What Will I Learn?

Data. Knowledge. Action: these three words symbolize steps in the data science value chain.

The UBC Master of Data Science curriculum covers all stages of this chain, providing you with skills to focus on the “knowledge” section. Over 10 months, you will learn how to extract data for use in experiments, how to apply state-of-the-art techniques in data analysis and how to present extracted knowledge effectively to domain experts.

Curriculum

The UBC Master of Data Science provides you with a scientific approach to use data to explore different hypotheses. Along the way, you manipulate messy, ill-formed data to extract meaningful insights, using an appropriate data analysis approach and applying it in the right context.

The program includes 24 one-credit courses offered in four-week segments. This structure enables you to carry out detailed, focused analyses of each topic.

Courses are lab-oriented and delivered in-person with some blended online content. After this intensive 10-month program, you can appropriately select and tailor data science methods to deal with diverse data types (numeric, categorical, text, dates, graphs, etc.) across diverse subject-area domains.

The program also includes an eight-week capstone project, allowing you to work alongside other students with real-life data sets. In this project, you determine questions of interest for the data in conjunction with mentors drawn from academia, industry and non-profits. You experience the data science value chain, applying techniques you have learned to investigate the questions.

Sharing the knowledge gained through analysis of data is a critical component of data science. As part of both the courses and the capstone project, you learn how best to communicate results of data science experiments and to recommend subsequent actions to decision-makers.

By the end of the program, you are comfortable in applying statistical thinking in your data analysis, in building prototypes and small systems to support data experimentation and in using visualizations to tell a story about your experimental results.