Statistics, modeling, and experimentation
Data Scientist
Build the programming, SQL, statistics, and modeling workflow needed to answer questions and evaluate predictions honestly.
By the end
Explore data, train and evaluate models, design experiments, and communicate the evidence behind a recommendation.
Start here
Start with Python for Data Work Python for Data Work
Turn Python records into trustworthy, analysis-ready tables with NumPy and pandas.
Work confidently with data
Use Python for analysis and SQL to retrieve the records your investigation needs.
- Data Science & MLPython for Data WorkTurn Python records into trustworthy, analysis-ready tables with NumPy and pandas.
- SQL & DataFiltering RowsAsk a table for exactly the rows and columns you mean, and nothing else.
- SQL & DataGrouping and AggregationCollapse many rows into one answer per group, and know exactly which rows each number counted.
- SQL & DataJoining TablesPut rows from two tables side by side, keep the ones that should survive, and find the ones with no partner.
- SQL & DataSubqueries, CTEs and Set OperationsUse one query inside another: as a value, as a list, as a table, as a named step, and as a set to compare against.
Measure uncertainty and build models
Develop statistical reasoning before training models, choosing features, and judging results.
- Data Science & MLProbability and Statistical ReasoningBuild probability from cases, then use it to reason about repeated observations and evidence.
- Data Science & MLClassical Machine LearningTake one prediction problem through data cleaning, modeling, deployment, and monitoring.
- Data Science & MLFeature EngineeringTurn raw data into useful ML features, with reliable splits, transformations, and model comparisons.
Make causal and product decisions
Separate prediction from experimentation and causal claims.
- Data Science & MLExperimentation and Product AnalyticsTurn an advertising-product experiment into defensible metrics, comparisons, and a product recommendation.
- Data Science & MLCausal InferenceLearn what causal inference means, why assumptions matter, and how to estimate treatment effects step by step.
Choose a specialization
Pick the branch that matches the problems you want to solve; these courses are optional, not a fixed sequence.
- Data Science & ML · OptionalCollaborative Filtering Recommender SystemsPredict tastes and rank recommendations from a sparse ratings matrix using neighborhoods, latent factors, and honest evaluation.
- Data Science & ML · OptionalApplied AI and LLM SystemsDesign grounded language-model workflows that can explain their evidence, abstain, and be evaluated.
- Data Science & ML · OptionalReinforcement LearningLearn through trial and error: start with bandits, then teach a small robot to reach a goal with Q-learning.