference called BSTARS . Both students and industry alliance partners present research in the form of posters and lightning talks. All students in their second year and beyond are required to present a poster at BSTARS each year. This requirement is intended to acclimate students to presenting their research and allow the department generally to see the fruits of their research. It is also an opportunity for less advanced students to see examples of research of more senior students. However, any students who do not yet have research to present can be exempted at the request of their thesis advisor (or their faculty mentors if an advisor has not yet been determined).

Mentoring for PhD Students

Initial Mentoring: PhD students will be assigned a faculty mentor in the summer before their first year. This faculty mentor at this stage is not expected to be the student’s PhD advisor nor even have research interests that closely align with the student. The job of this faculty mentor is primarily to advise the student on how to find a thesis advisor and in selecting appropriate courses, as well as other degree-related topics such as applying for fellowships.  Students should meet with their faculty mentors twice a semester. This faculty member will be the designated faculty mentor for the student during roughly their first two years, at which point students will find a qualifying exam chair who will take over the role of mentoring the student.

Research-focused mentoring : Once students have found a thesis advisor, that person will naturally be the faculty member most directly overseeing the student’s progression. However, students will also choose an additional faculty member to serve as a the chair of their qualifying exam and who will also serve as a faculty mentor for the student and as a member of his/her thesis committee. (For students who have two thesis advisors, however, there is not an additional faculty mentor, and the quals chair does NOT serve as the faculty mentor).

The student will be responsible for identifying and asking a faculty member to be the chair of his/her quals committee. Students should determine their qualifying exam chair either at the beginning of the semester of the qualifying exam or in the fall semester of the third year, whichever is earlier. Students are expected to have narrowed in on a thesis advisor and research topic by the fall semester of their third year (and may have already taken qualifying exams), but in the case where this has not happened, such students should find a quals chair as soon as feasible afterward to serve as faculty mentor.

Students are required to meet with their QE chair once a semester during the academic year. In the fall, this meeting will generally be just a meeting with the student and the QE chair, but in the spring it must be a joint meeting with the student, the QE chair, and the PhD advisor. If students are co-advised, this should be a joint meeting with their co-advisors.

If there is a need for a substitute faculty mentor (e.g. existing faculty mentor is on sabbatical or there has been a significant shift in research direction), the student should bring this to the attention of the PhD Committee for assistance.

PhD Student Forms:

Important milestones: .

Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO. If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding (including GSI or GSR appointments, and many fellowships). 

†Students who are considering a co-advisor, should have at least one advisor formally identified by the end of the second year; the co-advisor should be identified by the end of the fall semester of the 3rd year in lieu of finding a Research Mentor/QE Chair.

Expected Progress Reviews: 

* These meetings do not need to be held in the semester that you take your Qualifying Exam, since the relevant people should be members of your exam committee and will discuss your research progress during your qualifying exam

** If you are being co-advised by someone who is not your primary advisor because your primary advisor cannot be your sole advisor, you should be meeting with that person like a research mentor, if not more frequently, to keep them apprised of your progress. However, if both of your co-advisors are leading your research (perhaps independently) and meeting with you frequently throughout the semester, you do not need to give a fall research progress report.

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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. 

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

Choosing a Field of Study

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

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

Residency Requirements

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

Your Advisor and Special Committee

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

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

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

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

Statistics PhD Travel Support

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

Completion of the PhD Degree

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

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

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Ph.d. program.

Statistical Science at Duke is the world's leading graduate research and educational environment for Bayesian statistics, emphasizing the major themes of 21st century statistical science: foundational concepts of statistics, theory and methods of complex stochastic modeling, interdisciplinary applications of statistics, computational statistics, big data analytics, and machine learning. Life as a Ph.D. student in Statistical Science at Duke involves immersion in a broad range of research experiences and emphasizes conceptual innovation, as well as building a deep and broad foundation in theory and methods.

Coupled with our core emphases in modeling, computation and the methodologies of modern statistical science is a broad range of interdisciplinary relationships with many other disciplines (biomedical sciences, environmental sciences, genomics, computer science, engineering, finance, neuroscience, social sciences, and others). The rich opportunities for students in interdisciplinary statistical research at Duke are complemented by opportunities for engagement in research in summer projects with nonprofit agencies, industry, and academia.

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PhD in Statistics

Program description.

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

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

Requirements for the Ph.D. (Statistics Track)

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

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

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

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

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

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

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

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

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

Financial Aid

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

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

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

Ph.D. Program Milestones

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

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

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

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• Previous Program Requirements

The Ph.D. in Statistics is flexible and allows students to pursue a variety of directions, ranging from statistical methodology and interdisciplinary research to theoretical statistics and probability theory. Students typically start the Ph.D. program by taking courses and gradually transition to research that will ultimately lead to their dissertation, the most important component of the Ph.D. program.

These requirements apply to students admitted for Fall 2020 and after. Students admitted in Fall 2019 and earlier should consult the Previous Program Requirements page .

PhD Coursework:

The core PhD curriculum is divided into five areas: 

Methods — STATS 600 and 601

Practice — STATS 604

Statistical Theory — STATS 511, 610, 611

Probability — STATS 510, 620, 621

Computing — STATS 507, 606, 608 

All doctoral students must complete the following in their first three semesters in the program and before advancing to candidacy: 

Take all methods and practice courses (600, 601, 604)

Take at least two courses in the combined areas of statistical theory and probability,  including at least one course in statistical theory and at least one 600-level course 

Take at least one computing course

Achieve a 3.5 average grade (on the 4.0 scale used by Rackham) in 600, 601, 604, and one 600-level statistical theory or probability course

Not completing requirements 1-4 by the end of the third semester will trigger probation which, if not resolved by the end of the fourth semester, may lead to dismissal from the program.  For more details, see the link below. 

By the end of the PhD program, all students must take at least 30 credits of graduate statistics courses.    All courses from the core areas count towards this total, as well as all 600-level, 700-level, and selected additional  500-level courses with approval of the PhD Program Director. Seminars and independent study courses do not count. At least 21 credits must be at the 600 level or higher. The Rackham Graduate School requires PhD students to maintain an overall GPA of at least 3.0 to remain in good standing.   

In addition, all doctoral  students must take 3 credits of cognate courses as required by the Rackham graduate school, and two professional development seminar courses. Cognate courses are 400- and higher-level courses from outside Statistics and not cross-listed with Statistics. All cognate course selections must be approved by the PhD Program Director. The professional development courses are 

STATS 810, research ethics and introduction to research tools, in the first semester in the program.

STATS 811, technical writing in statistics. Students are strongly advised to complete this course in their second or third year.

Typical Course Schedules:

Our Ph.D. program admits students with diverse academic backgrounds. All PhD students take STATS 600/601  in their first year. Students are strongly encouraged to take STATS 604 in their second year (Stats 600 is a prerequisite).  

Students with less mathematical preparation typically take STATS 510/511 (the Master’s level probability and statistical theory) in their first year and 600-level probability and/or statistical theory courses in their second year.    

Advanced students, for example those with a Master’s degree, typically do not need to take 510/511, and in some cases may skip 610 and 621. Students who wish to take 600-level probability and statistical theory courses in their first year must take a placement test just before the fall semester of their first year to get approved. The PhD Program Director will help each student choose their individual path towards completing the requirements.  

Some typical sample schedules are listed below. In most cases, we do not recommend taking more than three full-load courses per semester (not counting seminars).

Sample schedule 1:

Sample schedule 2:

Advancing to Candidacy:

Students are expected to find a faculty advisor and start research leading to their dissertation proposal no later than the summer after their first year. The PhD Program Director and the faculty mentor assigned to each first year student can assist with finding a faculty advisor. Students are expected to submit a dissertation proposal and advance to candidacy some time during their second or third year in the program.   

Requirements for advancing to candidacy are:

Satisfying Requirements 1-4

Completing at least 3 credit hours of cognate courses

Writing a dissertation proposal and passing the oral preliminary exam, which consists of presenting the proposal to the student's preliminary thesis committee

A dissertation proposal should identify an interesting research problem, provide motivation for studying it, review the relevant literature, propose an approach for solving the problem​, and present at least some preliminary results​. The written proposal must be submitted to the preliminary thesis committee and the graduate coordinator a​head of time (one week minimum, two weeks recommended)​ and then presented in the oral preliminary exam. The preliminary thesis committee is chaired by the faculty advisor and must include at least two more faculty members, at least one of them from Statistics. ​​The faculty on the preliminary thesis committee typically continue t​o serve ​on ​the doctoral thesis committee​​, but changes are allowed.  Please see Rackham rules on thesis committees for more information.  

At the oral preliminary exam, the committee will ask questions about the proposal and the relevant background and either elect to accept the proposal as both substantial and feasible, ask for specific revisions, or decline the proposal. The unanimous approval of the proposal by the committee is necessary for the student to advance to candidacy.

Additional Information:

Students are encouraged to complete the bulk of their coursework beyond Requirements 1-4 in the first two years of study.  Candidates are allowed to take only one course per semester without an increase in tuition.

All PhD students are expected to register for Stats 808/809  (Department Seminar) every semester unless restricted by candidacy, and attend the seminar regularly regardless of whether they are registered.  

