Build a reproducible path from permitted data acquisition through numerical analysis and cautious interpretation.
Follow a practical data workflow from a public web page or API to structured data, numerical operations, and an analysis of relationships. Learners consider permission, respectful access, schema and type checks, missingness, and reproducible transformations before interpreting results. The course's original assignments distinguish correlation from causation and ask learners to document provenance and uncertainty. Examples are for learning and do not authorize scraping a site or collecting data that its owner has not made available for that use.
Plan a data acquisition workflow that checks permissions, request limits, provenance, and schema changes. Describe how array operations and transformation choices affect an analysis. Interpret a correlation analysis with appropriate caveats and reproducible records.
Comfort reading basic Python and tabular data is helpful; familiarity with variables, functions, and data frames is recommended. You may complete the work on paper. Use only sources whose terms permit your intended access, avoid personal or sensitive data, and never bypass access controls or rate limits.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Intermediate · Full course.
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