## Institutional Resources, Foundations, Supports, Consolidation and Application of Undergraduate Data Science Education 

*This book was produced with the support of the National Science Foundation
(Grant #1915714).*

Unique to the University of California, Berkeley’s Data Science Education
development has been our DataHub Cyberinfrastructure and Autograding
Cyberinfrastructure. The campus community enables full access to data science
environments through any device capable of running a web browser connected to
the internet. This alleviated the burden and inequity of requiring students to
upgrade their personal computing devices to a minimum standard which would have
been a barrier to widespread student participation.  The autograder is an
essential component of any scalable computation-centered course that includes
graded assignments. Otter Grader is used for autograding. 

The Data Science Undergraduate Studies program at UC Berkeley is a matrix of
courses and learning opportunities for students to be directly involved in Data
Science both in and outside of classrooms.  UC Berkeley’s most notable Data
Science development has been the creation of the Data Science major and minor
programs that welcomes a large number of students from diverse academic and
personal backgrounds.  The Foundations of Data Science Course, Data 8 serves as
an initial access point for many students who are interested but might not have
any prior experience. Students may initiate their Data Science experiences
through this course or find their way to Data 8 from Data Science Modules, known
as “Modules” that have been embedded into the curriculum of other domain area
courses. The curricular experiences of students may then continue both through
upper level Data Science courses and through Domain Connector Courses, or better
known as just “Connector Courses” that integrate Data Science learning across
the semester-long course.  The Data Science Discovery Projects also offers
students the opportunity for applied experience with partnering organizations on
a semester-long project.

Conscious of the differences in student experiences and backgrounds, programs
continue to be developed that focus on uplifting and providing additional
support to students from populations that are underrepresented in Data Science. 
- The Data Scholars Program accepts a cohort of students each semester for
  participation in the program components: a foundations seminar, academic
  development, discovery research, career accelerator, and a speaker series. The
  Data Scholars Foundation Seminar is a course that provides additional
  structure and support as they are enrolled in the Foundations of Data Science
  Course.  The Data Scholars Pathways Seminar invites students to join in a
  series of talks from professionals in the field to learn more about career
  opportunities and development. The Data Scholars Discovery Research Projects
  partners with the existing Discovery Research Program to ensure Data Scholars
  are matched with appropriate research projects for a semester-long experience.
- Transfer Mentors is a newly established program (2020) that is continuing to
  develop and build capacity for entering transfer students interested in Data
  Science through direct support by current more established transfer students.


Students looking for additional hands-on learning experiences in courses and
with peers might become involved in a number of employment opportunities.

- Students can assist in the development of the course content for Modules as
  part of a Modules Jupyter Notebook Developers or the Jupyter Notebook
  Development Team. 

- They might assist peers through direct 1:1 conversations through the Data
  Peers Consulting program.  

- Students can become part of the teaching staff for the Introduction to Data
  Science course, Data 8, the Foundations Scholars or the Discovery Scholars;
  they can become an Undergraduate Student Instructor or a tutor.