Data 9: Practical Data Science

Data 9 (taught at El Camino College as CSCI 9: Practical Data Science) is an intermediate data science course designed for students who have completed a foundations course like Data 8. It bridges introductory data science and the tools and workflows used in industry-without the mathematical intensity of courses like Data 100.

What is Data 9?¶

Data 9 helps students build practical, job-ready data science skills while still in community college. The course emphasizes the full data science lifecycle: working with tabular data in pandas, visualization with matplotlib, seaborn, and plotly, modeling with scikit-learn, and querying relational databases with SQL.

Students who have taken Data 8 (often using the datascience library and Berkeley’s introductory tables) transition here to industry-standard Python tooling. The focus is on looking up documentation, thinking critically about data, and communicating findings-not memorizing syntax.

Course Materials¶

All core materials are openly available:

• Course website (JupyterLite) - temporary ZeusLite deployment with a weekly schedule linking to lecture demos and labs in the browser (no local install required). This URL may change as the course site moves to a permanent home.

• Online textbook - MyST/Jupyter Book course notes covering pandas, visualization, sampling, modeling, and SQL. Source: ds-modules/ecc-cs9-textbook on GitHub.

• Lecture materials - slide decks and lecture notebooks organized by topic.

• Labs and projects - Jupyter notebooks for projects and hands-on work (CalEnviroScreen, linear regression, Stanford Open Policing Project, and more).

Who Is This For?¶

This guide is designed for:

• Community college instructors looking for a practical post–Data 8 sequence

• Instructors who want students to graduate with pandas, scikit-learn, and SQL experience

• Institutions adopting open materials developed through the UC Berkeley Data Science Modules program