The Data 8 Pedagogy Guide¶
Data 8 is a “The Foundations of Data Science” course taught to first-year students at UC Berkeley. It combines principles and skills from statistics and computer science, such as inference, modeling, hypothesis testing, visualization, and others. It provides a foundation in the many disciplines encompassed by “data science”, and gives students a practical introduction to the field.
Teaching Data Science requires a shift in the way we traditionally teach each of the individual concepts. What were once introductory classes in statistics, computer science, and ethics (among others) are now combined into a single introductory course.
This book covers many of the pedagogical decisions that were made in Data 8 and should be seen as a reference and background for it.
All of the tools that Data 8 uses are available for the community to use (either as broader community-run projects, or as Berkeley projects). Many of them are open source (see Types of content in Data 8 for more information). The course material can be accessed at the following online resources:
To explore the guide, select a section to the left!
Types of content in Data 8¶
There are many kinds of content associated with Data 8, released under two different licenses. You can find more complete information at the Data 8 adoption website. Here is a quick breakdown:
The Data 8 textbook is the textbook used by Data 8, with material that complements each topic in the class. It is licensed CC-BY-ND-NC, you are welcome to use the textbook at inferentialthinking.com, but you may not modify or distribute it yourself without permission from the textbook authors.
Private course materials, including exams and answers. These are kept private in order to protect the integrity of the Data 8 courses at Berkeley and being run elsewhere. If you’d like access to these materials, please fill out this Google form.
If you are looking for a more technical overview of the infrastructure required to create a data science course at your institution, there is a new guide titled “The Data Science Educator’s Guide to Technical Infrastructure”.