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Our concentrations, data science, what is data science.

As a discipline, statistical science emerged from various scientific domains, established foundations and developed structure through mathematics, and has evolved to become inseparable from scientific computation, machine learning and computer science. Training in Data Science, as well as the field itself, captures all aspects of this evolution and makes it experiential for the trainee. The field of Data Science has emerged as a response to our increasing, and relentless, ability to generate data, particularly on a massive scale, and our reliance upon it in nearly every facet of human endeavour. The importance of the field has led to entirely new type of professional – the Data Scientist. 

The Data Science concentration, and the  Artificial Intelligence  concentration have both been recognized by Forbes as “ The 10 Best Artificial Intelligence and Data Science Master’s Courses ”. 

The Data Science concentration offers students an advanced understanding of statistical and computer science methods, vigorous research training and the opportunity to test your knowledge in the real world through our applied research internship.

Endless Career Opportunities

Discover the endless possibilities to accelerate your career as a world-class innovator.

Career Opportunities in Data Science

AI Research Scientist

Associate, Investment Science

Big Data Engineer

Data Engineer

Data Scientist

Machine Learning Research Scientist

Senior Data Scientist

Research Data Scientist

NLP Data Scientist

Manager – Machine Learning

Program Requirements

• One course (0.5 FCE) chosen from the Department of Statistical Sciences course schedule. These must be STA-2000 level courses or higher. The course selection may also include a maximum of two courses (0.5 FCE) chosen from the STA-4500 level of six-week modular courses (0.25 FCE each)
• One course (0.5 FCE) chosen from the four approved Statistical Science courses. STA2101H Methods of Applied Statistics I, STA2102H Computational Techniques in Statistics, STA2311H Advanced Computational Methods for Statistics I, STA2312H Advanced Computational Methods for Statistics II
• Two courses (1.0 FCEs) chosen from the Department of Computer Science’s (CSC designator) course schedule.
• Two required courses (1.0 FCEs):  Communication for Computer Scientists  ( CSC 2701H ) and  Technical Entrepreneurship  ( CSC 2702H ).
• An eight-month industrial  internship , CSC 2703H (3.5 FCEs). The internship is coordinated by the department and evaluated on a pass/fail basis. ‘Pass’ grades are awarded based on evaluations received from the industry/academic supervisors of the internship project and submission of an appropriately written final report, documenting the applied research internship.

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Statistical sciences, statistical sciences: introduction, faculty affiliation.

Arts and Science

Degree Programs

Financial insurance.

• Statistical Theory and Applications;
• Probability
• Probability;
• Actuarial Science and Mathematical Finance

Statistical Sciences involves the study of random phenomena and encompasses a broad range of scientific, industrial, and social processes. As data become ubiquitous and easier to acquire, particularly on a massive scale, and computational tools become more efficient, models for data are becoming increasingly complex. The past several decades have witnessed a vast impact of statistical methods on virtually every branch of knowledge and empirical investigation.

Please visit the departmental website for details about the fields offered, the research being conducted, and the courses. The department offers substantial computing facilities and operates a statistical consulting service for the University's research community. Programs of study may involve association with other departments such as Astronomy and Astrophysics, the Dalla Lana School of Public Health, the Faculty of Information, Mathematics, Philosophy, Psychology, Sociology, the Rotman School of Management, and the School of the Environment. The Department of Statistical Sciences maintains an active seminar series and strongly encourages graduate student participation.

Students may be interested in the Data Science concentration within the Master of Science in Applied Computing program .

Contact and Address

Mfi program.

Web: www.mfi.utoronto.ca Email: [email protected] Telephone: (416) 978-7420

Department of Statistical Sciences Faculty of Arts & Science, University of Toronto Ontario Power Building, 700 University Avenue, 9th Floor Toronto, Ontario M5G 1Z5 Canada

MSc and PhD Programs

Web: www.statistics.utoronto.ca Email: [email protected]

Statistical Sciences: Graduate Faculty

Full members, members emeriti, associate members, statistical sciences: financial insurance mfi, master of financial insurance, program description.

The MFI is a full-time professional program based on three pillars: data science, financial mathematics, and insurance modelling. This program is appropriate for students with backgrounds in statistics, actuarial science, economics, and mathematics. Students with a quantitative background (such as physics and engineering) and sufficient statistical training are also encouraged to apply.

Minimum Admission Requirements

Applicants are admitted under the General Regulations of the School of Graduate Studies. Applicants must also satisfy the Department of Statistical Sciences' additional admission requirements stated below.

An appropriate bachelor’s degree from a recognized university in a related field such as statistics, mathematics, finance, and actuarial science, or any discipline where there is a significant quantitative component. Studies must include significant exposure to statistics, mathematics, finance, and actuarial science, including coursework in advanced calculus, computational methods, linear algebra, probability, and statistics.

An average grade equivalent to at least a University of Toronto B+ in the final year or over senior courses; applicants who meet the SGS grade minimum of mid-B and demonstrate exceptional ability through appropriate workplace experience will be considered.

Three letters of reference including two academic references, one of which should be in a quantitative discipline.

A curriculum vitae detailing the student’s educational background, professional experience, and skills.

Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English using one of the official methods outlined in the SGS Calendar .

Selected applicants may be required to attend an interview.

Admission to the program is competitive, and achievement of the minimum admission standards does not guarantee admission into the program.

Program Requirements

Students must successfully complete 5.5 full-course equivalents (FCEs) as follows:

Eight required half courses (4.0 FCEs).

STA2546H Data Analytics in Practice (0.25 FCE).

Any one of Statistical Sciences’ 0.25 FCE 4000-level graduate course offerings with significant financial, insurance, or data science components, with approval of the MFI program director.

