Program Overview

Professional Master's Program in Statistics

The Professional Master's Program in Statistics (MStat) offers a customized and individualized curriculum based on the interests and career objectives of the student. The program provides a balanced training in statistical methods, computational statistics, and statistical theory, preparing students 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. 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

The following 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 developed between the student and the advisor/director of the MStat program. The current recommended core courses are listed below:

• 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)