PYDATA Bloom Framework: An Approach to Data Science in University Education
summary
A concrete four‑year Python curriculum claims to close a skills gap by aligning Bloom’s Taxonomy with the KDD pipeline and year‑by‑year learning outcomes, starting with basics and ending in capstone deployment and peer review. It centers on PyData Bloom framework, uses Pandas NumPy Seaborn plus Anaconda and Google Colab, suggests Snowflake for warehousing, and cites 80% of jobs require data skills as motivation. The proposal maps assessments, mini‑projects and industry‑oriented capstones to produce deployable, evaluable student work.
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chapters (10)
- 0:00Welcome
- 1:02PyData Bloom Framework concept
- 5:44Gaps in data science education
- 7:11Bloom's Taxonomy relevance
- 13:58KDD process integration
- 16:47Python for data science
- 17:52PyData Bloom Framework overview
- 23:45Curriculum alignment with Bloom’s levels
- 27:01Why PyData Bloom Framework matters
- 28:28Future directions
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