STA2560Y Industrial Internship , a four-month summer internship (1.0 FCE). Students must submit a project proposal to the program director and select an advisor by May 15. An interim report is required by July 7. Students must prepare a final written report and deliver an oral presentation on the internship project at the conclusion of the internship.

Required Courses

Fall session, winter session, summer session.

+ Extended course. For academic reasons, coursework is extended into session following academic session in which course is offered.

Program Length

3 sessions full-time (typical registration sequence: F/W/S)

3 years full-time

Statistical Sciences: Statistics MSc

Master of science.

Students in the MSc program can conduct research in the fields of 1) Statistical Theory and Applications or 2) Probability. The program offers numerous courses in theoretical and applied aspects of Statistical Sciences, which prepare students for pursuing a PhD program or directly entering the data science workforce.

The MSc program can be taken on a full-time or part-time basis. Program requirements are the same for the full-time and part-time options.

Fields: 1) Statistical Theory and Applications; 2) Probability

Admission to the MSc program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies. Admission requirements for the Statistical Theory and Applications field and the Probability field are identical. Successful applicants have:

An appropriate bachelor's degree from a recognized university in a related field such as statistics, actuarial science, mathematics, economics, engineering, or any discipline where there is a significant quantitative component. Studies must include significant exposure to statistics, computer science, and mathematics, including coursework in advanced calculus, computational methods, linear algebra, probability, and statistics.

An average grade equivalent to at least a University of Toronto mid-B in the final year or over senior courses.

Three letters of reference.

A curriculum vitae.

Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English. See General Regulations section 4.3 for requirements.

Both the Statistical Theory and Applications field and the Probability field have the same program requirements. All programs must be approved by the Associate Chair for Graduate Studies.

Students must complete a total of 4.0 full-course equivalents (FCEs), of which 2.0 must be chosen from the list below:

STA2101H Methods of Applied Statistics I

STA2201H Methods of Applied Statistics II

STA2111H Probability Theory I

STA2211H Probability Theory II

STA2112H Mathematical Statistics I

STA2212H Mathematical Statistics II

The remaining 2.0 FCEs may be selected from:

Any Department of Statistical Sciences 2000-level course or higher.

Any 1000-level course or higher in another graduate unit at the University of Toronto with sufficient statistical, computational, probabilistic, or mathematical content.

One 0.5 FCE as a reading course.

One 0.5 FCE as a research project.

A maximum of 1.0 FCE from any STA 4500-level modular course (each are 0.25 FCE).

All programs must be approved by the Associate Chair for Graduate Studies. Students must meet with the Associate Chair to ensure that their program meets the requirements and is of sufficient depth.

Part-time students are limited to taking 1.0 FCE during each session. In exceptional cases, the Associate Chair for Graduate Studies may approve 1.5 FCEs in a given session.

3 sessions full-time (typical registration sequence: F/W/S); 6 sessions part-time

3 years full-time; 6 years part-time

Statistical Sciences: Statistics PhD

Doctor of philosophy.

Students in the PhD program can conduct research in the fields of 1) Statistical Theory and Applications or 2) Probability or 3) Actuarial Science and Mathematical Finance. The research conducted in the department is vast and covers a diverse set of areas in theoretical and applied aspects of Statistical Sciences. Students have the opportunity to work in multidisciplinary areas and team up with researchers in, for example, Biostatistics, Computer Science, Economics, Engineering, and the Rotman School of Management. The main purpose of the program is to prepare students for pursuing advanced research both in academia and in research institutes.

Applicants may enter the PhD program via one of two routes: 1) following completion of an appropriate master’s degree or 2) direct entry after completing an appropriate bachelor’s degree (excluding Actuarial Science and Mathematical Finance).

PhD Program

Admission to the PhD program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies.

Applicants may be accepted with a master's degree in statistics from a recognized university with at least a B+ average. Applicants with degrees in biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component will also be considered.

Three letters of recommendation.

A letter of intent or personal statement outlining goals for graduate studies.

Course Requirements

During Year 1, students must successfully complete a total of 3.0 full-course equivalents (FCEs) as follows:

STA3000Y Advanced Theory of Statistics (1.0 FCE)

     and two of the following:

STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II (1.0 FCE)

STA2111H Probability Theory I and STA2211H Probability Theory II (1.0 FCE)

STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II (1.0 FCE).

Courses must be chosen in consultation with the advisor and approved by the Associate Chair of Graduate Studies.

Comprehensive Examination Requirements

Within Years 1 and 2, students must complete a two-part comprehensive examination: 1) an in-class written comprehensive exam and 2) a research comprehensive exam.

Students must attempt the in-class written comprehensive by the end of Year 1. If a student fails this portion of the comprehensive exam, one further attempt will be allowed by the end of Year 2. Students who achieve A or A+ grades in all required coursework are exempt from the in-class written exam.

Students must attempt the research comprehensive exam by the beginning of Year 2, which includes a technical report and an oral presentation. If a student fails this portion of the comprehensive exam, one further attempt will be allowed at the end of Year 2.

Students must pass both the in-class written exam and the research exam to continue in the program.

Thesis Requirements

Conducting original research is the most important part of doctoral work. The thesis document must constitute significant and original contribution to the field. Students will have yearly meetings with a committee of no less than three faculty members to assess their progress. The completed thesis must be presented and defended within the Department of Statistical Sciences in addition to being presented and defended at the School of Graduate Studies.

Residency Requirements

Students must also satisfy a two-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

PhD Program (Direct-Entry)

Applicants may be accepted via direct entry with a bachelor's degree in statistics from a recognized university with at least an A– average. The department also encourages applicants from biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component.

