Professional Development Certificate in Applied AI and Data Science

McGill University’s School of Continuing Studies Professional Development Certificate in Applied AI and Data Science is designed to equip professionals with the skills needed to transform data into meaningful, decision-ready insights. In an economy increasingly driven by data, the program emphasizes not only technical proficiency but also the ability to communicate analytical value to stakeholders. By bridging theory and practice, it prepares learners to apply artificial intelligence and data science concepts in real-world business contexts.

Geared toward professionals seeking to transition into technical or data-focused roles, the program delivers hands-on training using industry-standard tools. Participants work extensively with Python to manage large datasets, build and evaluate predictive and classification models, and apply machine learning techniques across the full data lifecycle. Visualization and storytelling are strengthened through the use of Tableau and SQL, while emerging technologies such as Generative AI are integrated to address complex, modern business challenges.

The curriculum is rigorous and execution-focused, offering end-to-end exposure to data exploration, model performance evaluation, and analytical insight generation. Complementary courses in areas such as Generative AI and Agentic AI allow learners to tailor the program to evolving interests and career objectives. Through practical application and immersive learning, graduates are prepared to contribute effectively to high-performance data and analytics teams.

Admission Requirements

• Bachelor of Engineering (B.Eng.), Bachelor of Science (B.Sc.), Bachelor of Commerce in MIS (B.Com MIS), or Bachelor’s degree in another field and completed the course Introduction to Data Analytics with Python.
• 21 years of age or older.

What You’ll Learn

• Turn business and technical needs into clear data science projects.
• Build end-to-end data science solutions on structured and unstructured data.
• Prepare and analyze large datasets using batch and streaming methods.
• Apply and evaluate machine learning models for prediction and classification.
• Use Generative AI to solve complex problems.