TA office hours for Python Q/A, one hour per week (time TBD, over Zoom)
Location
Princess Margaret Cancer Centre, 610 University Ave, Room 6-604 (lectures)
Recommended Prerequisites
Required module – there are no prerequisites.
Students without prior Python coding experienced are encouraged to familiarize themselves with the basic syntax and concepts of Python programming at the level of Lectures 1-4 of the Harvard CS50 Python YouTube course.
Module Goals
This course will serve as a rapid introduction to probability and statistical thinking with methods drawn from frequentist as well as Bayesian statistics. Students will gain a thorough understanding of how statistical inference is conducted and will, by the end of the course, be able to critically assess our use of statistics in the search for scientific truths and implement basic statistical modelling in Python.
Evaluation Method
Homework: 6 assignments, lowest mark dropped (10% each for 5 highest assignments)
Take-Home Final Exam: (50%)
Schedule
Date
Instructor
Lecture
Fri September 8
Jamie Near
Probability and Exploratory Data Analysis
Fri September 15
TA
Statistical Analysis in Python Bootcamp
September 19
No Class
Departmental Retreat
Fri September 22
TA
Statistical Analysis in Python Bootcamp
Mon September 25
Jamie Near
Hypothesis Testing
October 3
Jamie Near
Linear Models
October 10
Angus Lau
Inference and Prediction
October 17
Angus Lau
Introduction to Non-Parametric Statistics
October 24
Angus Lau
Introduction to Artificial Intelligence in Healthcare