Exceptions to the PhD program requirements may be granted by the PhD Program Director.

Annual Report:

Each candidate is required to meet with the members of their thesis committee annually. This could be in the form of either giving a short presentation on their research progress to the thesis committee as a group, or meeting with committee members individually.

Each committee member should complete a Thesis Committee Member Report and return it to the student. The student should share the completed Thesis Committee Member Reports with both the PhD Program Coordinator and their advisor.

All meetings with the committee members should take place by April 15.

Following the meetings, the student and the advisor should complete the Annual PhD Candidate Self-Evaluation and Feedback Form . The advisor should review the committee members’ Thesis Committee Member Reports and take them into account when completing the advisor’s portion. The completed Annual PhD Candidate Self-Evaluation and Advisor Feedback Form must be submitted to the PhD Program Coordinator by May 31. The completed form will be saved with the department, and a copy will be shared with the student.

Dissertation and Defense:

Each doctoral student is expected to write a dissertation that makes a substantial and original contribution to statistics or a closely related field. This is the most important element of the doctoral program. After advancing to candidacy, students are expected to focus on their thesis research under the supervision of the thesis advisor and the doctoral committee. The composition of the doctoral committee must follow the Rackham's  guidelines for dissertation committee service . The written dissertation is submitted to the committee for evaluation and presented in an oral defense open to the public.

Rackham Requirements:

The Rackham Graduate School imposes some additional requirements concerning residency, fees, and time limits. Students are expected to know and comply with these requirements.

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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 Commitee 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.

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Statistics PhD Program

Sample syllabi from previous years, bst 401 probability theory.

• Semester: Fall
• Description: Probability spaces; random variables; independence; distributions; expectation; characteristic functions and inversion theorems; convergence; laws of large numbers; central limit theorem.
• Can students outside the department’s program(s) take it? Please contact instructor to discuss your background.
• BST 401 Syllabus

BST 402 Stochastic Processes

• Description: Markov chains; birth-death processes; random walks; renewal theory; Poisson processes; Brownian motion; branching processes; martingales; with applications.
• BST 402 Syllabus

BST 411 Statistical Inference I

• Description: Probability distributions, transformations and sampling distributions; statistical models; estimation, hypothesis testing, and confidence intervals for parametric models; introduction to large-sample methods.
• BST 411 Syllabus

BST 412 Statistical Inference II

• Semester: Spring
• Description: Types of convergence; asymptotic linearity; influence functions; consistency and asymptotic normality; large sample estimation, maximum likelihood estimation; Wald, likelihood ratio, and score tests; generalized Neyman-Pearson lemma; nuisance parameters; efficiency; alternative methods for estimation (M-estimation, GEE, generalized method of moments); resampling methods (bootstrap, permutation tests); decision-theoretic inference.
• BST 412 Syllabus

BST 413 Bayesian Inference

• Description: Posterior distributions for single and multiple parameter models under conjugacy; hierarchical models; noninformative and informative prior distributions; modern computational techniques, including Markov chain Monte Carlo; model checking; posterior predictive checks; sensitivity analysis.
• BST 413 Syllabus

BST 426 Linear Models

• Description: Theory of least-squares; point estimation in the general linear model; projection operators, estimable functions and generalized inverses; tests of general linear hypotheses; power; confidence intervals and ellipsoids; simultaneous inference; linear and polynomial regression; analysis of variance and analysis of covariance models; fixed, random, and mixed effects; correlation; prediction.
• BST 426 Syllabus

BST 430 Introduction to Statistical Computing

• Description: Basic/intermediate R programming; statistical analysis in R; visualization in R; reproducible research and collaborative coding; command line tools and BlueHive. Introduction to SAS programming; statistical analysis in SAS. Topics in statistical analysis provide working examples.
• BST 430 Syllabus

BST 432 High Dimensional Data Analysis

• Description: An overview of modern tools for high-dimensional data analysis, with a particular focus on connecting them to their statistical underpinnings, both applied and theoretical perspectives. Emphasis will be placed on understanding benefits and limitations of these tools. The major topics include: decision theory; basic tail and concentration bounds; univariate/multivariate methods; large-scale testing; penalized methods; dimension reduction; clustering; tree-based methods; support vector machine; network analysis.
• BST 432 Syllabus

BST 434 Genomic Data Analysis

• Description: Introduction to techniques used in modern genomic experimentation and the corresponding statistical methods and software available to visualize, analyze, and interpret these data. Specific topics include mRNA/microRNA expression, protein abundance, protein-DNA binding, copy number variants, single nucleotide variants, DNA methylation, and microbial abundance.
• BST 434 Syllabus

BST 461 Biostatistical Methods I

• Description: Study designs; inference regarding proportions; contingency table analysis; diagnostic testing; one-way and two-way analysis of variance; multiple comparisons involving means; simple and multiple linear regression; analysis of covariance; interactions; logistic and Poisson regression; introduction to survival analysis; multicollinearity; variable selection; model checking; sample size determination.
• BST 461 Syllabus

BST 462 Biostatistical Methods II

• Description: Linear mixed effects models; intraclass correlation; advanced logistic regression; generalized estimating equations; missing data; extensions of the Cox proportional hazards model; shrinkage estimation in regression; nonparametric methods; bootstrap methods; scatterplot smoothing; nonparametric regression (trees, forests, generalized additive models).
• BST 462 Syllabus

BST 463 Introduction to Biostatistics

• Description: Introduction to statistical techniques with emphasis on applications in the health sciences. Summarizing and displaying data; introduction to probability; Bayes' theorem and its application in diagnostic testing; binomial, Poisson, and normal distributions; sampling distributions; estimation, confidence intervals, and hypothesis testing involving means and proportions; simple correlation and regression; contingency tables; use of statistical software.
• Can students outside the department’s program(s) take it? Yes
• BST 463 Syllabus

BST 465 Design of Clinical Trials

• Description: Introduction to the principles of clinical trials; clinical trial protocols; overview of the drug development process; hypotheses/objectives; specification of response variables; defining the study population; randomization; blinding; ethical issues; factorial designs; crossover designs; equivalence trials; trial monitoring and interim analyses; sample size and power; issues in data analysis and reporting; evaluating clinical trial reports.
• BST 465 Syllabus

BST 467 Applied Statistics in the Biomedical Sciences

• Description: Introduction to statistical techniques with emphasis on applications in the biomedical sciences. Introduction to probability and probability distributions; sampling distributions; estimation, confidence intervals and hypothesis testing in small and large samples; analysis of categorical data; analysis of variance; correlation and linear and nonlinear regression analysis; use of statistical software; illustrations using published articles in the biomedical sciences.
• BST 467 Syllabus

BST 479 Generalized Linear Models

• Description: Generalized linear models; computational techniques for model fitting; logistic and conditional logistic regression; Poisson and negative binomial regression; log-linear models; models for nominal and ordinal categorical data; quasi-likelihood functions; model checking; nonlinear regression models.
• BST 479 Syllabus

BST 487 Seminar in Statistical Literature

• Semester: Spring and Fall
• Description: Provides an introduction to the process of searching the statistical literature, opportunities to acquire knowledge of a focused area of statistical research, experience in organizing, preparing, and delivering oral presentations, and an introduction to the research interests of members of the faculty.
• Can students outside the department’s program(s) take it? No

BST 511 Topics in Statistical Inference I

• Description: Advanced topics in statistical inference and/or decision theory. Topics may change each year.

BST 512 Topics in Statistical Inference II

Bst 513 analysis of longitudinal and dependent data.

• Description: Modern approaches to the analysis of longitudinal and dependent data; random and mixed effects models; marginal models; generalized estimating equations; models for continuous and discrete outcomes.
• BST 513 Syllabus

BST 514 Survival Analysis

• Description: Parametric, nonparametric, and semiparametric methods for the analysis of survival data. right censoring; Kaplan-Meier curves; log-rank and weighted log-rank tests; survival distributions; accelerated life and proportional hazards regression models; time-dependent covariates; partial likelihood; models for competing risks and multiple events.
• BST 514 Syllabus

BST 531 Nonparametric Inference

• Description: Nonparametric estimation and inference for one-sample location and paired data, two-sample location and/or dispersion, one- and two-way layouts with and without order restrictions, tests of independence, and regression; exact and large-sample results for some commonly used procedures, including the sign test and the sample median, the Mann-Whitney-Wilcoxon test and the Hodges-Lehmann location measure, and some generalizations to more complex data structures; density estimation; nonparametric regression; generalized additive models (GAM); cross-validation; bandwidth selection; exact and asymptotic bias, variance, and mean squared error (MSE).
• BST 531 Syllabus

BST 550 Topics in Data Analysis

• Description: Advanced statistical methods for data analysis. Topics may change each year.

BST 570 Topics in Biostatistics

• Description: Advanced biostatistical techniques. Topics may change each year.

Spring 2024 Semester PhD Courses

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

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This program has a rich tradition of creating groundbreaking statistical methods and conducting innovative applied statistics, bridging theory and practice and supporting knowledge discovery and decision-making through meaningful data extraction and analysis. Statistics is an indispensable pillar of modern science, including data science and artificial intelligence.

