An Introduction to Statistical Reasoning and Data Science: This course taught the foundations of data science and statistical reasoning through "a combination of logical thinking, mathematics, computer simulation, and oral and written discussion and analysis". It ultimately covered traditional statistical methods along with foundations of supervised learning and some models. In every tutorial, we analysed a problem using statistical methods and presented our conclusions to the class. A more detailed description is available on the calendar (course catalogue) or at the course website. The course culminated in a group data analysis project that analysed global internet usage with R on a multitude of factors, e.g, region and socioeconomic status.

Check out the final project slideshow.

Mathematical Expression and Reasoning for Computer Science: This course was an introduction to theoretical computer science with an emphasis on developing the foundations of "abstraction and rigour". The course used proofs to develop "precise expressions of mathematical ideas, including definitions and theorem" and mainly applied them to the running times of iterative programs (Big-Oh, Big-Omega, Big-Theta). A more detailed description is available on the calendar (course catalogue) or at the cached course website.

Check out problem set 4: our work or the handout.

Introduction to Computer Science: This course taught the much of the foundations of computer science using python. The main concepts covered were "Abstract data types and data structures for implementing them. Linked data structures. Encapsulation and information-hiding. Object-oriented programming. Specifications. Analyzing the efficiency of programs. Recursion." Our final project (assignment 2) was to create a recursive tree-based data structure that would represent music efficiently (and thus be able to play correctly formatted songs). The course website is no longer up, but here is the syllabus. More information about the course is also available on the calendar.

Calculus! (Calculus with proofs): This course covered differential and integral calculus with an emphasis on proofs. Ultimately this course expected us to know how to the theoretical foundations of calculus concepts in conjunction with the computational methods. For example, it was expected of us to be able to use the formal definition of a certain series to prove its divergence. More information is available on the calendar (course catalogue) or find some of the course materials used in one section.

Probability with Computer Applications: This course taught the foundations of probability through calculations and some proofs of core concepts. Notable concepts include: "random variables, discrete and continuous probability distributions, expectation and variance, independence, conditional probability". These concepts were applied to various common distributions not limited to the normal, exponential, poisson, and geometric distributions. More information is available on the calendar (course catalogue)

Statistics for Computer Scientists: This course covered inferential and descriptive statistics from an applied and computational perspective. Assignments heavily used R to perform data analysis using concepts such as hypothesis testing, confidence intervals, regression and ANOVA. Proofs were sometimes used to reinforce theoretical foundations. More information is available on the calendar (course catalogue)

Check out some questions that I did in R:

1. Assignment 2 - handout
   • Question 1 (my work)
   • Question 2 (my work)
2. Final Problem Set - handout
   • Question 1 (my work)

Introduction to the Theory of Computation: This course was a rigourous introduction to the Theory of Computation. It covered topic such as various forms of induction, running times of recursive algorithms, program correctness, and state machines (NFAs and DFAs) using formal languages. The course consisted of weekly quizzes, tests, and assignments that would consist of proof based questions. A more detailed description is available on the calendar (course catalogue) or at the course website.

Software Design: This course was a project based course, covering software design concepts, namely SOLID design principles and design patterns noted by the Gang of Four. Practical experience of design patterns and SOLID design principles were reinforced by practical experience in using Android Studio to create a game in a group setting. Emphasis was placed on writing clean code for future extensibility. A more detailed description is available on the calendar (course catalogue).

Software Tools and Systems Programming: This course covered programming in bash and C. Emphasis was placed on being able to utilize bash commands to efficiently assist in develop programs in C in addition to writing Bash programs. Assignments required C and Bash, and the final assignment focused on creating a twitter-like sever and client, with emphasis placed on correctly writing asynchronous and synchronous code. A more detailed description is available on the calendar (course catalogue).

Calculus II: This course taught multivariate calculus, covering vector calculus, line integrals, and surface integrals in addition to differential and integral calculus of multiple variables. Additionally, emphasis was placed on applications from mainly physics and sometimes statistics. A more detailed description is available on the calendar (course catalogue).

