About the Curriculum

This introductory data science course curriculum for undergraduates is split into modules. Computational thinking course instructors that are looking for data science applications are especially encouraged to adapt this work in all or in part.

Course Modules¶

Each module can be adapted to a 2-4 week undergraduate course with a total of 4 class hours each week. Materials are a mixture of Google Slides, Jupyter Notebooks, and digital or printed PDF handouts of activities and readings. Classroom modalities:

• Lecture: Google Slides with some paired Jupyter Notebooks. Twice a week.

• Lab: Guided active classroom activities autograded^[1] Jupyter Notebooks. Weekly.

• Readings: Take-home readings with paired multiple-choice take-home quiz. One social science reading per module. Other weekly readings are computing- and data-based.

• Discussion: Unplugged discussion worksheets (PDFs). Weekly.

• Homework and Projects: Take-home longer assignments as otter-supported autograded^[1] Jupyter Notebooks. Approximately bi-weekly.

Browse the curriculum¶

• (easiest) Access the website’s sidebar menu:

  • Descriptive module overviews

  • View-only Jupyter notebooks as HTML pages

  • Overiew pages will note which module materials have been classroom-tested (most are!)

• Access our GitHub for all Jupyter notebooks.

• Access our Google Drive for all slides and PDFs.

Why are there different versions?¶

Our goal is to make this curriculum as adaptable as possible. Materials were originally developed using the datascience Python package as our tabular programming paradigm. We recommend you view these first.

We are in the process of translating a set of extended materials to other APIs:

• pandas

• polars for Python

• tidyverse for R

Please contact us if you are interested in adopting these extended materials.

Course Syllabi¶

• Laney College: CIS 116 Syllabus

• UC Berkeley: Data 6 Syllabus

Usage and License¶

See our home page.