Data Science & Machine Learning
Build the foundations, choose a specialization, then apply it to real problems.
Build your foundations
Three courses to establish the core workflow.
- 01
Python for Data Work
Turn Python records into trustworthy, analysis-ready tables with NumPy and pandas.
Open courseBefore thisNo prerequisite courses - 02
Probability and Statistical Reasoning
Build probability from cases, then use it to reason about repeated observations and evidence.
Open courseBefore thisPython for Data WorkPut it into practiceExercise · Monty Hall: Stay or Switch? Project · Free Shipping Threshold Experiment - 03
Classical Machine Learning
Take one prediction problem through data cleaning, modeling, deployment, and monitoring.
Open course
Explore a specialization
Pick what matches your goals. Follow each course’s prerequisites; these are not sequential steps.
- ↗
Collaborative Filtering Recommender Systems
Predict tastes and rank recommendations from a sparse ratings matrix using neighborhoods, latent factors, and honest evaluation.
Open courseBefore thisClassical Machine Learning - ↗
Experimentation and Product Analytics
Turn an advertising-product experiment into defensible metrics, comparisons, and a product recommendation.
Open courseBefore thisProbability and Statistical ReasoningPut it into practiceExercise · Repair the A/B Test Analyzer Project · Free Shipping Threshold Experiment - ↗
Applied AI and LLM Systems
Design grounded language-model workflows that can explain their evidence, abstain, and be evaluated.
Open courseBefore thisClassical Machine Learning - ↗
Reinforcement Learning
Learn through trial and error: start with bandits, then teach a small robot to reach a goal with Q-learning.
Open coursePut it into practiceProject · Perishable Inventory Replenishment
How each interview works
- 01Read the repositoryTrace the contract before changing code.
- 02Make a focused changeRepair, complete, extend, or test one behavior.
- 03Explain the decisionDefend edge cases, trade-offs, and complexity.
Each repository prepares the next one.
Follow the path in order, or enter at the stage that matches your interview.
IPython and pairing foundations
Learn to read an unfamiliar contract, preserve grid state, review classifier decisions, and finish with a first leakage-safe evaluation.
Python and pairing foundations
Learn to read an unfamiliar contract, preserve grid state, review classifier decisions, and finish with a first leakage-safe evaluation.
Conway's Game of Life with OOP
Diagnose why a complete-looking Game of Life implementation corrupts a generation while it updates the board.
Inbox Safety Cutoff
Trace a working classifier evaluator, predict how its cutoff changes errors, and defend a precision–recall decision.
Trail Arrival Model Review
Build a leakage-safe offline review, compare the current model with a candidate, and check whether aggregate improvement hides a harmful trail segment.
IIProbability, statistics, and experiments
Build trustworthy decisions from classifier metrics, controlled experiments, uncertainty, and calibrated probabilities.
Probability, statistics, and experiments
Build trustworthy decisions from classifier metrics, controlled experiments, uncertainty, and calibrated probabilities.
Alice and Bob Game Engine
Find and repair three subtle state-transition bugs in a working-looking rock-paper-scissors engine.
Repair the A/B Test Analyzer
Repair the campaign-level analysis, compare company segments and budget distributions, and return a decision that preserves conflicting business evidence.
Bayesian Rate Monitor
Complete focused Beta–Bernoulli updates and reproducible posterior comparison inside an otherwise working model.
IIIModeling and product decisions
Apply evaluation skills to recommendation ranking, causal retention questions, and a leakage-free offline validation.
Modeling and product decisions
Apply evaluation skills to recommendation ranking, causal retention questions, and a leakage-free offline validation.
Neighborhood Picks
Diagnose a neighbor-based recommender whose similarity and score calculations produce plausible but incorrect rankings.
Mentor Stipend Retention Study
Diagnose a plausible before-after analysis that wrongly attributes a shared retention trend to the mentor stipend.
Validation Leak Audit
Audit an offline model evaluator for a data leak, repair it so learned preprocessing is fit on the training split only, and restore an honest validation score.
IVProduction ML and AI systems
Move from a stable serving boundary to auditable risk decisions, grounded retrieval, and inference memory.
Production ML and AI systems
Move from a stable serving boundary to auditable risk decisions, grounded retrieval, and inference memory.
Marketplace Risk Gate
Repair a real-time risk gate whose time window and decision precedence can silently approve or delay the wrong orders.
Incident Runbook RAG
Complete the offline indexing and online retrieval boundaries, then defend the full RAG system design.
Shared Generation Memory
Complete an LRU request-memory manager that updates recency, replaces entries safely, and evicts enough sessions to respect capacity.
Test the reasoning behind reliable code.
Check probability intuition with a simulation, repair tests that make incorrect claims about working code, and recommend to a member the system has never seen.
Monty Hall: Stay or Switch?
Complete a reproducible simulation, preserve the host’s information rule, and connect the observed win rates to conditional probability.
Repair the Model Ranking Tests
Repair three incorrect pytest expectations, then prove the corrected tests detect broken ranking implementations.
Cold-Start Picks
Recommend movies to a brand-new member from a user–movie rating matrix, handling the unrated (None) entries that make the new member cold.