Computing in Medicine Program

Program Overview

From Electronic Health Records and medical imaging to wearable devices and remote monitoring, technology enables healthcare providers to collect and examine vast amounts of data to gain insights. As data and artificial intelligence (AI) become increasingly pervasive in medicine, it has become critical for health professionals and clinicians to develop a strong understanding of data science and have the skills needed to build and evaluate their own machine learning models.

T-CAIREM is pleased to offer three standalone 10-week professional development courses to participants who would like to learn more about the fascinating intersection of data science and medicine.

Program Delivery

To accommodate learners of different levels, there are three sequential courses available. Course 1 is self-study and Courses 2 and 3 are online instructor-led.

Students will be able to reach out and ask questions through Quercus and live office hours with the instructor.

A Certificate of Completion will be issued for each course to participants who complete the required assignments in a timely fashion.

You don't need to take all three courses if you don't want to. Each course is treated as a separate, standalone component for learners at different phases of their educational journey.

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NOTE: 100% completion of all course assignments is mandatory in order to receive a Certificate of Completion.

IMPORTANT: Please use a Gmail account to register for this course. Some company email addresses get caught in spam filters, and we cannot deliver the course login information.

For those who are part of the UofT community and have a UTORid, please register with the email address associated with that account. You will be receiving course materials via Quercus.

Course Descriptions

COURSE 1: Basic Programming (Self-Study)

Participants will learn basic programming concepts like variables, functions, conditionals, and iteration in this 10-week self-study course. Course 1 provides learners with an introduction to Python programming language, an overview of how to interact with different types of medical data in computer programs, and case studies on how to apply machine learning on real-world datasets.

Course Dates: Tuesday, October 14, 2025 to Monday, December 22, 2025

Note: Students are completing the lectures and course assignments at their own pace until December 22, 2025. Course 1 has 5 assignments and learners can complete the course on their own time and submit assignments to the instructor by the last day of the course.

What you'll learn in the Basic Programming course

Course 1 Objectives:

• To learn how to write basic Python programs involving variables, conditionals, iteration, functions in simple combinations
• To learn how to trace basic Python programs involving lists, dictionaries and files
• To learn how to implement good practices in software design (e.g., conventional naming standards, modular design)

REGISTRATION DEADLINE: Tuesday, October 7, at 11:30pm ET

COURSE 2: Data Science (Online Instructor-led)

Participants will learn how to visualize and manipulate time-series and image data in this 10-week course.

Course Period: Monday, January 26, 2026 to Monday, April 6, 2026

Course Time: Ten (10) Weekly Wednesday evenings from 7:00pm to 8:00pm

What you'll learn in the Data Science course

Course 2 Objectives:

• To learn how to work with tabular data using the pandas library
• To learn the basics of digital signal processing with time-series data using the numpy and scipy libraries
• To learn the basics of image processing with image data using the opencv library
• To learn how to generate useful visualizations using the matplotlib library

REGISTRATION DEADLINE: Monday, January 19, at 11:30pm ET

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COURSE 3: Machine Learning (Online Instructor-led)

Participants will learn how to train and evaluate machine learning models on real-world datasets in this 10-week course.

Course Period: Monday, April 20, 2026 to Monday, June 29, 2026

Course Time: Ten (10) Weekly Wednesday evenings from 7:00pm to 8:00pm

What you'll learn in the Machine Learning course

Course 3 Objectives:

• To learn the terminology associated with machine learning (e.g., train-test splits, cross-validation)
• To learn how to execute an end-to-end machine learning pipeline using the scikit-learn library
• To learn various techniques for inspecting how a machine-learning model makes its decisions

REGISTRATION DEADLINE: Monday, April 13, at 11:30pm ET

Tuition

COURSE 1 (Self-Study)                                                      $700 CAD

COURSE 2 and COURSE 3 (Online, Instructor-Led)   $800 CAD

Meet the Instructor

Alex Mariakakis is an Assistant Professor in the Department of Computer Science at the University of Toronto and an Affiliate Scientist at Techna. He runs the Computational Health and Interaction (CHAI) lab, which leverages ubiquitous and emergent technologies to address problems related to people’s health and well-being.

What is Quercus?

Quercus is the University of Toronto’s primary online teaching and learning platform for accessing course content, submitting assignments, and interacting with your instructor and other learners.

Your Quercus site will be the “virtual classroom” where you, your instructor, and other students will interact. You will receive a detailed course schedule and information about how Quercus will be used for all formats (in-class, online and hybrid).

Contact

You can send any questions to: t.cairem@utoronto.ca.