You can take advantage of the department’s flexible research options and work with your faculty of choice. You can leverage cross-department collaboration with biology, chemistry, medical sciences, economics, computer science, government, and public health to pursue your intellectual interests. You will become part of a close-knit, friendly department that offers many extra learning opportunities both inside and outside the program.

Examples of student projects include developing statistical methods to forecast infectious diseases from online search data, delineating causality from association, building a software package for evaluating redistricting plans in 50 states, leveraging machine learning algorithms for model-free inference, and employing a randomization-based inference framework to study peer effects. 

Graduates have secured faculty positions in institutions such as Stanford University; University of Pennsylvania; University of California, Berkeley; Johns Hopkins University; Carnegie Mellon University; Columbia University; and Georgia Institute of Technology. Others have begun careers at organizations such as Google, Apple, Etsy, Citadel, and the Boston Red Sox. 

Additional information on the graduate program is available from the Department of Statistics , and requirements for the degree are detailed in Policies .

Admissions Requirements

Please review admissions requirements and other information before applying. You can find degree program-specific admissions requirements below and access additional guidance on applying from the Department of Statistics .

Academic Background

Applicants should understand what the discipline of statistics entails and show evidence of involvement in applications or a strong theoretical interest.

The minimum mathematical preparation for admission is linear algebra and advanced calculus. Ideally, each student’s preparation should include at least one term each of mathematical probability and mathematical statistics. Additional study in statistics and related mathematical areas, such as analysis and measure theory, is helpful. In the initial stages of graduate study, students should give high priority to acquiring the mathematical level required to satisfy their objectives.

As statistics is so intimately connected with computation, computation is an important part of almost all courses and research projects in the department. Preferably, students should have programming experience relevant for statistical computation and simulation.

Standardized Tests

GRE General: Optional GRE Subject: Optional

Theses & Dissertations

Theses & Dissertations for Statistics

See list of Statistics faculty

APPLICATION DEADLINE

Questions about the program.

Department of Statistics

Handbook for phd students in statistics.

TABLE OF CONTENTS

• Introduction
• Incoming Students
• Course Registration
• First Year Course Requirements and Preliminary Examinations
• Second Year Requirements
• Third Year Requirements
• Fourth Year Requirements
• Fifth Year and Beyond
• Dissertation Defense and Submission
• Consulting Program
• Academic Year Student Support and Teaching Duties
• Summer Support
• Off Campus Work
• Student Representatives
• Student Offices
• Reimbursements

INTRODUCTION

Dear Students,

We have compiled this manual summarizing all the rules, requirements and deadlines governing the PhD program in the Statistics Department. We intend this manual to be the primary repository of these rules and we encourage you to refer to it periodically as you progress through our program. If you have any questions regarding the content you are welcome to contact the Department Graduate Advisor (Yali Amit) or the Student Affairs Specialist (Keisha Prowoznik).

Good luck with your studies!

INCOMING STUDENTS

• New Graduate Student Information–UChicago Grad: Information regarding the University of Chicago campus, living in the neighborhood, security, health, and other resources for incoming graduate students can be found here: https://grad.uchicago.edu/life-at-uchicago/admitted-students-welcome/
• Diagnostic Exam: A diagnostic exam will be emailed to all students the week before orientation to be returned to their advisor by the end of that week in order to help determine which courses to take for the upcoming year.
• Orientation: This event will take place on the week before classes start where new students will attend meetings throughout campus and the Department to become acclimated with procedures and guidelines for the PhD program. This is also course registration week, where all students will have to meet with their advisor to determine which courses to take during Autumn quarter. Incoming students will meet with the Department Graduate Advisor (DGA) for course registration.

COURSE REGISTRATION

Course registration starts the Monday of the 8th week of every quarter when the Student Affairs Specialist sends out an email to all students with the attached registration form for each student to fill out, and gain consent from their advisor and return back to the Student Affairs Specialist. Students must register themselves and if they cannot, they can seek help from the Student Affairs Specialist. All course registration must be completed the Friday of 10th week by 12:00PM Central Standard Time in order to avoid a late fee.

Drop/add week will always take place the first week of every quarter and run for three weeks and end on the Friday of third week for all PhD students. This is a time where students are able to drop and/or add courses to their schedule if they do not wish to take the courses they registered for during course registration week. Courses can be changed upon the advisor and instructor’s approval.

FIRST YEAR COURSE REQUIREMENTS AND PRELIMINARY EXAMINATIONS

The program offers four core sequences:

• Probability (STAT 30400, 38100, 38300)
• Mathematical statistics (STAT 30400, 30100, 30210)
• Applied statistics (STAT 34300, 34700, 34800 and 34900)
• Computational sequence (STAT 30900, 31015/31020, 37710).

All students must take the Applied Statistics and Mathematical Statistics sequence. In addition, it is highly recommended that students take a third core sequence based on their interests and in consultation with the Department Graduate Advisor (DGA).

Preliminary exams: At the start of their second year, several weeks before the start of the Fall quarter , the students take two preliminary examinations. The students will be informed by June 1 of the precise dates. All students must take the Applied Statistics Prelim. The prelim is a take-home exam provided online to the students during  prelim week. Student written reports are handed in two days later. A few days later, after the faculty review the reports the students have a 30 minute oral interview about their report.

For the second prelim, students can choose to take either the Theoretical Statistics or the Probability prelim. Students planning to take the Probability prelim should take the Probability sequence as their third first year course sequence and must receive approval from the DGA to take 38300 in the Spring instead of 348.  

During six weeks leading up to the prelims, two advanced PhD students will assist the first year students in preparing for the exams, holding weekly meetings one for the Applied Statistics exam and one for the Theoretical Statistics exam.

Incoming first-year students may request the DGA to take one or both of their preliminary exams. This will only be considered if the students have had extensive training in statistics in their prior studies. If approved, and if the student passes one or more of these, then he/she may be excused from the requirement of taking the first-year courses in that subject.

First year summer reading courses: It is highly recommended for first year students to take a reading course with a faculty member during the summer. This does not require formal registration, only coordination with a faculty member. Such a reading course typically involves reading a number of papers recommended by the faculty member and presenting them during the meetings.

Incoming students are advised by the DGA until they find a faculty advisor for their PhD thesis work.

First year students also share responsibility for organizing lunches with faculty to hear about their research, lunches with visiting seminar speakers and weekly departmental tea time.

SECOND YEAR REQUIREMENTS

In their second year, PhD students typically take several advanced topics courses in statistics, probability, computation, and applications. These should be selected with the dual objective of (i) acquiring a broad overview of current research areas, and (ii) settling on a particular research topic and dissertation supervisor. It is recommended that the students take at least one regular class based course each quarter. In addition, students can ask to take reading courses with faculty to learn more in depth about their fields of research. Students have considerable latitude in selecting their second-year courses, but their programs must be approved by the Department Graduate Advisor.  Students are expected to find a dissertation/thesis advisor by the end of the second year. The thesis advisor does not need to be a faculty member of the Statistics Department, however the dissertation/thesis committee must include at least two members of the Statistics Department (see below.)

Mini-seminars: During the second half of Spring Quarter second year students are required to give a short 10-12 minute presentation on a paper/papers they have read, followed by a short Q & A period. This provides the students with their first experience giving a presentation and both faculty and other students can provide feedback. The students typically present papers they have read in one of the reading courses they have taken with a faculty member during the second year.

THIRD YEAR REQUIREMENTS

Thesis Advisor and Dissertation Committee

By the end of the third year, each PhD student, after consultation with his or her dissertation advisor, shall establish a committee of at least three members, at least two of whom should be from Statistics. The departmental form listing the committee members, with their signatures, must be filed in the Department office by the end of Spring Quarter of the third year. The composition of the committee may be changed at any time if the student or faculty so choose; however, it must always include the student's dissertation advisor and at least two of the committee members must be regular faculty members from the Department of Statistics. Any such change must be filed as a resubmitted and newly completed and signed form with the Department office. As long as a student has not found a thesis advisor the DGA will remain the student’s advisor.

Interdisciplinary Theses

Many of our students choose to pursue research combining statistics and computation with another area of scientific research, such as genetics, neuroscience, health studies, environmental science, or social science. Students who choose to write an interdisciplinary thesis can work with a thesis advisor from another department as long as the two other committee members are from the Statistics Department.

FOURTH YEAR REQUIREMENTS

Proposal Presentation and Admission to Candidacy

By the end of Autumn Quarter of the fourth year, students should have completed a proposal presentation to their committee. This consists of a written (typically 5-10 page) report on completed and planned research with relevant references and a meeting with the committee discussing the proposed research (format is flexible, but typically a 1.5 hour meeting, with 45 minutes for student presentation and 45 minutes for questions and discussion). The proposal meeting will be scheduled by the student and his or her committee and reported to the Department office. Acceptance of the proposal by the Dissertation Committee is a formal requirement of the Department's PhD program. After a successful proposal presentation, the student will be formally admitted to candidacy for the PhD degree. By University rules, the dissertation defense cannot occur earlier than 8 months after admission to candidacy, and the student should keep this in mind when scheduling both the proposal presentation and the defense.

