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Data Science Curriculum Adoption

Courses, modules, and computing infrastructure for your institution - developed by UC Berkeley and partner institutions. Jupyter notebooks, Canvas templates, autograding, and a standardized setup guide, all in one place.

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Who this is for

Instructors, course coordinators, and institutions, especially community colleges, who want proven, NSF-funded data science courses and modules they can stand up in a couple of hours and adapt to their students. If you only need computing infrastructure (a JupyterHub), see Infrastructure & Tools - Cal-ICOR and Cloudbank can provision a hub independently of curriculum adoption.

UC Berkeley Courses

  • Data 6: Computational Thinking with Data Science and Society

    Interdisciplinary introduction with course notes and grading infrastructure. Full adoption package.

    Adopt Data 6

  • Data 8: Foundations of Data Science

    Inference, computing, and statistics for first-year students. Full adoption package with two versions.

    Adopt Data 8

  • Data 100: Principles and Techniques of Data Science

    Adoption materials are in progress, resources are available now.

    Data 100 resources

Modules

Standalone Jupyter notebooks you can drop into an existing course - no full course adoption required.

  • Cal-ICOR Modules

    Computational notebooks for courses across California higher education. Contribute or adopt notebooks.

    Cal-ICOR

  • Data Science Modules

    Multidisciplinary Jupyter notebooks from UC Berkeley DSUS - ethnic studies, AI, calculus, chemistry, and more.

    Data Science Modules

Partner Courses

Courses developed outside UC Berkeley that share the same open-source infrastructure and adoption workflow.

  • El Camino College CSCI 9: Practical Data Science

    A collaboration with UC Berkeley Data Science Modules. Adoption materials are in progress, resources are available now.

    CSCI 9 resources