Statistics (STAT) | Faculty of Science
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STAT 1115 3 credits
Statistics I
Students will summarize and display data and perform inferences about proportions, means and standard deviations for one and two populations. Students will summarize and display data, find confidence intervals, and perform hypothesis tests for proportions, means, and standard deviations, for one and two populations, both large and small. They will also perform regression analysis, and determine probabilities. This course is equivalent with MATH 1115. Students may earn credit for only one of these courses.
Level: UG
Prerequisite(s): Level C1 as defined in the Math Alternatives Table
STAT 1170 3 credits
Introduction to Data Science: An AI Approach
This course provides students with their initial exposure to the fundamental tools of data science, emphasizing the role of artificial intelligence in enhancing analytical capabilities. Applications span various disciplines—including social sciences, life sciences, physical sciences, business, and engineering. Participants will explore data manipulation, visualization, analysis, and interpretation using the Python programming language. The course integrates AI-driven methods into traditional data science workflows, covering foundational classification and regression models alongside advanced machine learning and deep learning techniques. Students will learn to forecast trends, recognize patterns, and leverage AI to automate complex analyses, all while engaging with real-world case studies that demonstrate the unified merging of AI and data science. Throughout the course, emphasis is placed on crafting compelling narratives that effectively translate AI-enhanced analytical results to non-technical audiences, ensuring practical understanding and real-world applicability. This course is equivalent with MATH 1170. Students may earn credit for only one of these courses.
Level: UG
Prerequisite(s): Level C1 as defined in the Mathematics Alternatives Table
STAT 2315 3 credits
Probability and Statistics
Students will study introductory probability and statistics using a background of calculus. They will study concepts including randomness, probability, probability distributions for discrete and continuous random variables, descriptive statistics, multivariate distributions, laws of expectation, functions of random variables, statistical inference, and hypothesis testing. Distributions studied will include binominal, normal, geometric, hypergeometric, exponential and Poisson distributions. This course is equivalent with MATH 2315. Students may earn credit for only one of these courses.
Level: UG
STAT 2335 3 credits
Statistics for Life Sciences
Students will learn statistical techniques and their application to life sciences. They will study descriptive statistics, elementary probability, probability distributions, in particular, the binomial, normal, t and chi-square distributions, confidence intervals and hypothesis testing for population means, and proportions, as well as for differences in population means and proportions. Students will also study linear regression, and the chi-square goodness-of-fit test. This course is equivalent with STAT 2342, MATH 2335. Students may earn credit for only one of these courses.
Level: UG
Credit Exclusion: STAT 2342
STAT 2342 3 credits
Introduction to Statistics for Business
Students will learn statistical techniques and their application to business and economics. They will study descriptive statistics, elementary probability, random variables, sampling distributions, linear regression, correlation, estimation and hypothesis testing. They will also learn how to apply statistical software to descriptive and inferential statistics. Distributions studied will include binominal, normal, t- and chi-square distributions. This course is equivalent with MATH 2341. Students may earn credit for only one of these courses. This course is credit excluded with MATH 2335. Students may enroll in and earn credit for only one of these courses.
Level: UG
Prerequisite(s): All of (a) Level C1 as defined in the Math Alternatives Table, and (b) 9 credits from courses at the 1100 level or higher
Credit Exclusion: STAT 2335
STAT 3315 3 credits
Applied Inferential Statistics
Students will be introduced to the standard techniques of multiple regression analysis. They will study simple regression, ANOVA, multivariable distributions, analysis of residuals and general linear models and their role in research. This course is equivalent with MATH 3315. Students may earn credit for only one of these courses.
Level: UG
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