COURSE CURRICULUM

The MSc in Statistics program at the University of Manitoba offers a diverse range of courses designed to provide students with both theoretical foundations and practical skills in modern statistical methods. For instance, STAT 4150: Bayesian Analysis and Computing introduces students to Bayesian approaches, emphasizing computational techniques essential for modern data analysis. STAT 4250: Statistical Learning covers methods used in predictive modeling and data mining, equipping students to work with large and complex datasets. The course STAT 4530: Design of Experiments teaches principles of planning, conducting, and analyzing experiments to obtain valid and efficient results. At the advanced level, STAT 7060: Advanced Theory of Probability delves deeply into probability theory, forming the mathematical basis for statistical inference. STAT 7080: Advanced Statistical Inference builds on this foundation to explore sophisticated methods for drawing conclusions from data. STAT 7100: Analysis of Discrete Data focuses on statistical techniques for categorical data, which is common in many fields. Time series analysis, critical for understanding data collected over time, is covered in STAT 7260: Time Series. Lastly, STAT 7270: Bayesian Inference extends Bayesian methods to complex applications. Together, these courses prepare students for a wide range of careers by blending rigorous theory with practical application in statistics.