Students must successfully complete a total of 5.0 full-course equivalents (FCEs) as follows:

Year 1: complete 3.0 FCEs:

STA3000Y Advanced Theory of Statistics (1.0 FCE) and two of the following:

Complete an additional 2.0 FCEs at the graduate level. The additional courses must be approved by the Associate Chair of Graduate Studies.

Students must also satisfy a three-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

Field: Actuarial Science and Mathematical Finance

During Year 1, students must complete the following 3.0 full-course equivalents (FCEs) :

(1.5 FCEs) All of:

STA2111H Probability Theory I ,

STA2211H Probability Theory II , and

STA2503H Applied Probability for Mathematical Finance .

(0.5 FCE) One of:

STA2501H Advanced Topics in Actuarial Science or

STA4246H Research Topics in Mathematical Finance .

(1.0 FCE) One of:

STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II or

STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II or

STA3000Y Advanced Theory of Statistics .

Statistical Sciences: Statistics MSc, PhD Courses

The department offers a selection of courses each year from the following list with the possibility of additions. The core courses will be offered each year. Consult the department for courses offered in the current academic year .

Note: The following modular courses are each worth 0.25 full-course equivalent (FCE) .

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Main Content

Operations Management and Statistics

Our strength: our program.

The Rotman PhD Program in Operations Management and Statistics is designed to prepare students for academic and research careers in universities and industry. Our faculty includes PhDs from Columbia, Indiana, John Hopkins, MIT, and Stanford, and their interests include Supply Chain Management and Logistics, OM in Services, Inventory Management, Call Centre Management, OMS/Marketing Interface, and other areas.

Strengths of the Program

Operations Management and Statistics involves the study of management concerns related to the design, decision-making, and implementation of operating systems. The Rotman PhD Program in Operations Management and Statistics is designed to prepare students for academic and research careers in universities and industry.

Admission to the Program

Operations Management and Statistics is looking for students with a degrees in engineering, mathematics, computer science, natural sciences or business. Admissions are highly competitive and are based on transcripts, letters of reference, and standardized test scores.

PhD Courses

Operations Management and Statistics offers courses providing an in-depth analysis of a variety of topics, including; theory of production planning and control, inventory theory, logistics, modelling service operations, and facility location.

Program Structure and Requirements

The PhD program in Operations Management and Statistics is a strenuous academic exercise, providing effective training for an academic career. Our program includes required courses, a research project, comprehensive examinations and a final dissertation.

Current PhD Students

Meet the current students in the Operations Management and Statistics area at the Rotman School, and get a glimpse into how they are pushing the boundaries of research and knowledge in this exciting field.

The Operations Management and Statistics faculty at the Rotman School includes PhDs from Columbia, Indiana, John Hopkins, MIT, and Stanford, and their interests cover such areas as supply chain management and logistics, OM in services, inventory management, call centre management and OMS/marketing interface.

Faculty by Research Focus

As a PhD student you will work closely with faculty that share your research interests, are working on similar areas, or can provide a new perspective. Here find out what our faculty members are currently working on.

Dalla Lana School of Public Health

• PhD: Epidemiology
• Our Programs
• Doctor of Philosophy (PhD)

Degree Overview

This program aims to develop excellent epidemiologists, able to work, teach and conduct research on contributors to health; disease, disability and death; and effective measures of prevention.

The overall goal of the program is to enable graduates to acquire the necessary scientific knowledge and methodological skills to become independent researchers in epidemiology.  Graduates with a PhD in epidemiology are expected to have developed the skills which enable them to:

• evaluate the scientific literature with respect to epidemiologic concepts, theoretical hypotheses, designs, methods, analyses and interpretation;
• develop theoretical formulations and testable hypotheses from concepts in the literature or epidemiological observations, and propose research questions and design and write research proposals;
• understand the practical and scientific implications of epidemiological research designs and the associated methodological and analytical techniques;
• identify and evaluate available data for addressing specific research questions;
• evaluate strengths and weaknesses of data collection methods, develop methods appropriate for answering specific research questions, and assess the measurement properties of data collection tools;
• address ethical issues related to epidemiologic studies;
• appreciate the policy implications of epidemiologic research; and,
• write and defend a doctoral dissertation which makes a contribution to the scientific literature.

Click here to view PhD Competencies

Admission Requirements

• Applicants generally are expected to hold a master’s degree in epidemiology or a master’s degree in a related field with strong course work in epidemiology and biostatistics.
• Applicants are expected to have prior research experience which may be demonstrated through the completion of a master’s thesis, supervised research practicum, or other research experience, and which includes independent contributions to scientific publications.
• Applicants should have practical experience and reasonable expertise using standard statistical software packages.
• Click here for information regarding the application process.

Successful applicants will have research interests congruent with those of one or more members of faculty, and may have identified a possible primary or co-supervisor, prior to admission.  Admission may otherwise be conditional upon identifying a supervisor.  Thus, applicants are strongly encouraged to seek out potential supervisors, and discuss with them the possibilities, prior to applying to the degree program.  Applicants should note that identifying a potential supervisor does not guarantee admission.

Course Requirements

Course Requirements (4.0 FCE)

Required Courses (3.5)

Elective Courses (0.5)

Students are best served if their elective courses form part of a coherent package of experience. In this light, students are encouraged to choose elective courses that relate to the theme of their dissertation. For example, advanced methodological courses might be appropriate for a dissertation which involves highly complex statistical analysis; pathology courses for a dissertation which focuses more on disease process; bioethics courses for a dissertation on genetic epidemiology. Electives also may fill gaps in overall training and experience: A student with a largely social sciences background might benefit from health professional level pathology courses; a student with substantial bench-sciences training, who is interested in disease screening, might consider courses in behavioural sciences, health economics, or health policy. Students are encouraged to discuss the selection of appropriate electives with their Supervisory Committees.

