Introduction
Data 8 is a "Foundations in Data Science" course taught for first year students at UC Berkeley. It combines principles/skills in statistics, programming, inference, modeling, hypothesis testing, visualization, and exploration. In short, it provides a foundation in the many fields encompassed by "data science", and gives students a practical introduction to the field.
This online resource serves as a "snapshot" of the Data 8 stack, and a guide providing a path forward for others that wish to imitate the Data 8 model. It represents all of the components that go into making Data 8 what it is. Including both pedagogical approach, concepts / topics covered, and technical pieces used in the class.
See the chapters to the left to navigate this book. Below is a general structure of the material.
The Data 8 project has several components, all of which are freely-available and open-source.
The first section of this guide is a step-by-step guide that teaches you how to deploy your own version of Data 8. It explains how to create a JupyterHub running in the cloud that students can access, how to connect this JupyterHub with the coding environment that Data 8 students use, and how to incorporate course materials into your class. Check it out here:
Deploying Data 8 describes how to set up your own Data 8 JupyterHub from scratch. It also contains suggestions and guidelines for running a Data 8 course at your own institution.
These sections cove the high-level reasoning behind the Data 8 course, including an overview of the technical and pedagogical pieces that make up the course. It should be treated as a reference for running your own Data 8 course, or for taking a similar technical approach to teaching data science.
Teaching and Pedagogy covers the unique blend of computational, coding, and conceptual information taught in Data 8.
The Data 8 Tech Stack describes the technical pieces of Data 8, including hosting student environments, grading, and managing the course.
All Data 8 materials are freely available online. The course material in full can be accessed at the following online resources:
To explore the guide, select a section to the left!