UBC’s MDS condenses a traditional two-year curriculum into consecutive terms of fast-paced, five-week modules. The schedule demands full-time dedication, swift context-switching and disciplined workload management, mirroring the intensity of real-world data projects.

The program’s fast-paced format combines Statistics, Machine Learning and practical Data Science skills in every five-week module. Instead of learning each topic separately, you work on theory, modeling, and engineering at the same time. This setup keeps everything connected and helps you quickly switch between analysis, building models and using cloud tools, just like in real-world data science projects.

Core Pillars & Representative Topics

Statistics & Inference

• Probability foundations, hypothesis testing and statistical inference
• Linear & generalized regression, Bayesian methods
• Experimentation design, A/B testing and causal-inference techniques

Machine Learning & AI

• Supervised modelling, feature/model selection and hyperparameter optimization
• Deep learning architectures and training pipelines
• Unsupervised, spatial and temporal models
• Introductory natural language processing

Production-Ready Data Science

• End-to-end workflows with Git, Docker, automated testing and CI/CD
• Python packaging & publishing to PyPI
• Cloud computing on AWS.

Capstone Project – Vancouver Whitecaps FC (2 months)

• Applied the full stack of skills to build a set-piece analytics platform for Vancouver Whitecaps FC. Everything from data engineering to model deployment and visualization.
• Details: View Whitecaps Project