Emphasis in Artificial intelligence and Data Science

Students in the PhD program in the Epidemiology field of study have the option to complete an emphasis by completing appropriate coursework in a given area. The emphasis requirements will also count toward, but may exceed, the 4.0 full-course equivalent (FCE) field requirement.

Course Requirements: Emphasis in Artificial Intelligence and Data Science (1.5 FCE)

Qualifying Examination

The qualifying examination is made up of 2 components. Both of the components of the qualifying examination should be completed by the end of the first year.

Details of each component are below:

• Ethical Conduct for Research Involving Humans (TCPS 2) online tutorial: CORE (Course on Research Ethics) is an introduction to the 2nd edition of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2). It consists of eight modules focusing on the guidance in TCPS 2 that is applicable to all research regardless of discipline or methodology. The completion of this module is usually done within the Introduction to Public Health Research (CHL5005H) course. A certificate of completion must be emailed to Matilda Kong at kong@utoronto.ca ( http://www.pre.ethics.gc.ca/eng/education/tutorial-didacticiel/ )
• Doctoral Qualifying Examination: Written doctoral qualifying examination*, which includes an in-class written exam and a take-home question. This exam is held June of the first academic year. This part of the examination is designed to test competence in the concepts, principles, data sources, and content of epidemiology, and the ability to apply these concepts and principles critically. The examination may include multiple choice, fill-in-the blanks, calculations, and short answer questions. The take-home question will be an essay-style. An Examination Committee will mark the examination, blind to the identity of the student. A passing grade is 70%. Students who achieve higher percentages will be informed that they have received grades of Honours (90%+) or High Pass (80-89%).

*The written qualifying can be fulfilled after the indicated required courses are complete:

CHL5005H: Professional Skills for Doctoral Students in Public Health (0.5) CHL5404H: Research Methods I (0.5) CHL5406H: Quantitative Methods for Biomedical Research (0.5) CHL5408H: Research Methods II (0.5) CHL5424H: Advanced Quantitative Methods in Epidemiology (0.5)

PhD Proposal Defense

The PhD proposal defense is a requirement for candidacy and should be completed by December of the second year.  The proposal defense can be done during the first year of study  with the approval of the Program Director. The purpose of the proposal defense is to:

• Ensure that proposed research will result in a successful PhD dissertation.
• Strengthen the thesis question, design, and methods through critical feedback.
• Assess the students’ ability to conduct independent and original research.
• Assess sufficient content/substantive knowledge base relevant to their thesis topic.
• Provide a formal approval to proceed with the dissertation research.

Format: The proposal will include a brief and cogent review of the literature, justification of the research question, the objectives and hypotheses, design, data collection or data sources, proposed analysis strategies, timetable, ethics, and potential problems or issues. The proposal will conclude with references in proper bibliographic format. The proposal also will include a concise statement of the student’s role in the development and conduct of the research. A title page, with word count, will include the names of the Supervisor and other Supervisory Committee members. The proposal will be printed using a 12-point font, and limited to 10 single-spaced pages. The bibliography and title page are not included in the page or word counts. Appendices should be kept to a minimum.

Defense for approval of PhD proposal:

The proposal defense consists of a written outline of the dissertation proposal and an oral presentation. The completion of this process also counts as the protocol approval, which is required for candidacy. The following elements will be assessed:

• The literature review is comprehensive and specific to the content area;
• The proposed work demonstrates scholarly impact and innovation with respect to methods and/or substantive contribution;
• Clarity of research question/objectives
• Completeness and relevance to study design/research plan
• Rationale for approach and methodology
• Appropriateness of research design
• Appropriateness of research methods and statistical analyses
• Feasibility of research approach including power calculation as appropriate
• Requirement, timeline, preliminary data etc.
• Anticipation of difficulties/limitations and plans for management
• Ethical considerations
• The project is adequate and appropriate for a PhD dissertation and manageable within the time-frame and expectations of the PhD program.

The proposal presentation must be attended by the student, the Supervisory Committee and one external reviewer approved by the Program Director. The presentation will be advertised within the Graduate Department of Public Health Sciences, and students and faculty are encouraged to attend.  The external reviewer must be a Full or Associate member of SGS, ideally has research supervisory experience at the doctoral level, and must have specific research expertise in the dissertation topic or methods. The reviewer should have had no previous involvement with the development of the proposal under review.

Process for evaluation:

• The student’s Supervisory Committee approves the written proposal at least three weeks before the anticipated date of proposal defense.
• The student contacts the Program Director, with a copy to the Administrative Assistant, to give notice that the proposal is ready for defense, together with the name, email and brief rationale for the external reviewer. As a reminder, the reviewer must have an SGS appointment at the University of Toronto. The Program Director will approve the external reviewer via email.
• The Supervisor contacts reviewer and committee to arrange the date/time of the presentation, and informs the program Administrative Assistant of the arrangements.
• The Administrative Assistant reserves a room and any required audiovisual equipment specified by the student, and posts notices on bulletin boards and e-mail, including a confirmatory e-mail to the reviewers and Supervisory Committee.
• The student distributes the proposal to the external reviewer, Supervisory Committee members, and Administrative Assistant, three weeks before the date of the proposal defense.
• The proposal defense will begin with a 20-minute presentation of the research proposal by the student, followed by a period of questions and discussion. Presentation questions are posed to the student in two rounds, with approximately 10 minutes allotted to each reviewer per round, with the reviewer taking the lead in the questions. The Supervisor will chair the proceedings and act as timekeeper. The question period will typically be expected to last 60 to 80 minutes. The Supervisor will take notes of all issues raised.
• At the end of formal questioning, the student and other attendees not part of the review panel will leave the room, and the reviewer and Supervisory Committee will have a general discussion of four elements (I – IV) outlined above. The reviewers will rate the performance of the student using a standardized form and an Accept/Provisional Acceptance/Not Accepted decision will be reached. The Supervisor and external reviewer will take note of the feedback and prepare a summary of the recommendations to share with the student.  Typically, the Supervisor will take notes, on the form during the defense, and email to the external reviewer for final review before sending to the student.

