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Computer science
Connect programming, data cleaning, analysis, and communication into a repeatable workflow.
raw = [12, 15, None, 18]
clean = [value for value in raw if value is not None]COURSE CURRICULUM
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Learn how from a measurable question to a data product works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how data dictionaries, notebooks, and reproducible structure works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how raw values, identifiers, provenance, and state works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how tabular, temporal, spatial, text, and missing data works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how transformations, joins, aggregations, and leakage works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how cohorts, filters, and decision boundaries works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how batch work, vectorization, and pipeline execution works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how functions, features, and reusable transformations works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how tables, relationships, feature matrices, and labels works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how text cleaning, categoricals, dates, and encoding works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how data quality, bias, drift, and validation works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how projects, environments, versioning, and lineage works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how models, metrics, experiments, and communication works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how files, databases, apis, and scalable processing works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how testing data contracts and analytical claims works in Data Science, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.