Following the fourth year, during each year that the student remains, the student is required to have a meeting with the committee no later than November 30 th of Autumn Quarter or defend by that time.

FIFTH YEAR AND BEYOND

The Department goal is for the majority of students to complete and defend their thesis by the end of their 5th year. Foreign students will have their visas extended beyond the fifth year on a yearly basis depending on the decision of the committee.

In the first 4 weeks of the Fall quarter of the 5th year students should convene their Dissertation Committee for an update on their progress. Committee members will confirm satisfactory progress on a form provided by the Department office.

Students who have not completed their thesis by the end of Fifth year must petition their committee and the Department Chair in order to continue in the program into their Sixth year and maintain their stipend. If their petition is  approved and they are not supported as RA’s they  will be required to teach every quarter.

Students who continue to their 6th year should again  convene their Dissertation Committee in the first 4 weeks of the Fall quarter  of the 6th year and Committee members will confirm satisfactory progress on a form provided by the Department office.

Students who have not completed their dissertation and defense by the end of the sixth year will no longer receive stipends or be employed by the Department. These students are required to petition their committee and the Department Chair both in order to continue in the doctoral program and for any financial support (tuition, fees). The petition is to be made before the end of Spring Quarter of the sixth year.

DISSERTATION DEFENSE AND SUBMISSION

The PhD degree will be awarded following a successful defense and the electronic submission of the final version of the dissertation to the University's Dissertation Office. In this process, a number of University and Department deadlines have to be obeyed. Listed in reverse order, the steps are:

a) Submission of Final Version of Dissertation: The deadline is set by the University and is generally on a Friday in the 6th or 7th week of the quarter when the degree will be awarded. See:

• Information for PhD Students: https://www.lib.uchicago.edu/research/scholar/phd/students/
• Dissertation Deadlines: https://www.lib.uchicago.edu/research/scholar/phd/students/dissertation-deadlines/
• Information about dissertations: https://www.lib.uchicago.edu/research/scholar/phd/students/
• Latex template for dissertation: https://www.overleaf.com/latex/templates/university-of-chicago-phd-dissertation-template/syvxgkqhvqqt

for this deadline as well as guidelines for the formatting of dissertations.

b) Dissertation Defense: The thesis defense will be an open seminar announced to the Department. Following the regular question-and-answer session, the committee will remain, together with any interested faculty, and continue questioning the candidate. The decision on the thesis will then be reached in a closed meeting of the dissertation committee. The defense is to be scheduled at least two weeks before the University deadline indicated in point (a). A final draft of the dissertation must be made available to the entire faculty 8 days before the dissertation presentation.

c) Committee Approval of Scheduled Defense: A draft of the dissertation should be distributed to the members of the dissertation committee no later than five weeks before the dissertation defense. The committee then has two weeks to approve that the student can reasonably expect to defend the thesis, and three more weeks to fully assess.

These rules delineate the minimum level of involvement of the dissertation committee. We strongly recommend that students set up their committees early and that they interact regularly with the members of their committees once they are established. We strongly recommend that those students wishing to complete all degree requirements, including their defense, by the end of Summer quarter contact their committee to schedule their Summer defense date before Summer Quarter begins. Else unanticipated committee requirements may lead to the degree being delayed to the Winter Quarter.

CONSULTING PROGRAM

The Department runs a consulting for training purposes, at the same time providing a service for researchers in other departments in the University. Students serve as the consultants, working as the quantitative expert in statistics alongside the researchers. Two faculty members lead the consulting program. The consulting seminar meets once a week for an hour during academic quarters. In these meetings researchers may present a problem, the students may present their projects, or some interesting applied case study may be analyzed. The students rotate weekly through consulting `office hours', which are the times when researchers can approach with their requests. Typically, four to six graduate students work together as a team under the supervision of faculty members to address these requests.  The teams share their experience by presenting their analysis to the seminar. Students are required to register for the consulting program for two quarters each of years 1 through 3. Third year students can delay one of their consulting quarters to their fourth year.

ACADEMIC YEAR STUDENT SUPPORT AND TEACHING DUTIES

PhD students are guaranteed support for five years and in return are required to work as teaching assistants (TAs) for two quarters of each year and on one quarter they are off. Incoming first year students  are all off during the first quarter. TA assignments are determined 3-4 weeks prior to the start of the quarter, at which point the students are required to contact the faculty member teaching the course for instructions on their upcoming duties. Students may request the DGA to assign them to particular courses, or ask to have a particular quarter off. There is no guarantee that these requests will be satisfied, but the DGA does take them into account. Students are not allowed to work as TA's for any other University unit during their off quarter.

Research Assistants (RAs): Faculty members may decide to support their student  from a grant in one or more of their teaching quarters. In those quarters the students are not required to perform TA duties. Students can receive RA support from faculty advisors outside the Department.

Instructorship: Some students may be asked to be instructors in introductory Statistics courses, especially during the Spring quarter. These students receive a bonus in their summer support (at time of writing, July 2022, this is 2000 USD). The DGA determines which students are suitable for such positions.

Sixth year students and beyond: Students who have not completed their dissertation by the end of the fifth year must, by the end of Spring Quarter, obtain permission from their committee and the Department Chair to continue beyond the fifth year. If they are allowed to continue but are not hired as RA’s they will be funded by the Department, but  required to teach every quarter. Students who have not completed their dissertation and defense by the end of the sixth year will no longer receive stipends or be employed by the Department. These students are required to petition the Department both in order to continue in the doctoral program and for any financial support (tuition, fees). This petition is to be made to both their committee and the Department Chair before the end of Spring Quarter of the sixth year.

Quarterly Funding Letters: A few weeks before every quarter the Student Affairs Specialist will send out the quarterly funding letter which will list each students’ position (TA, Instructor, RA, OFF) for the upcoming quarter. This letter will also list stipend or paycheck dates depending on the students’ position and an itemized amount of costs for the quarter and who is responsible for the payment. This letter is very important in that it will tell the student if they will hold a position that quarter and what date/dates they will be paid.  

SUMMER SUPPORT

Students are provided with full 3 month summer support during their first 4 years. Support during the fifth summer is contingent on approval of the advisor and the Chair.

Internships: Students can choose to take on internships during the summer, in which case they forfeit the departmental summer support. The decision on whether to take an internship and which ones are appropriate are taken in consultation with the student's thesis advisor. It is not recommended to take internships before finding a thesis advisor.

OFF CAMPUS WORK

Students in a full-time registration status are expected to focus their attention and efforts principally on their academic work and additional employment is secondary to their student status. A domestic student wanting to take off-campus employment will typically need to take a leave of absence from the program. For international PhD students, OIA recently introduced a version of CPT (CPT RCOT) that may allow them to work off-campus outside of summer. [see https://internationalaffairs.uchicago.edu/page/curricular-practical-training-cpt ]. However, this requires approval by the PSD Dean of Students, and will involve careful consideration of a number of factors Moreover, the Dean of Students views this mechanism as intended for only very short-term off-campus work (eg 1 quarter) and not for long term. Repeated enrollment in CPT RCOT will generally not be approved by the Dean of Students. Students who have questions about CPT RCOT should direct them to the PSD Dean of Students.

Note: work at Argonne national labs is excepted from usual "off-campus" regulations due to an agreement between Argonne and UChicago.

STUDENT REPRESENTATIVES

During the first week of Fall quarter the PhD students gather to elect a student representative(s), who are responsible for communicating with the DGA and the Chair regarding any issues arising among the student body. They are also asked on occasion to coordinate student social activities such as the annual picnic. The Departmental Student Affairs Specialist assists the student representatives with any administrative tasks associated with their duties.

STUDENT OFFICES

All keys for student offices will be given during orientation week in the Student Affairs office. 1st year PhD students will always have desks in Jones 208, 2nd-4th year students will have desks in Jones 203/204, and 5th and 6th year students will have desks in Jones 209. Students will sign a key check-out form which states they will be responsible for their desk key of $20 and the office key of $30 and if they lose the key they must pay the Department either of the amounts in order to obtain a new key.

REIMBURSEMENTS

Students who wish to travel throughout the year for conferences that are sponsored on a grant or research funds from their advisor can be reimbursed by the Student Affairs Specialist with detailed receipts and confirmation that this trip has been approved and sponsored by the advisor/faculty member.

In case the advisor is unable to support the student travel but still approves it, the student may petition the Department Chair for up to $1000 of Departmental support.

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 year (due May 1), students are expected to fill out the Annual Progress Review . 
• 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  

PhD in Statistics

The Doctor of Philosophy (PhD) program in Statistics prepares students for careers in industry, research institutions, and universities. The students initially undergo a sound training in theory and applied statistics, followed by advanced courses and dissertation research. Statistics faculty in the department work in diverse areas of research topics and are also actively involved in interdisciplinary research collaboration. This provides our PhD students a wide array of research topics to choose from for their PhD dissertation.

