# 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:

 Lin. Alg. I Calculus 1 Calculus II Disc. Math Comp Sci I Specialist MATA22 MATA31 MATA37 MATA67 CSCA08* Major MATA22 MATA30/31 MATA36/37 - CSCA08/20 Minor MATA22/23 MATA30/31 MATA36/37 - CSCA08/20

*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.

If I am considering a degree in Statistics, which courses should I take?

See the table below

 Lin. Alg. I Calculus 1 Calculus II Disc. Math Comp Sci I Specialist MATA22 MATA31 MATA37 MATA67 CSCA08* Major MATA22 MATA30/31 MATA36/37 - CSCA08/20 Minor MATA22/23 MATA30/31 MATA36/37 - CSCA08/20

The Machine Learning and Data Science Stream also requires CSCA48. To have access to all programs, it is recommended that you take MATA22, MATA31, MATA37, MATA67, and CSCA08.

What sequence of calculus courses should I take?

The sequence MATA31H3 and MATA37H3 is recommended. MATA37H3 requires MATA67H3 and MATA31H3 as prerequisites.

If I just want to get some experience in Statistics, which courses should I take?

You should take STAA57H3 if you are in CMS and have taken a programming course, or STAB22H3 if you are not in CMS and have no Mathematical/Computer Science background.

Can I take STAB22/23H3 if I'm planning on doing a Statistics degree?

No, if you plan on doing a Specialist or Major in Statistics, you should take the foundational courses and theory-based Statistics courses.

If I already have Statistics experience, can I skip courses?

Generally no, unless you have applicable transfer credits from previous studies.

Why do I have to take Computer Science and Mathematics courses if I want a degree in Statistics?

Mathematical modelling and computation are integral parts of analyzing data, so you need a solid foundation in them for a degree in Statistics.

What is the difference between a Major and Specialist program in Statistics?

Statistics Majors are required to complete 8 credits (16 courses) that offer a thorough understanding of the field. Specialists complete 13 credits (26 courses) to gain a more in-depth and targeted training on a particular area of application of Statistics. There are three focus areas, or streams, in the Specialist program in Statistics, namely: a) Statistical Science, b) Quantitative Finance, and c) Statistical Machine Learning and Data Science.