The following outline the implications for the evaluation:

Approval: The student may proceed with dissertation work and remaining program progression, taking note of all feedback received during the protocol defense and in consultation with the Supervisor considering minor amendments to their doctoral research accordingly. This candidacy requirement has been met.

Provisional Approval: The student must create a point-by-point response to the concerns/issues raised and make changes to the proposal within 60 days of the proposal defense. Once the Supervisory committee has approved the revisions, the proposal must be submitted to the Program Director and Administrative Assistant as a final record. An approval will then be recorded for candidacy.

Not approved: Non-approval indicates that the performance was inadequate and/or the protocol has major deficiencies according to the IV domains. In the event that the student is not approved on the first attempt, the student will be permitted one more attempt. Failure of the second attempt will result in a recommendation for program termination.

• At the conclusion of the discussion, the student will be invited into the room to learn the general outline of the committee’s decision. The decision and the completed form must be conveyed to the Program Director and Administrative Assistant within 1 week of the defense.

Supervision

Click here to view the SGS Supervision Guidelines for Students.

Beginning prior to admission, and with the assistance of the Program Director, the applicant will explore supervisory possibilities: a faculty member with an appointment in the Division of Epidemiology who has a Full appointment in the School of Graduate Studies (SGS), and who conducts epidemiological research. In some instances, the student and the Program Director will identify both a primary and a co-supervisor. The co-supervisor generally will be a faculty member with an Associate appointment in the SGS. The faculty supervisor may be confirmed prior to beginning the program, and generally will be in place by the end of the first year.  students are encouraged to explore broadly and have wide-ranging discussions with potential supervisors.  The Program Director must approve the selection of the primary supervisor and the co-supervisor.

Role and Responsibilities

The Supervisor is responsible for providing mentorship to the student through all phases of the PhD program. Thus; to the extent possible, the Supervisor will guide the selection of courses, dissertation topic, supervisory committee membership, and supervisory committee meetings; will assist with applications for funding; will make every effort to provide funding to the student directly; and will provide references for the student on a timely basis. The Supervisor also will comment on the student’s plan for preparation for the comprehensive examination. The Supervisor will guide the development of the student’s research proposal, and the implementation and conduct of all aspects of the research; advise on writing the dissertation; correct drafts and approve the final dissertation; and attend the defense.

Supervisory Committee

With the assistance of the Supervisor, and with the approval of the Program Director, the student will assemble a Supervisory Committee within the first year of study.

The Supervisory Committee, chaired by the Supervisor, will contribute advice regarding course selection; preparation for the comprehensive examination; selection of the dissertation topic; preparation and defense of the proposal; and implementation of the research plan. The Supervisory Committee also will provide timely and constructive criticism and guidance regarding data analysis, writing the dissertation, and preparing for its defense.

Composition

The Supervisory Committee generally will comprise the Supervisor and at least two members who hold either Full or Associate appointments in the SGS and may or may not hold a primary appointment in Epidemiology. Between these individuals and the Supervisor, there should be expertise in all content and methodological areas relevant to the student’s research focus and dissertation proposal. At times, when the student’s Supervisory Committee extends beyond the requisite Supervisor plus two SGS-qualified members, additional members may not necessarily hold SGS appointments (e.g., community members).  Non-SGS members, however, may participate only as non-voting qualified observers at the SGS Final Oral Examination (i.e., observer who has been approved by the student, the Supervisor, and the SGS Vice-Dean, Programs).

Supervisory Committee meetings will be held at least every six (6) months throughout the student’s PhD program. Under certain circumstances (e.g., during times of very rapid progress), the student and the Supervisory Committee may decide there is a need for more frequent meetings.

At the end of every meeting of the Supervisory Committee, the student and the Committee will complete the Supervisory Committee Meeting Report . All present must sign the report, which will be delivered to the Program Director and filed in the student’s progress file in the Graduate Department of Public Health Sciences.

The Report of the Graduate Department of Public Health Sciences Oral Defense Committee Meeting will be completed at the end of the Departmental Defense during which the Oral Defense Committee makes the recommendation for the student to proceed to the SGS Final Oral Examination (FOE).  The Report will also be signed and delivered to the Program Director and filed in the student’s progress file in the Graduate Department of Public Health Sciences.

Progress Through the PhD

The phases of the PhD program are identified by a set of accomplishments which the student generally will attain in order, and within a satisfactory time. These phases, which will be monitored by the Program Director of the PhD program, are the identification of the Supervisor and the Supervisory Committee, completion of required and elective course work, completion of the comprehensive examination, defense of the research proposal, and defense of the dissertation (both Departmental and SGS ). Full-time students are expected to complete the PhD within four (4) years. Flex-time students may take longer, but not more than eight (8) years; they must submit a revised list of milestones, for approval by the Supervisor and the Program Director.  Click here to view the PhD Epidemiology Timeline .

