COURSE CURRICULUM

The Machine Learning Analyst diploma program is structured over four terms to provide students with a comprehensive blend of technical, mathematical, and business education. In the first term, students build foundational knowledge with courses such as Introduction to Computing, Introduction to Machine Learning and Data Science, Scientific and Technical Writing, Introductory Calculus, and Statistics I. These courses lay the groundwork for understanding programming, mathematical principles, and communication skills essential in data-centric roles. In the second term, students delve deeper into core subjects with Database Management Systems, Canadian Business Fundamentals, Introduction to Artificial Intelligence, Linear Methods I, and Algorithms and Data Structures. These courses enhance analytical thinking and prepare students to work with structured data and basic AI models. The third term shifts focus to application and strategy through Public Speaking for Business, Technology Business Strategy and Execution, Data Preparation Analytics, and Machine Learning I. Students also begin their first Machine Learning Work Integrated Project, where they tackle real-world problems in collaboration with industry partners. In the fourth term, the curriculum covers advanced topics including Technology Ethics and Society, Machine Learning Deployment and Software Development, and Machine Learning II. The final capstone, Machine Learning Work Integrated Project II, allows students to showcase their skills in a second industry project. Students also choose one business elective such as Business Values and Ethics, Venture Development, or Financial Markets and FinTech, allowing them to tailor the program to specific interests or career goals. This program structure ensures graduates are not only proficient in technical and analytical skills but also well-versed in business strategy and ethical considerations, making them job-ready for a range of roles in the AI and machine learning industry.