Courses

I’ve taken a lot of classes over my time in school, here are some of my favourites, or ones that I found the most interesting!

Economics

ECON 408 Computational Methods in Macroeconomics

This was definitely one of my favourite classes I’ve taken. It was really a culmination of so many other classes. This class integrated macroeconomic theory, statistics, linear algebra, and computer science in an elegant and insightful way. Some topics included:

• Asset pricing
• Linear state space models
• Models of consumption and saving
• Rational expectations
• Lucas trees

The professor for this class was Jesse Perla, an amazing prof. If you can go to his office hours and have a chat with him, he deeply cares about his students’ learning and will help in any way he can. You can also check out his github here, where the materials for past versions of the course are available. If you are at all interested in economics and computer science, I highly recommend taking this course.

ECON 425 Advanced Econometrics

Econometrics was a challenging course, but it is definitely an important topic if you intend to go into research or academia. The problems sets in this class were a bit difficult, and the grading was sometimes a bit ambiguous or unclear. Econometrics involves similar concepts to math or statistics courses, except there is a specific application in mind. This causes some overlap between this course and STAT 305. However, there are topics unique to ECON 425 that make it work taking including:

• Endogeneity and IV in Models
• Linear Panel Models
• Limited Dependent Variables
• Program Evaluations

ECON 370 Cost-Benefit Analysis

I wish I took this class earlier than my 3rd year. It provides a lot of foundational concepts to economics and finance such as discounting, net present value, and time value of money. The course taught by professor Clive Chapple was challenging but very well structured which amde it easy to follow. Cost-Benefit analysis is particularly useful for anyone who wants to work in the public sector, but it does have some uses in the private market.

ECON 345 Money and Banking

I took this class with Geoffery Newman, he’s a fun prof in my opinion, and although he is not as infamous as Gateman, he does have his moments. He structures his class like what I imagined a university course to be. He gives out notes and lectures from them. This course was very exam heavy with only a few assignments throughout the term. Since professor Newman has been lecturing for so long, he teaches his own models and which includes his three-sector model and his risk-return portfolio theory. Aside from that some topics covered in the class include

• The Canadian Banking system
• Financial system basics (bonds, business cycle)
• Portfolio Theory
• Tobin model

Statistics

STAT 306 Finding Relationships in Data

I’d say this was a good course overall. I took it over the summer so I may have gotten off easier than those who took it during the winter term. This goes into more detail about linear models and regression, implementing some math and showing the derivation as well. This course also has a project, however I found it to be a bit limited as we were only able to choose from a few datasets; you can check it out here. Some topics covered in this course include:

• Linear, Binary Models (Matrix interpretations)
• Model Diagnostics (Correlation, R-squared)
• Model Selection (Leverage & Influence, VIF, Mallow’s Cp, Cook’s distance, AIC)
• Interaction

STAT 305 Introduction to Statistical Inference

Another core course for statistics majors, very concept heavy. This class is very similar to a math class, with practice and an understanding of the theory behind the methods, it’s not too bad. I also took this during the summer and would recommend to do so if you can. Some topics we covered in the class included

• Estimators (Moments, MLE, MGF)
• Hypothesis Testing & Confidence Intervals
• Bayesian Inference

STAT 301 Statistical Modelling for Data Science

The final course in the “data science stream” of statistics courses that follows the same structure of the previous two classes. This class focuses on applying regression models in data science projects. There was a group project in this course, with an assigned dataset and team it was a bit different than previous projects. You can view my project here. Here are some topics we covered in the class:

• A/B Testing
• Simple Linear Regression
• Multiple Regression
• Explanatory vs. Predictive Modeling
• Inference for Regression

STAT 300 Intermediate Statistics for Applications

A good course for non-statistics majors, a lot of topics in this course are touched on in other courses in more detail. STAT 300 has a broad range of topics but does not go into much depth in any of them. Nonetheless, I thoroughly enjoyed taking this class with prof Ben Burr. Some of the topics touched on are as follows:

• Nonparametric tests
• Model fit
• Bootstrapping
• ANOVA
• Regression
• Time series

STAT 201 Statistical Inference for Data Science

As a part of the “data science stream” of statistics courses, it follows the same structure of DSCI 100. With a group project throughout the term, it builds on the content of the prerequisite courses. The content focuses on statistical inference, bootstrapping, confidence intervals, and hypothesis testing. For my project, we did some inference on credit card default likeliness. You can view the project here.

Computer Science

CPSC 210 Software Construction

This class was great because it included a personal project throughout the term. The project is very open-ended and the TAs are there to give you advice and help, so you really get out of it whatever you put in. The structure is very similar to that of a personal project so this is a good way to get into doing personal projects if you don’t know how they work. Personally, I found it to be somewhat hard because of my limited experience with programming at the time, however it was a good learning experience. You can check out my project here!

CPSC 330 Applied Machine Learning

I enjoyed this class as it covered a wide range of topics, with relatively straightforward concepts and applications. This course does not contain any creation of machine learning models, but shows you how to implement them and how they work. Some of the models covered were

• Unsupervised Models (k-Means, Recommenders, NLP, Time Series, Survival Analysis, Computer Vision)
• Supervised Models (k-NN, SVM RBF, Linear Models, Ensembles)
• Classification and Regression Metrics
• Feature Importances, Feature Engineering

Other

ENGL 110 Approaches to Literature and Culture

I went into this class fearing reading and writing about literature at a college level, however, I was pleasantly surprised and thoroughly enjoyed the class. I had Miranda Burgess for this class and I highly recommend taking a class with her. My TA, Thea Skeide, was also very supportive throughout the course. We read the following books, which I would recommend

• Belovcd
• The Marrow Theives
• Frankenstein
• The Best We Could Do

DSCI 100 Introduction to Data Science

A good introductory course to data science. I took it during the summer which allowed me to focus on each topics more. The format of the course is really nice, solely worksheets and tutorials that you run on Syzygy, UBC’s online Jupyter server. This makes it so that you can learn on your own if you’re busy with other classes or work. There is a group project, mine is linked here. Here are some of the topics covered in the course

• Reading, Cleaning, Wrangling Data
• Data Visualization
• Classification, Regression, Clustering
• Statistical Inference