Curriculum & Courses

The Professional Master's Program in Statistics (MStat) offers you a customized and individualized curriculum based on the interests and your career objectives. You get to choose either a broad-based or specialized program of study.

All our courses provide a balanced training in statistical methods, computational statistics, and statistical theory, and are intended to prepare you to adapt statistical methodologies to practical problems in a professional setting.

Course of Study

The MStat is a non-thesis master's degree and does not require an internship. Students are required to take 30 hours of approved coursework, with additional recommended career-enhancing enrichment courses.

Depending on your selected specialization, the mix of courses — required, track-specific, and elective — will be jointly determined by you and your graduate advisor, who you will meet with during your first year to select an individualized plan.

The program normally takes three semesters of full-time course work. Students are restricted to no more than four courses in their first semester (with three being preferable). It is also possible to complete the program on a part-time basis.

Core Curriculum

These required courses are normally completed by the end of the first two semesters:

  • Probability (STAT 518)
  • Statistical Inference (STAT 519)
  • Statistical Computing and Graphics (STAT 605)
  • Introduction to Regression and Statistical Computing (STAT 615)
  • Advanced Statistical Methods (STAT 616)

Courses Specific to Area of Specialization

These courses are recommended for a specialization track that is to be developed between the student and the advisor/ director of MStat program. Courses include recommended core and elective courses.

The current recommended core courses are listed below; recommended electives and courses for certain tracks such as Applied Statistics for Industry and Preparation for Ph.D. Studies are developed separately.

Learn more about our areas of specialization.

Financial Statistics and the Statistics of Risk

  • Applied Time Series and Forecasting (STAT 621)
  • Quantitative Financial Risk Management (STAT 649)
  • Quantitative Financial Analytics (STAT 682)
  • Market Models (STAT 686)
  • Quantitative Finance (STAT 699)

Bioinformatics, Statistical Genetics, and Biostatistics

  • Generalized Linear Models & Categorical Analysis (STAT 545)
  • Biostatistics (STAT 553)
  • Probability in Bioinformatics and Genetics (STAT 623)
  • Probability and Statistics for Systems Biology (STAT 673)

Statistical Computing and Data Mining

  • Bayesian Data Analysis (STAT 622)
  • Multivariate Analysis (STAT 541)
  • Simulation (STAT 542)
  • Statistical Machine Learning (STAT 613)

Environmental Statistics

  • Quantitative Environmental Decision Making (STAT 685)
  • Environmental Risk Assessment & Human Health (STAT 684)

Applied Statistics for Industry

  • Quantitative Environmental Decision Making (STAT 685)
  • Multivariate analysis (STAT 541)
  • GLM and categorical analysis (STAT 545)
  • Bayesian analysis (STAT 525)
  • Advanced Statistical Methods (STAT 616)
  • CoFES blockchain/crypto (STAT 687)

Preparation for PhD Studies in Statistics, Mathematical Economics, and Finance

  • Multivariate analysis (STAT 541)
  • GLM and categorical analysis (STAT 545)
  • Bayesian analysis (STAT 525)
  • Causal analysis (STAT 530)
  • Statistical inference I (STAT 532)
  • Probability (STAT 581)

Masters in Computational Science and Engineering

The Master in Computational Science and Engineering is a non-thesis degree program offered jointly by the departments of Computational and Applied Mathematics, Computer Science, Electrical and Computer Engineering and Statistics in the School of Engineering. The program is designed to provide training and expertise in modern and computational techniques with real-world applications in a wide range of industries.


Electives are targeted in the subfield of interest. Students may be asked to take specific courses outside the department, depending on the incoming background of the student, career objectives and funding sources. The links above to curriculum planning for each specialization include suggested electives for each specialization.