Research Ethics Board Approval

All research projects in which University of Toronto students are involved at any stage must have approval from the University of Toronto Research Ethics Board (REB). This includes ongoing research projects of the Supervisor which has previously received REB approval and where REB approval is already held from a University affiliated hospital or research institute. Preliminary work necessary to prepare the proposal may also require an original REB application or amendment to the original study. 
See details of the REB application and review process at Office of Research Ethics ( www.research.utoronto.ca/for-researchers-administrators/ethics/ ).

The dissertation proposal, as approved by the Program Director, must have University of Toronto Research Ethics Board approval as a supervised research study. An application for initial REB approval (or amendment to approval for an ongoing study), will therefore follow the approval of the dissertation proposal.

Dissertation

A dissertation in epidemiology must have relevance to the health of human populations. Within that broad framework, the dissertation may deal with any topic in the areas of medicine, public health and, health care services; and the research designs and statistical methods used in these fields. A doctoral dissertation in epidemiology may involve new data, collected for the purpose of the study, or the use of data previously collected. In the latter case, the analysis must be suitably complex, and must be driven by theoretical considerations and a specific research or methodological question. The dissertation result should be new knowledge and should include findings suitable for publication in peer-reviewed epidemiology journals. It may include both methodological and substantive advances in knowledge.

The dissertation topic must include clearly posed research questions amenable to study by appropriate epidemiologic methods. The student must have contributed substantially to the identification of the research question and must have played an integral part in the planning of the investigation. Wherever appropriate, the student will also be expected to participate directly in the collection of the data. Students will be expected to analyze their own data using appropriate analytic approaches.

Format Options for Dissertation

Students may choose one of two options for preparation of the dissertation: a monograph or a series of journal articles. The monograph is the default option. It is a single report, divided into chapters: introduction, literature review, methods, results, and discussion. A reference list would be followed by various appended material, which might include data collection instruments, additional related findings, and the like.

The journal article option varies from the monograph in that the main body of the dissertation comprises approximately three (3) complete, stand-alone manuscripts; these may already have been published, or may be ready to submit for peer-review. The manuscripts should be preceded and followed by material that unites them. So, for instance, an introduction and literature review, and possibly methods, more global in scope than those included in the manuscripts themselves, would precede the manuscripts; likewise, a discussion would follow, and would tie the manuscripts together, describing how they – as a group – make a contribution to the literature. Appended material might include the methodological details that would not be present in the methods sections of the manuscripts.

Regardless of format, the student should identify and follow appropriate style guides for the preparation of the dissertation.

Dissertation Defense

The student should aim to defend the dissertation within four years of entry into the PhD program. The defense of the dissertation will take place in two stages: first, a Departmental defense, second, a formal defense (the Final Oral Examination) before a University committee according to procedures established by the School of Graduate Studies (SGS). The two defenses generally are separated by about eight weeks.

Departmental Defense

The Departmental defense will be held after the completed dissertation has been approved by all members of the student’s Supervisory Committee, and the completion of the final Supervisory Committee meeting report. The purpose of this defense is to rehearse the oral presentation for the SGS defense and to determine whether the student is ready for the SGS defense.

The student should expect constructive criticism about the clarity and length of the presentation and the quality of visual materials, as well as about the dissertation itself. In particular, the Departmental defense will confirm that:

• The student has adequately met the requirements for a dissertation; and,
• The student has the required level of understanding of the scientific issues involved in the dissertation work.

The Departmental defense is attended by the student, the Supervisor and other members of the Supervisory Committee, and two reviewers with full SGS appointments. At least one reviewer should have supervisory experience in epidemiology at the doctoral level. The second reviewer may be a substantive expert from another discipline. Eligible reviewers will have had no prior involvement with the design or conduct of the research, with the exception of providing references or other background material, and generally will not be the faculty who served as reviewers at the proposal defense. The presentation will be advertised within the Graduate Department of Public Health Sciences, and other students and faculty are encouraged to attend.

• The Supervisory Committee approves the dissertation, at least four (4) weeks before the anticipated date of the defense.
• The Supervisory Committee identifies at least two potential reviewers.
• The student contacts the Program Director (copy to the Administrative Assistant) to give notice that the dissertation is ready for defense, together with the names and email addresses of potential reviewers. If necessary, the Program Director suggests alternative reviewers. The Program Director approves the reviewers, and will nominate one of them to be the Program Director’s representative.
• The Supervisor contacts reviewers and arranges the date/time of the defense, and informs the Administrative Assistant of the arrangements.
• The Administrative Assistant reserves a room and any required audiovisual equipment, as specified by the student, and posts notices on bulletin boards and e-mail, including a confirmatory e-mail to the Supervisory Committee and reviewers.
• The student distributes a copy of the dissertation to reviewers and to Supervisory Committee members four (4) weeks before the date of the defense, with an extra copy to the Supervisor (or designate) which may be made available to other faculty or students who may wish to read it.
• The Oral Defense Committee comprises the external reviewers, the Supervisor and the other Supervisory Committee members.
• Before the Oral Defense Committee convenes, the student and non-committee attendees may be asked to leave the room to permit discussion of the defense process among the Oral Defense Committee members.
• The defense will begin with a 20-minute presentation by the student of the research findings, followed by a period of questions and discussion among those present, with the two reviewers taking the lead in the questions. The Supervisor will chair the proceedings and act as timekeeper. The question period will typically be expected to last 60 to 80 minutes. The Supervisor will take notes of all issues raised.
• At the end of formal questioning, the student and other attendees will generally be asked to leave the room, and the Oral Defense Committee will discuss any issues of concern, to provide focused, constructive, and detailed feedback to the student, Supervisor, and other members of the Supervisory Committee on the dissertation and its oral defense. The Program Director’s Representative will take note of the feedback with respect to whether the dissertation work is generally adequate for the Final Oral Examination (FOE); changes that should be made to the dissertation prior to arranging for the FOE, and improvements that could be made to the oral presentation and defense; and will prepare a summary of the recommendations. If revisions to the text of the dissertation are recommended, there will also be discussion of the timing of the FOE. The student may be invited to be present at these discussions at the discretion of the Oral Defense Committee.
• At the end of the Departmental Defense, the Oral Defense Committee  will complete the Report of the Graduate Department of Public Health Sciences Oral Defense Committee Meeting. The options for proceedings are:

