Courses I've taken

Definitely one of my favourite classes I've taken. A real culmination of so many others. It integrated macroeconomic theory, statistics, linear algebra, and computer science in an elegant and insightful way. The professor, Jesse Perla, is amazing. If you can go to his office hours, do it. He deeply cares about his students' learning. His course materials are also on GitHub for past versions of the course.

A challenging course, but an important one if you intend to go into research or academia. The problem sets were difficult and grading was sometimes ambiguous, but the content is unique and valuable. Econometrics applies concepts from math and statistics to specific empirical questions. There's some overlap with STAT 305, but the causal inference topics are unique to this course.

I wish I had taken this earlier than my 3rd year. It covers a lot of foundational concepts in economics and finance: discounting, net present value, and time value of money. Professor Clive Chapple taught it in a challenging but very well-structured way, making it easy to follow. Particularly useful for anyone interested in the public sector.

I took this with Geoffrey Newman, a fun prof who structures class the way you'd imagine a university lecture to be. He hands out notes and lectures from them. The course was exam-heavy with few assignments. Since he's been lecturing for so long, he teaches some of his own models, including his three-sector model and risk-return portfolio theory. Great for understanding the Canadian financial system.

A good course overall. I took it over the summer so it may have been a lighter workload than the winter term. Goes into more detail on linear models and regression with actual mathematical derivations. The course project was a bit limited (you pick from a few provided datasets), but still a useful exercise in applying the methods.

A core course for statistics majors and very concept-heavy, similar in feel to a math class. With practice and an understanding of the theory behind the methods, it's manageable. I took it during the summer and would recommend doing so if you can.

The final course in the data science stream of statistics courses. Focuses on applying regression models to data science projects. The group project had an assigned dataset and team, which was a bit different from other courses, good practice for real collaborative work.

A good course for non-statistics majors. The course covers a broad range of topics without going too deep into any of them. I thoroughly enjoyed taking it with Prof. Ben Burr. If you want a solid survey of applied statistics methods, this is it.

Part of the data science stream of statistics courses, built on the structure of DSCI 100. A group project runs throughout the term. The content focuses on statistical inference, bootstrapping, confidence intervals, and hypothesis testing. My team did inference on credit card default likelihood.

Great because of the open-ended personal project that runs the whole term. You really get out of it what you put in, with TAs there to advise and help. The structure mirrors a real personal project, which makes it a good gateway into independent software work. I found it challenging at the time due to limited programming experience, but it was a valuable learning experience.

Covered a wide range of ML topics with relatively accessible concepts and practical applications. The course doesn't have you build models from scratch, but shows you how to implement and use them effectively; a great applied survey of the field.

I went in fearing college-level literary analysis but was pleasantly surprised and I thoroughly enjoyed it. Professor Miranda Burgess is wonderful; I'd recommend taking any class she teaches. My TA, Thea Skeide, was also very supportive throughout. The reading list was genuinely great.

A solid introductory course. I took it over the summer which let me focus more on each topic. The format is great, solely worksheets and tutorials run on Syzygy, UBC's online Jupyter server, so you can learn at your own pace. There's a group project throughout the term.