> For the complete documentation index, see [llms.txt](https://berkeleydsep.gitbook.io/zero-to-data-8/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://berkeleydsep.gitbook.io/zero-to-data-8/intro.md). # 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 (seefor more information). The course material can be accessed at the following online resources: * [The Data 8 Course Site](http://data8.org) * [The Data 8 Course Textbook](https://www.inferentialthinking.com/chapters/intro) To explore the guide, select a section to the left! (types-of-content)= ## 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](https://data.berkeley.edu/external/data8-adoption). Here is a quick breakdown: * [**The Data 8 textbook**](/zero-to-data-8/textbook.md) is the textbook used by Data 8, with material that complements each topic in the class. It is licensed [CC-BY-ND-NC](https://creativecommons.org/licenses/by-nc-nd/2.0/), you are welcome to use the textbook at [inferentialthinking.com](https://inferentialthinking.com), but you may not modify or distribute it yourself without permission from the textbook authors. * [**Data 8 course materials**](https://github.com/data-8/materials-fds) includes notebooks, exercises, and other course materials. It is licensed [CC-BY-NC](https://creativecommons.org/licenses/by-nc/2.0/), and you are free to modify and distribute as you wish. * **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](https://forms.gle/iKLcryjZXit5913k7). * **Course Calendar and Demonstation Platforms** We have an [example course calendar](https://www.data8.org/materials-fds/demo.html) laying out the course week-to-week with appropriate notebooks linked to various rendering platforms. ## Technology Guide 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"](https://ucbds-infra.github.io/ds-course-infra-guide/intro.html). ## Contacting Us If you woud like to learn more about any of the tools used in Data 8 or are interested in deploying your own data 8 course, please fill out our Data 8 [Instructor Interest form](https://forms.gle/y5aQBi816xDXKe7r7) or shoot us an email at .