Weekly 2 hour meetings @ UBC. Bi-weekly mini-projects. Online collaboration with GitHub & Slack.
September – December: The AQM candidates delve into statistical 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.
January – May: 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 will be deployed within the company and further development may be undertaken.