a) Dissertation is acceptable: ____    as is ____    with corrections/modifications as described in report to be prepared by the Program Director’s Representative

b) Another Supervisory Committee meeting required to see final dissertation: ____ Yes ____ No

c) If no, Committee member to see that changes are made: __________________________

d) Dissertation recommended for examination in: ______ months.

The Report will be delivered to the Program Director and filed in the student’s file in the Graduate office of Public Health Sciences.

School of Graduate Studies Final Oral Examination (FOE)

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PhD program admission requirements

Degree and course requirements.

Applicants must have completed, or be in the process of completing a master's degree in economics or a related field with an average of at least B+, or have completed, or be in the process of completing a bachelor’s degree in economics or a related field with an average of at least A- in the final two years of study.

We offer both regular-entry and direct-entry PhD programs. The regular-entry program is open only to students who will have completed the requirements for a master’s degree in economics or a related field by September of the year for which they are applying. Students who do not expect to satisfy this condition should apply to the direct-entry program . In particular, applicants currently in the final year of a bachelor’s program should apply for the direct-entry PhD. Under no circumstances should you apply to both the regular-entry and the direct-entry PhD program. Applicants to the direct-entry program will be asked whether they wish to be considered for the Economics MA program should their PhD application be unsuccessful.

Applicants educated in a country other than Canada should check the equivalent qualifications table prior to starting the online admission application (not all bachelor’s or master's degrees are equivalent to the corresponding degree from the University of Toronto). The PhD is a full-time program. It is not possible to pursue a PhD on a part-time basis. Candidates are required to remain in full-time attendance for the first three years of the program. There is one admission date, in September. There is no January admission.

Applicants must have a strong preparation in advanced mathematics, statistics, and economics, including courses in microeconomic theory, macroeconomic theory, and econometrics or statistics.

Please note that meeting these minimum requirements does not imply automatic acceptance into the program. (See the PhD FAQ for the typical profile of a successful applicant.)

The admission process

Please read in full the application information and instructions prior to starting the university's online application to ensure you have informed yourself on essential information including: application deadlines, application processing time, planning for your application submission, how to apply, contact information and the application assessment process.

Once the university's online application form has been completed and the application fee paid (final deadline January 19, 2024), applicants will receive an email message from the Department of Economics with a link to a supplementary form. When this form is completed and the Economics Graduate Office has received all required supporting documentation (final deadline January 19, 2024), the department will begin to review and assess the application. To avoid any issues, we strongly advise applicants to complete their application and supplementary form well in advance of the deadline. To be considered for certain prestigious scholarships, such as the Connaught or Trillium scholarship, the application must be completed by January 19, 2024, including all required supporting documentation.

The application assessment process

The files of applicants who meet the minimum requirements, submit all the required documents by the deadline will be reviewed by the Department of Economics Admissions Committee. Note: The meeting of these requirements is only a necessary condition, not a sufficient condition, for acceptance into the program. The Admissions Committee normally starts to make first round offers from mid-March through early April and may continue with subsequent rounds of offers until June. All applicants will be notified either with an offer of admission or rejection of their application by the end of June.

Please also see our PhD FAQ page for the answers to commonly asked questions.

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February 2024

ATTENTION: You may have heard of new measures in place for International students admitted to postsecondary institutions in Canada and applying for study permits. These new measures do not apply to graduate students (Master’s and PhD programs). While all international students must follow the established study permit application processes, the new Attestation Letter required for undergraduate students does not apply to graduate students in degree-granting programs.

As of February 15, 2024, graduates of master’s degree programs will be eligible to apply for a 3-year post-graduation work permit. Open work permits will also remain available to spouses of international students in master’s and doctoral programs. We will provide additional information about this change as it becomes available.

For more information see Apply for Your Study Permit – Centre for International Experience .

Types of programs

The training and experience you’ll acquire at the master’s or doctoral level at the University of Toronto will give you tools to drive change and excel in virtually any industry — whether you go on to teach and do research at a university, take a role in government, start a private enterprise, or embark on a professional career. Our research-driven graduate programs will help guide you through a lifetime of intellectual study, opportunity, and challenge.

View our types of graduate programs at a glance . Here’s a quick overview:

• More than 70 professional graduate programs in health sciences, management, engineering, and more.
• Approximately 140 combined degree programs.
• 14 dual degree programs.
• More than 40 collaborative specializations if you are interested in interdisciplinary studies.
• 4 diploma programs for professionals who would like to pursue academic study but don’t wish to enrol in a graduate degree program.

Ready to apply?

Are you ready to launch a lifelong path of intellectual discovery and professional enrichment? Apply to graduate school at Canada’s #1 research institution.* Visit our Future Students page to find out more.

*According to Times Higher Education’s World University Rankings 2021 . View our rankings.

U of T graduate program directory

Explore our 400 areas of study within more than 300 graduate program below.

Questions? Explore the 2023-24 SGS Calendar to access comprehensive information about graduate programs.