All PhD students have the opportunity to work as paid interns in the nearby industries and research institutions, such as Cincinnati Children's Hospital, Proctor & Gamble, and Medpace, after they finish a set of core applied statistics courses.  The internships are coordinated by the Department. The Department is consistently successful in placing most of our students with an internship of student’s interest. The internship offers the students experience in applying statistical methods they learn to real-world problems under the mentorship of experienced statisticians and researchers, while they pursue their PhD degree. Students also have the opportunity to choose a PhD dissertation research topic related to their internship work.  

The multifaceted training of our students, through course work, research, and the wide range of interdisciplinary experience through internship, sets our PhD program apart from other PhD programs in statistics. Our past students have had an extremely high success rate of employment in industries, research institutions, and universities after graduation.

Admission Requirements

Students applying for the program should have or be expecting to obtain a bachelor’s degree in statistics or a related area. Specifically, all students should have taken: 

• Three semesters of calculus up to and including multivariate calculus
• A semester course in linear algebra
• A semester course in calculus-based probability and statistics
• Courses in pure and applied statistics similar to STAT 6021-6022 (Mathematical Statistics I and II) and STAT 6031 (Applied Regression Analysis) and STAT 6032 (Design and Analysis of Experiments).

An official GRE score is required for admission.  This requirement is waived for UC undergraduates with a degree in a relevant field and a cumulative GPA of 3.5 or higher, for applicants with at least 6 graduate credits in a relevant field with a cumulative GPA of 3.2 or higher, or for applicants with numerous years of relevant work experience with demonstrated advancement.  A quantitative score of 160 or higher is recommended.

The English proficiency requirement is met for applicants with degrees earned in English from accredited universities and colleges in  the US or other English-speaking countries .

Financial Support

Most of our PhD students receive full financial support (tuition remission and a Graduate Assistantship, a fellowship, or an internship), and most are supported through their entire UC career.  Travel support is available for students to attend or present their work at conferences.  

All applicants for the PhD program are automatically reviewed for graduate assistantship eligibility at the time of application. 

• Financial aid opportunities for students in the Mathematical Sciences Department
• Tuition and fees for graduate and professional students

Application Instructions

Applicants will need to meet the minimum requirements to be considered for the program. Completed applications will be reviewed beginning February 1 . We will continue to receive applications until all positions are filled.

All application materials from international students requiring a US visa must be received prior to April 1 (but sooner is better) in order to allow time for the necessary paperwork to be processed. The visa application process can often take 90 days or more to complete.

How to apply: 

1. Create  an online application

2. Include these documents in your application:

• Three letters of recommendation. The application system will automatically send an email to each of the recommenders with a link to submit their letters. 
• Unofficial copy of transcript (official transcript will be required if you are admitted to the program). 
• GRE general test score
• Statement of purpose/cover letter 
• English Proficiency for international students.

3. Pay the application fee

UC’s CEEB college code is 1833, as established by The College Board . CEEB codes are used to ensure that test scores are sent to the correct institution. 

• More information about submitting your application materials
• FAQs for the admission process

Program Description

The credit-hour requirement includes a minimum of 90 graduate credits beyond the bachelor's degree or a minimum of 60 credits beyond a master's degree, including 7 hours in dissertation research, with a GPA of 3.3 or higher. 

All incoming PhD students are required to take the  qualifying exam before the beginning of their first semester. Students who do not pass this exam at the PhD level are placed in the appropriate 6000 - level courses. The Statistics Qualifying Exam is based on the two two-semester sequences Mathematical Statistics STAT6021-6022 and Applied Statistics STAT6031-6032.

All PhD students must pass the  preliminary examination  by the end of their second year.  The Statistics Preliminary Exam is based on the two courses Linear Models and Multivariate Analysis II STAT 7024 and Statistics Theory STAT 7031.

After the preliminary examinations, an advanced examination in the area of examination of the student is required. An advanced exam may either be a written exam, a presentation or a series of presentations. The exam will be administered by a committee. Generally, this committee will form the students’ dissertation committee.

Visit the curriculum guide to learn more about the required courses. More details concerning the requirements of the program are explained in the Mathematical Sciences Department’s Graduate Handbook . See the course descriptions for information on the content.

About Cincinnati

Cincinnati is a big city with a small-town feel. The cost of living is low, but the quality of life is high.  Forbes named Cincinnati the #5 most affordable city and the #9 best city for raising a family. Cincinnati has ranked the best place to live in Ohio by U.S. News & World Report, also the fourth-best city in the country for parks . UC is home to over 10,500 graduate students, 20% of which are international students.

• Why Cincinnati  
• Estimated living expenses  (for international students)

For further information, please contact the Graduate Program Director, Dr. Robert Buckingham:

• Email: [email protected]
• Phone: 513-556-4085

See the full list of our graduate programs

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Keyboard Shortcuts

Stat 500: applied statistics.

•   Overview
•   Materials
•   Assessment Plan
•   Prerequisites
•   Online Notes

This graduate level course provides an introduction to the basic concepts of probability, common distributions, statistical methods, and data analysis. It is intended for graduate students who have one undergraduate statistics course and who wish to review the fundamentals before taking additional 500 level statistics courses. This course is cohort-based, which means that there is an established start and end date, and that you will interact with other students throughout the course.

Upon completion of this course students will:

• Appreciate and understand the role of statistics in your own field of study.
• Develop an ability to apply appropriate statistical methods to summarize and analyze data for some of the more routine experimental settings.
• Make sense of data and be able to report the results in appropriate table or statistical terms for inclusion in your thesis or paper.
• Perform appropriate statistical techniques using Minitab and interpret the results/outputs.

Course Topics

This graduate level course covers the following topics:

• An overview of statistics
• Data description: scales of measurement, how to describe data graphically for categorical data (pie chart, bar chart) and graphs for quantitative variables (histogram, stem-and-leaf plot and time plot)
• How to describe data by summary statistics: measures of central tendency and variability
• How to create a box plot
• How to use a statistical package (Minitab)
• How probability and probability distributions are involved in statistics
• How binomial distributions are involved in statistics
• The role that normal distributions play in statistics
• Simple random sampling and sampling distribution of sample mean, central limit theorem, normal approximation to the binomial
• Differentiation between a population and a sample, how to use a statistic to estimate a population parameter, confidence interval and its interpretation, inferences of population proportion, margin of error and sample size computation
• Confidence interval for population mean, Sample size needed for estimating the population mean with a specified confidence level and specified width of the interval
• Hypothesis testing: in terms of how to set up Null and Alternative hypotheses, understanding Type I and Type II errors, performing a statistical test for the population mean
• How to compute power of a test and choosing the sample size for testing population mean
• p-value, how to compute it and how to use it
• Inferences about μ with σ unknown: the t-distribution and the assumptions required to check in order to use it
• How to compare the mean of two populations for independent samples: using pooled variances  t -test versus separate variances  t -test
• How to compare the mean of two populations for paired data
• How to compare two population proportions
• Using contingency table and the Chi-square test of independence
• Using an  F -test to compare the variances of two populations
• Understanding concepts related to linear regression models including, least squares method, correlation, Spearman's rank order correlation, inferences about the parameters in the linear regression model
• Analyzing data using analysis of variance (ANOVA) methods
• Analyzing data using multiple regression methods
•   Introductory Statistics
•   Applied Statistics
•   Data Analysis
•   Data Description
•   Summary Statistics
•   Probability
•   Confidence Intervals
•   Hypothesis Testing
•   p-values
•   Statistical Inference

Course Author(s)

Dr. Mosuk Chow is the primary author of these course materials with the most recent revision by Dr. Tracey Hammel.

This course will use the statistical software program Minitab. See the Statistical Software page  for more information.

  This course uses Honorlock for proctored exams. For more information view O.3 What is a proctored exam? in the student orientation.

A graphing calculator is recommended for this course, especially for students enrolled or considering the MAS program. Otherwise, a basic calculator that includes factorials and combinations will suffice. Please note that for the final exam using a calculator on a device with internet capabilities (e.g. cell phone) will NOT be permitted.

Ott, R. L., and Longnecker, M. (2016).   An Introduction to Statistical Methods and Data Analysis , 7th Edition, Cengage Learning. ISBN 13: 978-1-305-26947-7 or ISBN: 978- 0- 357- 67062- 0

Last updated: FA23

Assessment Plan

Homework:  Homework assignments will be submitted almost every week. Due dates will be specified in the course calendar.  Doing the homework promptly and carefully is necessary for learning the material. A reasonable amount of collaboration is allowed and encouraged on homework. However, each student must turn in his or her own written work which reflects his or her own understanding of the material. There is penalty for handing in homework late.

Assessments : 2 assessments / quizzes (short exams)

Mid-term Exams:  2 midterm exams 

Final Exam:  The final exam will be comprehensive.

PLEASE NOTE: This course may require you to take exams using certain proctoring software that uses your computer’s webcam or other technology to monitor and/or record your activity during exams. The proctoring software may be listening to you, monitoring your computer screen, viewing you and your surroundings, recording and storing any and all activity (including visual and audio recordings) during the proctoring process. By enrolling in this course, you consent to the use of the proctoring software selected by your instructor, including but not limited to any audio and/or visual monitoring which may be recorded.  Please contact your instructor with any questions .  ( Read more about proctoring... )

Prerequisites

1 undergraduate course in statistics

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Stanford Online

Medical statistics program.

