Professional Development Opportunities
Overview of the Courses
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 to have the skills needed to build and evaluate their own machine-learning models.
T-CAIREM is pleased to offer three standalone 5-week professional development courses to participants who would like to learn more about the fascinating intersection of data science in medicine.
These self-directed online courses will provide learners with an introduction to using data science in the Python programming language, an overview of interacting with different types of medical data in computer programs, and case studies on applying machine learning to real-world datasets.
Course Delivery
All three self-directed courses will be taught asynchronously via pre-recorded video lectures that will be released weekly every Wednesday from January 14, 2025 to May 6, 2025. The courses are self-study, meaning that students will be expected to watch pre-recorded lectures on their own time. Students will be able to ask questions about the materials through the University of Toronto's Quercus system and weekly live office hours with the instructor.
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.
We will be using Google Colab for most of the lecture materials, in-class exercises, and assignments which the instructor will explain in the first session. All that is required to participate in the course is internet access and a Google account.
Course Descriptions
COURSE 1: Basic Programming
Participants will learn basic programming concepts like variables, functions, conditionals, and iteration.
Dates: January 14, 2025 to February 11, 2025
Syllabus: What you'll learn in the Basic Programming course
Course 1 Registration deadline: January 7, 2025 (Tues.) at 5pm ET
Register for Course 1: Basic Programming
COURSE 2: Data Science
Participants will learn how to visualize and manipulate time series and image data.
Dates: February 25, 2025 to March 25, 2025
Syllabus: What you'll learn in the Data Science course
Note: Course 2 participants are expected to be familiar with programming in Python.
Course 2 Registration deadline: February 18, 2025 (Tues.) at 5pm ET
Register for Course 2: Data Science
COURSE 3: Machine Learning
Participants will learn how to train and evaluate machine learning models on real-world datasets.
Dates: April 8, 2025 to May 6, 2025
Syllabus: What you'll learn in the Machine Learning course
Note: Course 3 participants are expected to be familiar with programming in Python.
Course 3 Registration deadline: April 1, 2025 (Tues.) at 5pm ET
Register for Course 3: Machine Learning
Expectations
All assignments will be graded on a pass-fail basis. Course participants will receive a Certificate of Completion pending the following conditions are met:
• Self-Directed Course: Timely completion of all assignments
• Instructor-Led Course: Timely completion of all assignments, attendance to all online sessions (exceptions made in extenuating circumstances)
Tuition
• Each online self-directed course is $1500 CAD and includes all materials.
These courses take place between January 14 to May 6, 2025.
• Each online instructor-led course is $2,000 CAD and includes all materials.
These instructor-led courses take place between April 8 and July 30, 2025.
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.