Data Science & Machine Learning
Learn each dependency first, then apply the ideas in realistic interview repositories where the right decision is not already laid out.
Learn the ideas before the interview asks you to combine them.
Five courses move from trustworthy data to probability, modeling, experiments, production ML, and applied AI.
- 01Free
Python for Data Work
Turn Python records into trustworthy, analysis-ready tables with NumPy and pandas.
7 hoursStart here - 02
Probability and Statistical Reasoning
Build probability from cases, then use it to reason about repeated observations and evidence.
10 hoursAfter 1 - 03
Classical Machine Learning
Take one prediction problem through data cleaning, modeling, deployment, and monitoring.
14 hoursAfter 1, 2 - 04
Experimentation and Product Analytics
Turn an advertising-product experiment into defensible metrics, comparisons, and a product recommendation.
9 hoursAfter 2 - 05
Applied AI and LLM Systems
Design grounded language-model workflows that can explain their evidence, abstain, and be evaluated.
12 hoursAfter 3
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.
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.
IIIModeling and product decisions
Apply evaluation skills to recommendation ranking, causal retention questions, and a stable serving boundary.
Modeling and product decisions
Apply evaluation skills to recommendation ranking, causal retention questions, and a stable serving boundary.
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.
Test the reasoning behind reliable code.
Use simulation to check probability intuition, then repair tests that make incorrect claims about working code.
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.