Still can’t find what you’re looking for? Contact the graduate unit (department, centre, or institute) you’re thinking of applying to. Visit the graduate unit and collaborative specializations directory.

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Courses and programs, new for 2023-2024, pdf and archive, course description by course code, statistics major (science program) - asmaj2289.

Statistical Science encompasses methods and tools for obtaining knowledge from data and for understanding the uncertainty associated with this knowledge. The purposes of the undergraduate programs are to: (1) equip students with a general framework for obtaining knowledge from data; (2) give students skills that they are able to flexibly apply to a variety of problems; and (3) to provide students with the ability to learn new methods as needs, data sources, and technology change. The Major in Statistics gives students a broad understanding of the statistical methods and computational and communication skills appropriate for effective statistical problem solving. The successful student will also acquire a general understanding of the role of mathematical thinking to support the development and evaluate the properties of statistical methods. While the Major is designed to complement study in an area of application of quantitative methods, students in the Major may choose to have a greater focus in probability and statistical theory through elective courses.

This is a limited enrolment program. Note there are different admission criteria depending on whether a student has completed between 4.0 and 8.5 credits, or 9.0 or more credits.

For students who have completed between 4.0 and 8.5 credits:

Completed Courses The following courses are required:

• STA130H1 • ( MAT135H1 and MAT136H1 )/ MAT137Y1 /​ MAT157Y1

Variable Minimum Grade Average A minimum grade average in STA130H1 and ( MAT135H1 and MAT136H1 )/ MAT137Y1 /​ MAT157Y1 is needed for entry. This minimum grade average changes each year depending on available spaces and the number of applicants.

Note: - Students who take ( MAT135H1 and MAT136H1 ) will typically require a higher minimum grade average than students who take MAT137Y1 /​ MAT157Y1 .

- STA130H1 is restricted to first-year students, therefore students are strongly encouraged to take STA130H1 in their first year. If you are unable to complete STA130H1 in first year, see notes below for accepted substitutions for this requirement.

For students who have completed 9.0 or more credits:

• CSC108H1 /​ CSC110Y1 /​ CSC111H1 /​ CSC120H1 /​ CSC148H1 • MAT223H1 /​ MAT224H1 /​ MAT240H1 • MAT235Y1 /​ MAT237Y1 /​ MAT257Y1 • ( STA237H1 and STA238H1 )/ ( STA247H1 and STA248H1 )/ ( STA257H1 and STA261H1 )/ ECO227Y1

Variable Minimum Grade Average A minimum grade average in ( STA237H1 and STA238H1 )/ ( STA247H1 and STA248H1 )/ ( STA257H1 and STA261H1 )/ ECO227Y1 and MAT235Y1 /​ MAT237Y1 /​ MAT257Y1 is needed for entry. This minimum grade average changes each year depending on available spaces and the number of applicants.

Note: - Students who take ( STA237H1 and STA238H1 )/ ( STA247H1 and STA248H1 ) will typically require a higher minimum grade average than students who take ( STA257H1 and STA261H1 )/ ECO227Y1 .

(7.0 or 7.5 credits, including a 0.5 credit STA 400-series course)

First Year:

STA130H1 , CSC108H1 /​ CSC110Y1 /​ CSC111H1 /​ CSC120H1 /​ CSC148H1 , ( MAT135H1 , MAT136H1 )/ MAT137Y1 /​ MAT157Y1 .

( MAT223H1 /​​ MAT224H1 /​ MAT240H1 recommended in first year)

Second Year: MAT223H1 /​ MAT224H1 /​ MAT240H1 , MAT235Y1 /​ MAT237Y1 /​ MAT257Y1 ; ( STA247H1 , STA248H1 )/ ( STA237H1 , STA238H1 )/ ( STA257H1 , STA261H1 )/ ECO227Y1

( STA237H1 and STA238H1 are strongly recommended. MAT221H1 may not be used for this requirement.)

Higher Years: 1. STA302H1 2. 0.5 credit from STA313H1 /​ STA314H1 /​ STA365H1 /​ STA347H1 /​ STA355H1 3. 0.5 credit from STA414H1 /​ STA437H1 /​ STA442H1 /​ STA457H1 /​ STA465H1 /​ STA475H1 /​ STA480H1 4. 1.0 credit from remaining STA300+ level courses not used toward other program requirements in the following list: STA303H1 , STA304H1 , STA305H1 , STA313H1 , STA314H1 , STA347H1 , STA355H1 , STA365H1 , STA410H1 , STA414H1 , STA437H1 , STA442H1 , STA447H1 , STA450H1 , STA452H1 , STA453H1 , STA457H1 , STA465H1 , STA475H1 , STA480H1 , STA490Y1 , STA492H1 , STA496H1 /​ STA497H1 ( STA399H1 and STA399Y1 may be considered on a case by case basis with the approval of the Program Director).

• If you do not complete STA130H1 in your first year of study, this requirement must be fulfilled by completing a 300 or 400-level 0.5 credit STA course to replace STA130H1 . Please note that the 300 or 400-level STA course used to replace STA130H1 cannot be a course that is already being used to meet a program completion requirement.
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Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

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

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

Residency Requirements

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

Your Advisor and Special Committee

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

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

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

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

Statistics PhD Travel Support

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

Completion of the PhD Degree

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

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

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Doctor of Philosophy in Statistics

Program description.

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

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

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

Career Opportunities

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

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

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

Marketable Skills

Review the marketable skills for this academic program.

Application Deadlines and Requirements

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

Visit the  Apply Now  webpage to begin the application process. 

Contact Information

For more information, contact [email protected]

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

nsm.utdallas.edu

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