Stanford School of Medicine , Stanford Center for Health Education

All Access Plan: $499 USD Per course $179 USD

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Medical statistics, a branch of biostatistics, is the science of collecting, summarizing, presenting, and interpreting data in relation to the medicine and health fields. Through its use in medical research and investigations, we can better understand health phenomena in our populations. By studying medical statistics, you will gain the statistical literacy needed to remain adept and adaptable in our ever-changing health industries. While the examples and applications are within the context of health and medicine, the statistical foundations you will gain can be applied to any industry.

• Analyze, interpret, describe, and visualize data
• Program in language R or SAS to graph your data
• Draw conclusions based on a sample or subset of data
• Apply the statistical methods you’ve learned to medical research
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Medical Statistics I: Introduction to Data Analysis and Descriptive Statistics

Medical Statistics II: Probability and Inference

Medical Statistics III: Common Statistical Tests in Medical Research

Flexible enrollment options, enroll in individual courses.

Pay as you go

$179 per required course 60 days to complete

View and complete course materials, video lectures, assignments and exams, at your own pace. You also get 60 days of email access to your Stanford teaching assistant.

All-Access Plan

One Year Subscription

View and complete course materials, video lectures, assignments and exams, at your own pace. Revisit course materials or jump ahead – all content remains at your fingertips year-round. You also get 365 days of email access to your Stanford teaching assistant.

Groups and Teams

Special Pricing

Enroll as a group or team and learn together. We can advise you on the best group options to meet your organization’s training and development goals and provide you with the support needed to streamline the process. Participating together, your group will develop a shared knowledge, language, and mindset to tackle the challenges ahead.

What Our Learners Are Saying

The Medical Statistics Program from Stanford School of Medicine is pitched perfectly to enable medical professionals to critically interpret the statistical analyses in research literature that many of us accept unquestioningly. Dr Sainani's rare ability to explain and her enthusiasm for the subject are complemented with real-world examples of medical studies that illustrate fundamental principles and clarify underlying concepts. The mathematical content is kept to a non-intimidating minimum, but there are optional lessons that encourage one to explore further. I unreservedly recommend this course as being very educative, engrossing and, yes, enjoyable.

Sushil D., Professor of Surgery

Teaching Team

Kristin Sainani

Epidemiology and Population Health

Kristin Sainani (née Cobb) is an associate professor at Stanford University and also a health and science writer. After receiving an MS in statistics and a PhD in epidemiology from Stanford University, she studied science writing at the University of California, Santa Cruz. She has taught statistics and writing at Stanford for more than a decade and has received several Excellence in Teaching Awards from the graduate program in epidemiology. Dr. Sainani writes about science and health for a range of audiences. She authored the health column Body News for Allure magazine for a decade. She is also the statistical editor for the journal Physical Medicine & Rehabilitation; and she authors a statistics column, Statistically Speaking, for this journal.

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Data Science Courses

Kickstart your career with our data science courses, designed for fresh graduates and working professionals like you. Learn in-demand skills from courses taught by global experts. Earn dual certificates from globally recognized universities and become industry-ready today!

• Learn online at your own pace with flexible learning options
• Stand out from the crowd with successful career transitions
• Empower your data science learning journey with a comprehensive curriculum

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Explore Data Science Courses

Build a strong foundation with the best data science courses to boost your career. You will get a top-notch learning experience with these Popular Data Science Certificate Programs. These programs are in collaboration with world-class universities.

Popular Data Science Certificate Programs

Applied Data Science Program

PG Program in Data Science and Business Analytics

Master of Data Science (Global) Program

Masters (ms) in data science in usa & germany (study abroad).

MS in Data Science Programme

Online masters in data science, form submitted successfully.

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Career Transitions in Data Science Courses

Kamini Sahu

Data Science

Business Development Executive

Dynamo Consulting Services Limited

Associate Business Analyst

Sneha Jaiswal

Teacher Partner

Data Processing Analyst

Abdul Rauf Mohammad

Product Owner

JPMorgan Chase & Co.

Vice President

Wells Fargo

Rajat Dekate

Entrepreneur

MMKR POLYPLAST PVT. LTD.

Dhivya Karthic

Education Program Manager

IIT Madras | IIM Bangalore

Deloitte Consulting

Rishi Tiwari

Assistant Manager

Zerodha Broking Limited

Data Analyst

Reliance Retail

Siddharth Shinde

Process Associate

Ajit Muthunarayanan S

Digital content associate

Business Analyst

Ajay Devdas Pananchikal

DATABASE ADMINISTRATOR

Larsen And Toubro Infotech

Product Analyst

Ann Maria John

Drug Safety Associate

IQVIA RDS (India) Private Limited

Akansha Pruthi

Research Analyst

Aditya Sabbisetti

Walt Disney

Senior System Engineer

Associate Consultant

Amulya Manne

SPS Associate - SME

Product specialist

Business Reporting Analyst

Abhishek Pal

Manager - Analytics and Market Research

Silvermine Group

Senior Analytics Advisor

Get to Know Our Data Science Courses

Introduction to data science courses, the best course for data science, data science course syllabus (curriculum), data science courses faculty, data science course subjects, universities offering data science courses, data science course eligibility, ut austin's data science courses offered in partnership with great learning, mit data science courses offered in partnership with great learning, what is the best data science course for a beginner in 2024.

The ever-growing importance of data in today’s business landscape makes Data Science courses a must for anyone looking to start or further their career. Data Science courses offer people the opportunity to learn the ins and outs of Data Mining, Modeling, and Analysis, giving them the skills they need to make data-driven decisions in their chosen field. 

Data Science courses help people to develop the skills and knowledge necessary to become successful professionals in this discipline. In addition to learning the theory behind Data Science, these courses also provide people with practical experience working with data. This experience is essential for people who want to pursue careers in Data Science, as it helps them to understand how to use data to solve real-world problems effectively.

In addition to the technical skills gained in these courses, people will also develop soft skills, such as problem-solving and critical thinking, which are essential in any business role. With the insights gained from these courses, businesses can make more informed decisions, leading to improved efficiency and profitability.

Anyone looking for a comprehensive introduction to Data Science will find plenty of courses to choose from. These courses from Great Learning cover everything from the basics of Statistical Analysis and Machine Learning to more specific topics like Data Visualization, Data Analytics, and Data Mining.

Types of Data Science Courses:

1. Data Science Online Courses

Learning Data Science online gives you the flexibility to learn at your own pace and fit your studies around your work and other commitments. You’ll also have access to a wealth of online resources, including video tutorials, lectures, and forums where you can ask questions and get feedback from fellow students and instructors.

Here is the list of Data Science courses offered online by Great Learning in Collaboration with Universities:

PGP - Data Science and Business Analytics

PGP - Data Science and Engineering (PGP-DSE)

Data Science and Machine Learning: Making Data-Driven Decisions

Master’s in Data Science

Applied Data Science Program - (International Program)

Master of Data Science (Global)

Data Analytics Essentials - UT Austin

Great Learning’s most popular Data Science courses include a Master’s Degree in Data Science , a PG Certificate in Data Science , a Professional Certificate , or taking Bootcamp courses in Data Science . It really depends on what you are looking for and what you hope to gain from pursuing Data Science. Below is the list of Data Science courses offered by Great Learning:

Practical Decision-Making Using Data Science

Data Analytics Essentials  

M.Tech in Data Science and Machine Learning (PES University)

MBA Degree Program (Shiv Nadar University, Delhi NCR)

Data Analytics Program - Great Learning Career Academy

The Data Science course syllabus at Great Learning provides the skills you need to become a successful Data Scientist, Data Analyst, Business Analyst, Data Engineer, and many more. You will learn the fundamental Data Science concepts, tools, and techniques. You will learn to use Python, SQL, and R for Data Analysis and visualize and interpret data for gathering insights. The course covers Regression and Prediction, Machine Learning techniques, and much more. You will also complete Capstone Projects to hone your practical skills and implement them in the industry.

The faculty team of our Data Science courses comprises top academicians in Data Science, along with numerous other skilled industry practitioners from leading universities and organizations that practice this cutting-edge domain.

Before enrolling, it is usually advised to thoroughly understand the course syllabus, enabling you to determine whether the course equips you with the required skills. The subjects involved in Data Science are as follows:  

If you’re looking to learn Data Science from the world’s top-ranked universities, you’ve come to the right place. The courses are offered by:

Massachusetts Institute of Technology - Institute for Data, Systems, and Society (MIT IDSS)

The University of Texas at Austin (UT Austin) McCombs School of Business

Northwestern University School of Professional Studies

Deakin University

Great Lakes Executive Learning

With multiple options available, you’re sure to find a course that meets your requirements.

Below are the  eligibility requirements  for a range of data science courses:

*Please visit the respective program page for detailed information on the eligibility criteria.

UT Austin's Data Science Courses Offered in Partnership with Great Learning.

In collaboration with Great Learning, the University of Texas at Austin offers a range of comprehensive online data science certificate courses. These programs are designed for professionals and are tailored to accelerate your professional growth. Depending on the program format, these programs provide a data science certificate online and offline. 

