This option was introduced in the Fall of 2019. This is also a course-only option, with the same required courses as the standard course-only option, but where the electives are limited to a subset of courses that emphasize data science and artificial intelligence. This option will be of interest to students wishing to enter the growing field of Data Science, a multidisciplinary science combining elements of statistics and computer science, with roots in mathematics.

Students registered in this option require 5.0 FCE (equivalent to 10 half courses) to graduate. These students are expected to complete the 4.0 FCE required courses outlined in Option 1, as well as 1.0 FCE in approved AI / Data Science electives as outlined below.

Program Requirements

Required Courses: (4.0 FCE) FCE
CHL5004H: Introduction to Public Health Science 0.5
CHL5207Y: Laboratory in Statistical Design and Analysis 1.0
CHL5209H: Survival Analysis I 0.5
CHL5210H: Categorical Data Analysis 0.5
CHL5250H: Biostatistics Seminars 0.5

Plus one of either:

CHL5226H: Mathematical Foundations of Biostatistics
or
STA2112H: Mathematical Statistics I

0.5

Plus one of either:

CHL5223H: Applied Bayesian Methods
or
STA2212H: Mathematical Statistics II

0.5

Approved AI / Data Science Electives

Elective Courses (1.0 FCE) FCE
CHL5212H: Predictive Modelling in the Health Sciences 0.25
CHL5213H: Methods of Analysis of Microbiome Data 0.25
CHL5229H: Modern Biostatistics and Statistical Learning 0.5
CHL5230H: Applied Machine Learning for Health Data 0.5

The Practicum: CHL5207Y Laboratory in Statistical Design and Analysis

Format

This is a full year course with the following format:  i) a weekly 2-hour lecture and ii) a 4-hour per week practicum. Weekly lectures focus on design issues in term 1 and analysis issues in term 2. The practicum occurs at the supervisor’s employment site. To that end, students will be encouraged to integrate themselves as much as possible into their practicum setting.

Objectives

The main goal of the lecture series is to introduce the student to common statistical design and analysis techniques encountered by the practicing biostatistician/data scientist. The main goal of the practicum is to provide the student with hands-on experience with design and analysis issues encountered by applied statisticians/data scientists in a real workforce setting. It also emphasizes the importance of good communication skills and other soft skills that are required by a biostatistician/data scientist to be effective in today’s work environment.

Note: Students registered in the Emphasis in AI / Data Science must complete their practicum component in the area of artificial intelligence/data science.  The practicum sites will focus on industries that utilize AI and Data Science. The placements will typically run in the summer session, after all the required courses are taken.