Deciding Which First Year Statistics Courses To Take

The Department of CMS offers two different types of introductory Statistics courses: a) theory-based courses for students who want to focus on Statistics (e.g., complete a Specialist or Major program in Statistics), and b) practice-based courses for students who want to apply Statistics in their field of study. Students concentrating on Statistics are required to take a selection of foundational Mathematics and Computer Science courses in their first year. This guide will help you to understand your options and choose the courses that will benefit you the most. As a reminder, you should also check the calendar regarding prerequisites and exclusions.

Theory-Based Statistics Courses:

CMS students pursuing a Statistics Program must take the following foundational courses in Mathematics and Computer Science:

*The Machine Learning and Data Science Stream also requires CSCA48.

After completing the foundational courses, students should take the following core Statistics courses:

STAB52H3 - An Introduction to Probability
A mathematical treatment of probability. The topics covered include: the probability model, density and distribution functions, computer generation of random variables, conditional probability, expectation, sampling distributions, weak law of large numbers, central limit theorem, Monte Carlo methods, Markov chains, Poisson processes, simulation, applications. A computer package will be used.

STAB57H3 – An Introduction to Statistics
A mathematical treatment of the theory of statistics. The topics covered include: the statistical model, data collection, descriptive statistics, estimation, confidence intervals and P-values, likelihood inference methods, distribution-free methods, bootstrapping, Bayesian methods, relationship among variables, contingency tables, regression, ANOVA, logistic regression, applications. A computer package will be used.

All CMS students who want an early taste of statistics and data analysis can take:

STAA57H3 - Introduction to Data Science
Reasoning using data is an integral part of our increasingly data-driven world. This course introduces students to statistical thinking and equips them with practical tools for analyzing data. The course covers the basics of data management and visualization, sampling, statistical inference and prediction, using a computational approach and real data.

Practice-Based Statistics Courses:

Students who want to learn and apply Statistics without having to complete all the mathematical foundations can take STAB22/23 and STAB27. These courses have no other pre-requisites, and can be taken by non-CMS students as part of the Minor program in Applied Statistics.

Students pursuing CMS programs should take the theory courses instead, and not STAB22/23 or STAB27.

STAB22H3 – Statistics I
This course is a basic introduction to statistical reasoning and methodology, with a minimal amount of mathematics and calculation. The course covers descriptive statistics, populations, sampling, confidence intervals, tests of significance, correlation, regression and experimental design. A computer package is used for calculations.

STAB23H3 – Introduction to Statistics for the Social Sciences
This course covers the basic concepts of statistics and the statistical methods most commonly used in the social sciences. The first half of the course introduces descriptive statistics, contingency tables, normal probability distribution, and sampling distributions. The second half of the course introduces inferential statistical methods. These topics include significance test for a mean (t-test), significance test for a proportion, comparing two groups (e.g., comparing two proportions, comparing two means), associations between categorical variables (e.g., Chi-square test of independence), and simple linear regression.

STAB27H3 – Statistics II
This course follows STAB22H3, and gives an introduction to regression and analysis of variance techniques as they are used in practice. The emphasis is on the use of software to perform the calculations and the interpretation of output from the software. The course reviews statistical inference, then treats simple and multiple regression and the analysis of some standard experimental designs.

Frequently Asked Questions

Note on Pre-requisites & Exclusions

Please pay special attention to pre-requisites, co-requisites and exclusions when you enroll in courses:

• To enroll in a course you must have successfully completed all pre-requisites. If you have not satisfied pre-requisite, even if the computer system allows you to enroll, you will be removed from the course before the course begins.
• Co-requisites are courses that must be taken in the same session.
• When one course is an exclusion of another, then you will not get credit for both courses. If you take both then one of the two will not count toward the 20 credits needed to graduate. In some cases exclusions are not both ways. That is, it is ok to take course A before course B, but not course B before course A. Pay attention to this so you are not surprised by having too few credits when you apply to graduate.