Linear Algebra I: This course covered core concepts in linear algebra, such as eigenvalues and eigenvectors, spans, linear dependence/independence, cross/dot products, and linear transformations. A more detailed description is available on the calendar (course catalogue).

Principles of Microeconomics: This course taught the foundations of microeconomics. It covered core concepts such as pricing, externalities, taxes, introductory game theory, and monopolies. Emphasis was placed on developing intuition of market movements and the resulting prices. A more detailed description is available on the calendar (course catalogue).

Principles of Macroeconomics: This course taught the foundations of macroeconomics. It covered core concepts such as GDP, CPI, international trade, tariffs/taxes, monetary supply, and PPP. Similar to ECO101, emphasis was placed on developing intuition of market movements, but we used macroeconomic models in determining the behaviour of markets and resulting prices. A more detailed description is available on the calendar (course catalogue).

Check out assignment 6 (completed using R/markdown): my work

Methods of Data Analysis I: A required course for Statistics students at UofT. Goes over tests used for SLR/MLR. Interestingly, this course is accredited by the Canadian Institute of Actuaries with a minimum grade of 77 for credit, meaning I obtained actuary credit!

Methods of Data Analysis II

1. Final project - a mock consulting project investigating EDI (equity, diversity, and inclusion) goals
   • Submitted work PDF

Applied Bayesian Statistics: This course covered much of the information needed create and interpret Bayesian models from prior setting to model performance. In particular, the course focused on a justified Bayesian workflow with an emphasis on predictive checks and justified priors. Assignments varied across domains from psychology (examining maths anxiety treatments) to animal ecology (Raptor latent energy states). Overall, it was a very useful course in learning how to construct Bayesian models from scratch using Stan and R. More information is available on the course syllabus.

Check out my course assignments:

1. Homework 1 - a homework on understanding the Bayesian workflow
   • Handout PDF
   • Submitted work PDF
2. Assignment 1 - an assignment on modelling a Chlamydia infection using ODEs
   • Handout PDF
   • Submitted work PDF
3. Homework 2 - a homework on modelling maths anxiety average treatment effects
   • Handout PDF
   • Submitted work PDF
4. Assignment 2 - an assignment on modelling Raptor energy states with mixture models
   • Handout PDF
   • Submitted work PDF

Introduction to Machine Learning: This course covered a lot of the theoretical foundations for machine learning. It really emphasized the why rather than the how, which makes reading or learning about new models much easier. The course covered supervised learning and unsupervised learning, going over much of the standard material such as linear regression, PCA, the EM algorithm, and also principles like ensembling.

1. Final project - predicting math marks using machine learning
   • Handout PDF
   • Submitted work PDF
2. Homework 1 - a homework on the basics of ML, KNN, deriving cost functions by hand, and regularization
   • Handout PDF
   • Submitted work PDF
3. Homework 2 - a homework on linear separability with some linear programming, neural networks, diagnosing model performance, and hyperparameter tuning
   • Handout PDF
   • Submitted work PDF
4. Homework 3 - a homework on proving performance of boosting, and an intro to generative models using MAP and naive bayes
   • Handout PDF
   • Submitted work PDF
5. Homework 4 - a homework on mixture modelling and some Bayesian approaches
   • Handout PDF
   • Submitted work PDF

Data Structures and Analysis

1. Problem set 1 - an assignment on randomised runtimes amongst others
   • Handout PDF
   • Submitted work PDF
2. Problem set 2 - an assignment on augmented tree structures and hashing
   • Handout PDF
   • Submitted work PDF
3. Problem set 3 - an assignment on search algorithms, amortized analysis, and disjoint sets
   • Handout PDF
   • Submitted work PDF

Computer Organization

Introduction to Databases

1. Assignment 1 - an assignment on relational algebra and integrity constraints
   • Submitted work PDF with questions
2. Assignment 3 - an assignment on schema design and concepts necessary to re-factor relations in to 3NF and BCNF
   • Submitted work PDF with questions

The Design of Interactive Computational Media

1. Final project prototype - a critical design take on modern dating apps
   • Figma prototype