Registration now open for T-CAIREM's instructor-led professional development courses

INSTRUCTOR: Alex Mariakakis (Assistant Professor, Computer Science, University of Toronto)

Read our article with the course instructor!

T-CAIREM is pleased to offer three 5-week online, instructor-led professional development courses to participants who would like to learn more about the fascinating intersection of data science in medicine.

These 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. 

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About the online, instructor-led 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 develop the skills to build and evaluate their own machine-learning models.

T-CAIREM is pleased to offer 3 standalone 5-week professional development courses to participants who would like to learn more about the fascinating intersection of data science and medicine. These online, instructor-led courses will provide learners with an introduction to Python, an overview of how to interact with different types of medical data, and case studies on how to apply machine learning to real-world datasets.

Course Delivery

To accommodate learners of different levels, there are 3 sequential courses available. Each course has assignments and takes place over a 5-week window.

All 3 courses will be taught via online, instructor-led lessons using the University of Toronto's Quercus system. 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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COURSE 1: Basic Programming
Participants will learn basic programming concepts like variables, functions, conditionals, and iteration in this 5-week course.

Course Dates: April 8 (Tue.) to May 6 (Tue.) from 11:00am to 1:30pm ET
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)

COURSE 1 REGISTRATION DEADLINE: April 1 (Tue.) by 11:30pm ET

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COURSE 2: Data Science
Participants will learn how to visualize and manipulate time-series and image data in this 5-week course.

Course Dates: May 20 (Tue.) to June 17 (Tue.) from 11:00am to 1:30pm ET
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

COURSE 2 REGISTRATION DEADLINE: May 13 (Tue.) by 11:30pm ET

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COURSE 3: Machine Learning
Participants will learn how to train and evaluate machine learning models on real-world datasets in this 5-week course.

Course Dates: July 2 (Wed.) to July 30 (Wed.) from 11:00am to 1:30pm ET
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

COURSE 3 REGISTRATION DEADLINE: June 24 (Tue.) by 11:30pm ET

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