Please contribute! Feel free to place notebooks or example code under the topics directory.
Suggested weekly topics:
Week 1 — Python Coding Drills
- Arrays, strings, dicts, complexity, and edge-case thinking.
- LeetCode-style problem practice (1D/2D arrays, maps, string ops).
- Quick runtime analysis and Python idioms for speed.
Week 2 — Basic Probability & Counting
- Sample spaces, independence, conditional probability.
- Counting problems, permutations/combinations, inclusion-exclusion.
- Problems: dice, cards, and urn draws.
- Introduce Bayes’ theorem intuitively.
Week 3 — Random Variables & Expectation
- Discrete distributions (Bernoulli, Binomial, Geometric, Poisson).
- Continuous distributions (Uniform, Exponential).
- Expected value, variance, and linearity of expectation.
- Simulation sanity checks in Python.
Week 4 — Joint, Marginal & Conditional Distributions
- Covariance and correlation.
- Conditional expectation and independence.
- Problems: covariance between dice rolls, correlation interpretation.
Week 5 — Inference & Hypothesis Testing
- Z-test vs t-test, Type I/II errors, p-values, statistical power.
- Coin fairness, A/B test examples.
- Practice designing simple hypothesis tests from data.
Week 6 — Sampling, LLN & CLT
- Law of Large Numbers and Central Limit Theorem.
- Confidence intervals and bootstrapping intuition.
- Applying CLT to A/B testing, conversion rate differences.
Week 7 — Linear Models & Loss Functions
- Linear regression derivation, least squares, gradient descent intuition.
- Regularization (L1, L2), bias-variance tradeoff.
- Derive closed-form and interpret coefficients.
Week 8 — Classification, Metrics, and Probabilistic Thinking
- Logistic regression, likelihood and log-odds.
- Evaluation metrics: accuracy, precision/recall, ROC-AUC.
- Calibrated probabilities, confusion matrix reasoning.
Week 9 — Model Selection, Validation, and Overfitting
- Cross-validation, time-series split, learning curves.
- AIC/BIC, early stopping.
- Feature leakage and robust validation under realistic constraints.
Week 10 — Python Data Structures & Coding Review
- Mixed interview problems: data parsing, simulation, aggregation.
- Quick review of time/space tradeoffs.
- Writing clean, testable code under interview conditions.
Week 11 — SQL & Data Manipulation
- Aggregation queries, joins, subqueries, window functions.
- Real-world examples: churn rate, top-N users, retention tables.
- Optimization mindset: when to use GROUP BY vs window.
Week 12 — Case Study & Full Integration
- End-to-end mock interviews:
- Prob/stat + ML + code in one session.
- E.g., design an A/B test → analyze → interpret model results.
- Reflection: what to keep improving weekly.