These programs are designed to be flexible and accessible, offering mentored online learning experiences that cater to mid-career professionals. They provide a blend of theoretical knowledge and practical skills, preparing you for the evolving demands of the tech industry.

MIT Data Science Courses Offered in Partnership with Great Learning.

Massachusetts Institute of Technology (MIT), in partnership with Great Learning, offers a variety of in-depth online postgraduate certificate programs specifically designed for professionals seeking to enhance their careers. These programs emphasize key areas in data science and technology, ensuring participants are well-equipped for the dynamic demands of the industry. 

These MIT programs, facilitated through Great Learning, are structured to offer flexibility and convenience, making them ideal for mid-career professionals. The courses blend theoretical insights with practical applications, preparing participants for the rapidly evolving landscape of the data science and technology sectors.

For a fresher in 2024, there are several top-notch data science courses offered by Great Learning. They are listed below:  

Data Analytics Course for Beginners  - The University of Texas, Austin (For Non- Indian learners)

PGP in Data Science (Online)  - Great Lakes Executive Learning (For Indian Learners Only)

PGP in Data Science (Bootcamp)  - Great Lakes Executive Learning (For Indian Learners Only)

Things You Want To Know About Data Science Courses

Data science course fees, data science course syllabus, data science course with placement, data science certificate course, data science course reviews, data analytics courses, masters in data science courses, data science course faculty.

Learn from the vast knowledge of top faculty in the field of Data Science.

Prof. Dan Mitchell

Phd (University of Maryland)

Clinical Assistant Professor

Dr. Abhinanda Sarkar

Ph.D. from Stanford University, Ex-Faculty - MIT

Faculty Director, Great Learning

Dr. Kumar Muthuraman

PHD (Stanford University)

Faculty Director, Centre for Research and Analytics

Devavrat Shah

Professor, EECS and IDSS, MIT

Munther Dahleh

Program Faculty Director, MIT Institute for Data, Systems, and Society (IDSS)

Ryley Bauer

Chief Automation Officer - Bauer Automate

Bradford Tuckfield

Founder, KMBARA

Dr. Jones Mathew

Former VP Sales & Marketing

John N. Tsitsiklis

Clarence J. Lebel Professor, Dept. of Electrical Engineering & Computer Science (EECS) at MIT

Dr. P K Viswanathan

MBA (FMS, Delhi)

Professor, Analytics & Operations

Hossein Kalbasi

Senior Data Scientist,Mercator AI

Georg Huettenegger

Mentor, Great Learning

Ph.D. from Stanford University

Caroline Uhler

Henry L. & Grace Doherty Associate Professor, EECS and IDSS, MIT

Ankur Moitra

Rockwell International Career Development Associate Professor, Mathematics and IDSS, MIT

Mr. R Vivekanand

MBA (Monash University)

Operations Director

Dr. Sutharshan Rajasegarar

Senior Lecturer in Computer Science Course Director Master of Data Science

Senior AI Scientist

Prof. Prashant Koparkar

Corporate Trainer and Consultant - Machine Learning

Prof. Raghavshyam Ramamurthy

MBA (Whitman School of Management)

Industry Expert in Visualization

Denver Dias

Senior Data Science Consultant

Saurabh Kango

Linkedin insights - Program Manager

Dr. Srabashi Basu

Professor, PhD Statistics

Stefanie Jegelka

X-Consortium Career Development Associate Professor, EECS and IDSS, MIT

David Gamarnik

Nanyang Technological University Professor of Operations Research, Sloan School of Management and IDSS, MIT

Udit Mehrotra

Data Scientist

Jitendra Gopaluni

Data Solutions Architect,Veterans United Home Loans

Prof. Snehamoy Mukherjee

Masters (IIT Kanpur)

Adjunct Faculty

Dr. Bappaditya Mukhopadhyay

Ph.D (Indian Statistical Institute)

Co-Director, Gurgaon, Professor - Analytics & Statistics, Great Lakes Institute of Management

Dr. C P Gupta

Dr. Tom Miller

Faculty Director

Dr. D Narayana

PHD (Pierre & Marie Curie University, Paris)

Professor, Artificial Intelligence and Machine Learning, Great Learning

Guy Bresler

Associate Professor, EECS and IDSS, MIT

Senior Lecturer, Computer Science

Jonathan Kelner

Professor, Mathematics, MIT

Prof. Dipayan Sarkar

Master in Economics (Karnatka University)

Consultant, Author, Visiting Faculty

Kalyan Veeramachaneni

Principal Research Scientist at the Laboratory for Information and Decision Systems, MIT.

Dr. Bahareh Nakisa

Senior Lecturer, Applied Artificial Intelligence

Philippe Rigollet

Professor, Mathematics and IDSS, MIT

Dr. Amit Sethi

PHD (University of Illinois at Urbana-Champaign)

Faculty, IIT Bombay

Mr. Gurumoorthy Pattabiraman

MSc (Madras School of Economics)

Faculty, Data Science & ML, Great Learning

Dr. Asef Nazari

Senior Lecturer in Mathematics for Artificial Intelligence

Dr. Rohit Kapoor

PHD (IIM Ahmedabad)

Associate Professor

Tamara Broderick

Associate Professor, EECS and IDSS, MIT.

Prof. Mukesh Rao

PGDBA (SIMS)

Director- Data Science

Prof. Saurabh Aggarwal

B Tech (IIT, Delhi)

Professor, HBTI Kanpur

Victor Chernozhukov

Professor, Economics and IDSS, MIT

Professor,School of Info Technology

Mr. Krishna Mohan

MBA(Universtity of Cincinatti, Ohio)

Sr. Manager Technology

Mr. Vinit Thakur

M.S (University of Mumbai)

Visiting Faculty

Dr. Marek Gagolewski

Maia Angelova Turkedjieva

Professor, Real-World Analytics

Data Science Course - Industry Mentors

Learn from highly skilled industry practitioners working with top-notch companies. Stay ahead of the curve and ensure you are learning the most relevant data science skills.

Serdar Cellat

Nitish Jaipuria

Weibiao (Wilson) Huang

Analytics Consultant

Boston Consulting Group (Singapore)

Deputy Manager - Business Analytics & Business Intelligence

Keppel Corporation Limited (Singapore)

Dr. Satish Raghavendran

Vaibhav Verdhan

Phumzile Phantsi

ABSA Group (South Africa)

Adaikalavan Ramasamy

Senior Research Scientist

Genome Institute of Singapore (GIS)

Wole Ogungbesan

Director - Advance Analytics & Automation

UBS (United Kingdom)

Prabhat Bhattarai

Mr. Bradford Tuckfield

Mr. Manish Gupta

Mr. V Shekhar Avasthy

Nikhila Kambalapalli

Ms. Mayan Murray

Mr. Sreevasan P S

Mr. Balaji Sundararaman

Srihari Nagarajan

Mr. Vibhor Kaushik

Mr. Udayakumar Devaraj

Olayinka Fadahunsi

Mr. Amit Agarwal

Fahad Akbar

Mr. Kemal Yilmaz

Anis Sharafoddini

Ms. Xiaojun Su

Avinash Ramyead

Shannon Schlueter

Mr. Juan Castillo

Edward Krueger

Mr. Andrew Marlatt

Paolo Esquivel

Mr. Rohit Dixit

Michael Keith

Kalle Bylin

Mr. Srikanth Pyaraka

Yogesh Singh

Mr. Angel Das

Rushabh Shah

Mr. Shirish Gupta

Marco De Virgilis

Mr. Vanessa Afolabi

Roshan Santhosh

Mr. Thinesh Pathmanathan

Mr. Grivine Ochieng

Top Data Science Projects Done By Our Learners

Check out our learners' enthusiastic, career-transitioning projects and become a highly skilled Data Science professional.

Airplane Passenger Satisfaction Prediction

Entertainment

Movie Lens Data Exploration

Social Media

Facebook Comments Prediction

Insurance claim prediction.

Social + Healthcare

West Nile Virus Prediction

Network congestion type prediction, insurance premium default propensity prediction.

Sales & Marketing

Forecasting Monthly Sales of French Champagne

Retail sales prediction, covid-19 global forecasting, loan customer identification, product recommendation system, ceo compensation.

Object Detection

Face Mask Segmentation

Insurance data visualization.

News & Media

Sarcasm Detection

User testimonials of our data science courses.

Explore learner’s testimonials from real people who transformed their careers with our Data Science courses.

Mauricio De Garay

MIT Professional Education's Applied Data Science Program

Berthy Buah

Chun Wing Ip

Kelechi Enyioha

Post Graduate Program in Data Science and Business Analytics

Brooks Christensen

Mohammed majdy, perci olarte, flor de maría gómez esparza.

Leanne Da Cerca

Monica Suarez

Webinars on data science.

With our webinars, learn from leading experts in Data Science. Gain insights and strategies for your career success in the data science domain.

Introduction to Supervised Learning and Regression

12 june 2024 | 06:00pm cdt, matthew nickens.

Senior Manager, Data Science, CarMax

REGISTER NOW

Introduction to python for data science, 13 june 2024 | 06:00pm cdt, davood wadi.

