Welcome to my teaching page! Here, you can find information about the courses I have taught, workshops I have led, and other teaching-related activities.
Current Teaching (09/2024 – now)
Master of Data Science, Thompson Rivers University
- DASC 5410: Database Management for Data Science
- ADSC 3610: Database Systems in Applied Data Science 2
- ADSC 3910: Applied Data Science Integrated Practice 2
- ADSC 4910: Applied Data Science Integrated Practice 3
Topics: Deep learning, neural networks, E-R modeling, SQL, MongoDB, Spark, Data ethics and privacy
Previous Teaching Experience (08/2021 – 08/2024)
Instructor, Master of Data Science, UBC (Cohort 80-120 students)
- BAIT 509: Machine Learning in Business Applications (Python)
Topics: Decision trees, KNN, SVM, feature engineering, model evaluation, hyperparameter optimization, linear regression, logistic regression
- DSCI 511: Python Programming for Data Science (Python)
Topics: Object-oriented programming, functions, classes, data manipulation with numpy, pandas
- DSCI 551: Descriptive Statistics and Probability for Data Science ®
Topics: Random variables, conditional probability, joint probability
- DSCI 552: Statistical Inference and Computation I ®
Topics: Estimation, bootstrapping, hypothesis testing, ANOVA, t-tests, MLE
- DSCI 542: Communication and Argumentation in Data Science
Topics: Technical writing, presentation skills, statistical misconceptions
- DSCI 574: Temporal and Spatial Models (Python)
Topics: Time-series, ARIMA, LSTM, deep learning, kriging
- DSCI 100: Introduction to Data Science (~180 students x 2 terms) ®
Topics: Data manipulation in tidyverse, ggplot2, KNN, linear regression
- STAT 201: Statistical Inference for Data Science (~160 students) ®
Topics: Estimation, bootstrapping, hypothesis testing, ANOVA, t-tests
Capstone Coordination (09/2022 – 08/2024)
Capstone Coordinator, Master of Data Science, UBC
- Oversaw and coordinated the MDS capstone program, comprising 20 data science projects in collaboration with industry partners and a cohort of 80-100 students.
- Directly mentored six data science projects across various sectors:
- Forecasting models of stock price
- Forecasting models of energy prices
- Forecasting models of recycling demand and surge pricing models
- Identifying soccer formations from optical tracking and match event data
- Recommender system for career advising
Adjunct Lecturer (01–05/2021)
Master of Applied Data Science – Online, School of Information, University of Michigan (Cohort ~170 students)
- SIADS 505: Data Manipulation (pandas) - Python
- SIADS 532: Data Mining I (item sets, vectors, matrices, sequences) - Python
- SIADS 632: Data Mining II (N-gram, Hidden Markov, time-series) - Python
- SIADS 680: Learning Analytics (Supervised Learning, Data Visualization) - Python