Our Curriculum, Part 1

Computer Science & Machine Learning

This is the first in what will hopefully become a series of posts on our curriculum for the Master of Data Science (MDS) program at UBC. Our program is structured as six four-week blocks, each containing four mini-courses, for a total of 24 mini-courses. Each of these mini-courses is about... [Read More]

Visualizing massive open online courses

Our MDS capstone project

After eight months of coursework, the UBC Master of Data Science (MDS) program concludes with a 2-month Capstone project. We partnered with Ido Roll from UBC’s Centre for Teaching, Learning and Technology (CTLT) to analyze data from UBC’s massive open online courses (MOOCs). UBC offers dozens of MOOCs to thousands... [Read More]

Statistics-ML dictionary

One of the most rewarding aspects of working on the UBC Master of Data Science program has been the close collaboration between my home department, computer science, and the statistics department here at UBC. The collaboration has also come with a challenge, though: the two communities often use different words... [Read More]

Communication in data science

More than just the final report

When people stress the importance of good communication in data science, they are usually saying something like what Hadley Wickham says in his book R for Data Science: “[It] doesn’t matter how great your analysis is unless you can explain it to others: you need to communicate your results.” In... [Read More]

Algorithms and optimization

Over the years I’ve struggled with the disconnect between “algorithms” — as a student might see in a standard algorithms and data structures class — and optimization. Several of the algorithms taught in such courses are in fact instances of (discrete) optimization: for example, dynamic programming (DP), or Dijkstra’s algorithm... [Read More]