Getting Started

Programming fundamentals for researchers — data manipulation, visualization, and workflows

Hypothesis testing, inference, and distributions — the foundation for empirical work

Prediction, tree models, and cross-validation — for forecasting and pattern recognition

Treatment effects, DiD, RDD, and IV — answer 'what if' questions with data

PM thinking, metrics selection, and product strategy — build intuition for what to build and why

A/B testing, power analysis, and statistical rigor — bridge your training to industry experiments

Querying data, joins, and aggregations — essential for any data role

Arrays, linked lists, trees, and graphs — the building blocks for efficient algorithms

Sorting, searching, dynamic programming, and graph algorithms — the patterns that power interviews

Coding interview practice — curated problem sets and patterns for FAANG interviews

Web scraping, APIs, and workflow automation — collect and process data at scale

Linear programming, convex optimization, and combinatorial methods — solve pricing, scheduling, and allocation problems

Build AI agents that reason, plan, and execute — tool use, multi-agent systems, and orchestration

Time series analysis, demand forecasting, and prediction — ARIMA, Prophet, and modern ML methods for business planning

Dynamic pricing, subscription dynamics, and revenue optimization — price elasticity estimation and causal inference form the core toolkit

Mechanism design and auction theory in industry — researchers with game theory backgrounds have direct advantages here

CLV modeling, attribution, and retention analytics — researchers' panel data experience transfers directly to cohort analysis

Fraud detection, credit risk, and trust & safety ML — causal inference handles adversarial, imbalanced problems

Random utility models underpin collaborative filtering — preference elicitation mirrors revealed preference theory

Learning-to-rank combines ML with causal inference — counterfactual learning addresses position bias like selection bias correction

Minimal starter kit for routing, dispatch, and demand forecasting — free, practitioner-focused resources

Growth loops, funnel analysis, and experimentation — diff-in-diff and regression discontinuity power incrementality testing