AI Research Scientist, intelChain

Exploratory Data Analysis: Uncovering Patterns in Data

13 june 2024 | 07:30pm cdt, mr. saurabh kango.

Insights Program Manager, LinkedIn

Your Options in the GPT Universe

15 june 2024 | 10:00am cdt, mr. davood wadi, engineering data science on the cloud, 22 june 2024 | 10:00am cdt, mr. sameer sharma.

Director, Data Science - Bank Of America

Admission Session on PGP-DSBA Program

15 july 2021 | 11:00am cdt, mr. milind kopikare.

President, Great Learning, North America

VIEW RECORDING

Ask me anything | becoming a data driven leader with pgp-dsba - jamie l fairclough, 17 august 2021 | 11:00am cdt, ms. jamie fairclough.

Associate Dean and Professor at Roseman University College of Medicine

Ask me anything - Session with Applied Data Science Program Alumnus: Pradeep Podila

24 august 2021 | 11:00am cdt, mr. pradeep podila.

Health Scientist - Senior Service Fellow, CDC

Ask Me Anything | Becoming a data driven leader with PGP-DSBA - Sarah Bittner

21 september 2021 | 11:00am cdt, ms. sarah bittner.

Patent Agent with Montgomery McCracken Walker & Rhoads LLP

Workshop on Fraud Detection with Data Science | Tasleem Ahmed

08 november 2022 | 11:00am cst, tasleem ahmed.

Data Scientist, Pacific Blue Cross

[Workshop] Predicting Health Insurance Prices with Data Science by Eren Zedeli

13 december 2022 | 02:00pm cst, eren zedeli.

Data Scientist, imidia, USA

[Masterclass] Using Data to Optimize Delivery Operations for Your Business

16 december 2022 | 11:00am cst, kshitij srivastava.

Director of Technology, Milliman

Improving Customer Retention with Data Science by Dale Seema

22 december 2022 | 10:00am gmt.

Senior Data Scientist, Vodafone, Nigeria

[Masterclass] Usando la Ciencia de Datos para Tomar Decisiones Basadas en Datos

18 january 2023 | 11:00am cst, jabes rivera.

Sr. Machine Learning Engineer, Clivi Health

Predicting Flight Fares with Data Science

25 january 2023 | 10:00am gmt.

Head of Data Science and Engineering, Equity Bank Limited

[Masterclass] Transicionando a una carrera en Ciencia de Datos en 2023

23 february 2023 | 04:00pm cst, vinicio de sola.

De datos sénior en Aspen Capital

Transformadores e Ingeniería de Prompts: la nueva era de IA

22 march 2023 | 07:00pm cst, juan carlos medina serrano.

Senior Data Scientist, Nubank

[Workshop] Everything you need to know about ChatGPT

28 march 2023 | 11:00am cdt, aishwarya krishna allada.

Data Scientist at Cisco, Canada

[Workshop] Python Fundamentals with Tasleem Ahmed | Exclusive Data Science Masterclass

29 march 2023 | 08:00pm cdt, tasleem ahmad, tesla's autopilot: ai for safer and smarter driving, 08 august 2023 | 11:00am cdt, matthew graziano.

Senior Data Science Manager, Progressive Insurance

Uber's Surge Pricing: How AI Adjusts Prices Based on Demand

10 august 2023 | 11:00am bst.

Data Science Consultant, IQVIA Asia Pacific

Airbnb's Dynamic Pricing: AI for Hosts and Guests

23 august 2023 | 11:00am cdt.

Actuarial Data Scientist Manager, Arch Insurance Group Inc.

Accelerate your Analytics and Data Science Career with PL-300 Certification

24 september 2023 | 09:30am cdt, mr. srivaths kumar.

Lead Analytics Engineer: Great Learning

Read these reviews from our real learners and their experience with our courses. This will help you choose the right course for yourself.

Program : Post Graduate Program in Data Science and Business Analytics

Program : Post Graduate Program in Data Science and Engineering

Data Science Course Frequently Asked Questions

What is a data science course.

The Data Science course is a fine blend of mathematics, statistical foundations and tools, and business acumen, all of which assist in extracting from raw data the hidden patterns or insights that can significantly aid in formulating essential business decisions. Proving prevalent in academics, Business Analytics courses are now an amalgamate of Data Science.

The major components of the course also include scientific computing, data structures and algorithms, data visualization and data analysis, and machine learning tools and techniques to escalate business performance.  The course could be around six to twelve months, designed to give candidates a solid foundation in the discipline. In addition to educational materials, our Data Science certificate courses contain virtual laboratories, interactive quizzes and assignments, case studies, industrial projects, and capstone projects, which will accelerate your learning path.

What is the duration of the best Data Science programs?

The duration of Data Science programs at Great Learning varies from one program to another depending on the curriculum, institute, and so on. They range from 10 weeks to 6 months.

View all Data Science programs for the course duration.

What is the course fee for Data Science programs at Great Learning?

Like the course duration, the fee structure also varies from one program to another depending on the curriculum, institute, and so on.

View all Data Science courses for the fee structure.

Who would be the faculty teaching these online Data Science courses?

World-class, renowned academicians from the respective institutions would teach the online Data Science courses.

What are some of the best Data Science courses for students and working professionals?

All the Data Science programs from Great Learning are preferable for working professionals.

However, for students, we have designed the Post Graduate Program in Data Science and Business Analytics, where students learn the foundational skills of Data Science and Analytics. The curriculum covers Python programming language, Data Visualization, Data Analysis, Machine Learning techniques, Business Analytics skills, and much more.

How are the reviews of Data Science courses from Great Learning?

All our courses are top-class, acknowledged by several business leaders and top magazines. Most of our learners have accomplished successful career transitions into Data Scientist or Data Analyst roles.

Check out our learners’ testimonials who have completed our online Data Science courses.

Why should I opt for the online Data Science courses from Great Learning?

Great Learning and some of the world’s most reputed institutes have collaborated to offer learners the best Data Science programs in the industry, enabling them to gain a good understanding of it.

These Data Science programs offer numerous benefits, such as:

• Customized Learning Journey
• Hands-on Experiential Learning with Real-World Projects
• Dedicated Program and Career Support
• Result-Oriented Curriculum
• Cohort-Based Pedagogy
• Learning Analytics
• First-Class Faculty and Mentors

Numerous other benefits are offered by Great Learning. Check out our Data Science courses for in-depth information.

What certifications will I receive after completing the online Data Science course?

You will receive certifications from the respective institutes, depending on your chosen course, such as UT Austin for Data Science and Business Analytics, MIT PE for Applied Data Science Program, etc.

What are some of the best Data Science courses available in the market?

Some of the best online Data Science courses available in the market are offered by Great Learning, in collaboration with some of the world’s renowned institutes. The programs are as follows:

• Data Science and Business Analytics - The University of Texas at Austin
• Applied Data Science Program - MIT Professional Education
• Practical Decision Making using Data Science - National University of Singapore
• Data Science and Machine Learning: Making Data-Driven Decisions - MIT IDSS

What is the salary of a data scientist fresher across the world?

With the increase in the demand for data scientists across the globe, salaries are also skyrocketing. Data scientists are making generous pay from top-notch companies. Since there is a lack of data science professionals in the field, even freshers are earning excellent salaries.

The following are a few salaries of a data scientist fresher from different countries:

Data scientist salary in the United States : USD 101K per annum

Data scientist salary in the United Kingdom : £55K per annum

Data scientist salary in India : ₹10.5 Lakh per annum

Data scientist salary in Germany : €55K per annum

Data scientist salary in Australia : AU$121K per annum

What are the career options in data science?

Choosing a job opportunity in data science gives you a lot of career options:

Data Scientist:  Responsible for analyzing and interpreting complex data to help inform business decisions.

Data Analyst:  Focuses on processing and performing statistical analyses on large datasets.

Machine Learning Engineer:  Specializes in designing and implementing machine learning techniques, models and systems.

Data Engineer:  Focuses on the preparation of 'big data' for analytical or operational uses.

Business Intelligence (BI) Analyst:  Uses data to help organizations make better business decisions.

Data Science Manager/Lead:  Ensures meeting organizational goals with various data science teams and projects.

Research Scientist:  Engages in data-driven research, often in academic, government, or corporate settings.

Statistician:  Applies statistical methods to collect, analyze, and interpret data to solve real-world problems in business, engineering, healthcare, or other fields.

What is the demand for data science jobs in 2024?

The demand for data science jobs is extremely high, and data is considered the new oil in today's digital economy. Companies across industries are seeking professionals who can interpret and analyze this data to provide business insights. Therefore, the demand for data science skills, including machine learning, predictive analytics, and data visualization, is rising significantly.

What is the future scope for data scientists?

The future scope for data scientists is promising. With the advent of AI and machine learning, companies in various sectors, such as healthcare, finance, retail, and e-commerce, are increasingly leveraging data to make informed business decisions, resulting in a growing demand for data scientists. 

As per the U.S. Bureau of Labor Statistics, the future for data scientists is highly promising, with a  35%  growth in employment from 2022 to 2032, which is higher than all the other occupations.

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