TAships (& Material taught)
Business Administration and Technology Management (BAIT)

  • BAIT 507 Data Management for Business Analytics (Sept-Oct 2019)
– SQL commands, database management, user-centric access and queries
  • BAIT 509 Business Applications of Machine Learning (Jan-Feb 2019)
– applying supervised and unsupervised learning models (logistic regression, SVMs, random forest), bias-variance tradeoff, R caret, Python Sci-kit learn

Master of Data Science (MDS)

  • DSCI 554 Experimentation and Causal Inference (Mar-Apr 2020)
– experiment design, randomization, A/B testing, multiple test correction
  • DSCI 574 Spatial and Temporal Models (Feb-Mar 2020)
– time series analysis (smoothing, decomposition, autocorrelation function), ARMA models, variogram models
  • DSCI 562 Regression II (Jan-Feb 2020)
– GLMs, mixed effects, robust regression, missing data
  • DSCI 561 Regression I (Nov-Dec 2019)
– design matrices, simple regression models with R lm(), interpreting coefficients, regression diagnostics, prediction

Learning and Writing Peer Educator @ Simon Fraser University (SFU) (Sept 2015-Aug 2016)

  • Worked with fellow university students improve their writing, study, and note taking strategies through one-on-one advising sessions.