Bootcamp-style OpenDS4All workshop where students could enhance their theoretical knowledge with hands-on, industry-focused experience.

OpenDS4All is a project created to accelerate the creation of data science curricula at academic institutions. While a great deal of online material is available for data science, including online courses, we recognize that the best way for many students to learn (and for many institutions to deliver) content is through a combination of lectures, recitation or flipped classroom activities, and hands-on assignments.

OpenDS4All attempts to fill this important niche. Our goal is to provide recommendations, slide sets, sample Jupyter notebooks, and other materials for creating, customizing, and delivering data science and data engineering education.

The project hosts educational modules that may be used as building blocks for a data science curriculum.

Note: The link opends4all-resources takes you to the opends4all curriculum building blocks organized by category.

Note: If you adopt all or some of the content, please add your program's details to the ADOPTERS.csv file.

The initial modules were designed to target a broad, cross-university audience at both the undergraduate and graduate levels. Modules contain instructor notes and comments intended to aid in the delivery of the material; the expectation is that instructors will be generally fluent in basic database and machine learning concepts.

The perspective of the materials largely comes from computer science, with an emphasis on data wrangling and engineering as well as machine learning and validation. However, prior versions of the content have been used to teach students ranging from freshmen to PhD students, across a wide range of fields. The emphasis is largely on core concepts and algorithms with grounding in today's technologies and best practices.

Students are expected to come in with two major prerequisites:

• Comfort and familiarity with programming in Python (writing small functions, importing and calling library functions, using Python data structures).
• Familiarity with probability theory and very basic statistical notions.

To some extent, students with a limited background can follow along with this material, but they will likely need to supplement extensively.

The following topology shows how content is currently organized around categories. This is a living/dynamic taxonomy that is updated as new content is added to the project. Each category contains modules and each module consists of one or more of the following components:

• instructor notes (Instructor_Notes.md) and guide to files
• a set of PowerPoint slides (with presenter notes) ending in .pptx
• companion Jupyter notebooks, for students to see the lecture materials "in context" and to be able to experiment
• sample quiz materials (where applicable)
• sample homework assignments (where applicable)
• additional documentation (where applicable)

Note: The PowerPoint slides are not directly viewable on GitHub. After you clicked on the link to a set of PowePoint slides you need to select the Download button to download and view the slide deck. Two viewable extracts from the slide decks can be seen by clicking on the links below:

• INTRODUCTION-Data-Science-basic.pptx
• DATA-WRANGLING-Import-Link-mixed.pptx

There are many ways to interact with this repository:

• browse the repository in search of content ( use the 'Find file' search functionality )
• download content (PowerPoint slides, Jupyter notebooks, etc.)
• contribute content ( become a contributor to the project )
• become involved in the day-to-day management of the project ( become a committer )
• provide overall direction and leadership to the project ( become a Technical Steering Committee member )

The project's governance principles clarifies the different roles and describes the processes for becoming a contributor, a committer or a TSC member.

Anyone can contribute to this repository - learn more at CONTRIBUTING.md. Follow the step-by-step instructions COMMUNITY-GUIDE.md to submit a module for possible inclusion into to repository.

OpenDS4All is a project hosted by LF AI & DATA. This project has established its own processes for managing day-to-day processes in the project at GOVERNANCE.md.

To report a problem, you can open an issue. If the issue is sensitive in nature or a security related issue, please do not report in the issue tracker but instead email [email protected].

If you want to contact us, please open an issue and one of the members of the TSC will respond to your request. If you do not feel comfortable opening an Issue, email [email protected].

If you are interested in collaborating on the project, please open an issue and one of the members of the TSC will respond to your request. If you do not feel comfortable opening an Issue, email [email protected].

License: CC BY 4.0, Copyright Contributors to the LF AI & DATA OpenDS4All project.