Data Science

Time Series Fundamentals

Master time series forecasting: trend and seasonal decomposition, stationarity and differencing, ACF/PACF, exponential smoothing and ARIMA/SARIMA, lag-feature gradient boosting, temporal backtesting with MAE, MAPE, sMAPE and MASE, prediction intervals, and the leakage traps interviewers probe.

Descriptive Statistics & Exploratory Data Analysis

Learn to describe centre, spread and shape, choose the right plot for the question, detect outliers and missing-data mechanisms, run data quality checks that catch leakage and unit errors, avoid Simpson's paradox, and follow a repeatable EDA checklist in pandas.

Probability Fundamentals

Master conditional probability, Bayes' theorem and the base-rate fallacy, random variables, expectation and variance, the six distributions data scientists actually meet, the Law of Large Numbers and Central Limit Theorem, and the classic interview puzzles.

Logistic Regression & Linear Classifiers

Master logistic regression: sigmoid and log-odds, odds-ratio interpretation with worked numbers, maximum likelihood and log-loss, linear decision boundaries, cost-aware thresholds, calibration, class imbalance handling, multiclass softmax, and how it compares to SVMs, naive Bayes and trees.

Hypothesis Testing & Statistical Inference

Learn hypothesis testing from first principles: p-values and their misreadings, Type I/II errors and power, z, t, Welch, paired, chi-square and proportion tests, non-parametric alternatives, confidence intervals, effect sizes, multiple-comparison corrections, bootstrap and sample-size formulas.

Causal Inference Basics

Learn potential outcomes, confounding and colliders, and the observational toolkit — regression adjustment, matching, IPW, difference-in-differences, regression discontinuity, instrumental variables and synthetic control — with worked examples and failure modes.

Linear Regression

Learn how ordinary least squares fits a line, how to read coefficients (dummies, interactions, log transforms), the Gauss-Markov assumptions and how to diagnose violations, R-squared traps, standard errors and confidence intervals, VIF, leverage and Cook's distance.

A/B Testing & Online Experimentation

Learn to design trustworthy online experiments: pick metrics and randomisation units, size a test with power and MDE, catch SRM and peeking, cut variance with CUPED, and read lifts, segments and holdouts correctly.

SQL for Data Analysis

Master analytical PostgreSQL: query evaluation order, join fan-out traps, CTEs, window functions with frames, date bucketing, cohort retention, funnels, sessionisation, NULL pitfalls, CASE pivots, deduplication and the classic SQL interview questions.