Applied Quantitative Methods (AQM) is a 10 month data science workshop led by industry professionals and researchers. AQM is aimed at quantitative graduate students in fields such as physics, computer science/engineering, mathematics and statistics. Successful candidates spend approximately 4-5 months learning the foundations of data science via in-class lectures and group projects.

After the learning period, team members put their skills to the test in teams of 3-4 and tackle a project for a leading company. The project teams are led by one of the program leads who is an industry professional or researcher in the field of data science. In the past, projects have been completed for TransLink, Best Buy, Lululemon, Procurify and Talent Marketplace.

Program Structure

Weekly 2 hour meetings @ UBC or SFU. Bi-weekly mini-projects. Online collaboration with GitHub & Slack.

November – March

The AQM candidates delve into data science theory, applied programming and exploratory data analysis with real, complex data sets. During this process, the value of data management and the ETL process will be enforced. Utilizing a variety of tools, candidates extract data, clean it, explore (create visualizations etc.) and use it to explain and model real-world phenomena.

April – August

The AQM team begins the well anticipated project upon a meeting with the partnered firm of that particular year. More advanced topics related to machine learning and probability are introduced relating to the the given project. Students spend this period applying the concepts learned and develop a methodology to best serve the purpose of the project. At the end of the project, the team presents its findings at the company’s headquarters and receives feedback. If accepted, the method with be deployed within the company and further development may be required.