Data Science
Turn a question into a table, clean it, plot it, and decide whether the rows support a claim — with pandas labs on ExamMaster.
Undergraduate depth: concept notes and, where they help, in-browser labs. Reading is free.
Lessons
- Data cleaningIn Data Science because a claim is only as honest as the rows — missing values, wrong types, duplicates and obvious bad rows have to be faced before you summarise.
- Exploratory analysisIn Data Science because after the sheet is trustworthy you still have to read it — totals, group-by, and the shape of a column — before you draw a chart or make a claim.
- Time seriesIn Data Science because some questions are a value over dates — the shop till each day — and you read trend and weekend season in pandas, not as an ARIMA elective.
- Charts as evidenceIn Data Science because a chart is a claim drawn from rows — bar, line, or scatter — not a Tableau or Power BI product lesson.
- Where tables liveIn Data Science because the shop sheet is usually a slice of a larger store — you pull the week you need rather than dumping every table.
- What data science isIn Data Science because the job is to turn a question into a table and decide whether the rows support a claim — before anyone fits a model.
Practise Data Science
Reading every chapter below is free and needs no account. Practice, mocks and progress live in the app.
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