Statistics
Minor at the University of Toronto. Provides rigorous probabilistic foundations for Data Science and ML.
Topics covered:
- Probability theory (measure-theoretic)
- Statistical inference: MLE, Bayesian, frequentist
- Regression analysis, GLMs
- Hypothesis testing, A/B testing design
- Time series analysis
Tools
R for academic statistical work, Python + scipy.stats for applied work.
Related: Mathematics, R, Data Science