Subjects

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.

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.