This course will prepare students to be effective teaching assistants, course developers, and/or peer consultants in the field of data science. Students will gain aptitudes and tangible skills that are rarely taught in classes, broadly applicable to all career paths, and appropriate to placing on a resume.
This is a hands-on course, focused on building skills through practice with support from instructors and minimal lecture format:
FOUNDATIONAL LESSONS BY BERKELEY EXPERTS
The first five meetings employ talks by experts, use anecdotal evidence, and present different scenarios on teaching and consulting situations. Later in the semester, there will be opportunities for refresher courses based on interest.
HANDS-ON TEACHING AND CONSULTING EXPERIENCE
All students will work in one of the following roles:
• Connector Assistant: help instructors of Data Science Connector courses to deliver and teach material in labs and office hours
• Curriculum Developer: work closely with faculty (both Data Science and non-Data Science) to create and deploy data science curriculum materials in IPython notebooks
• Data-Enabled Course Assistant: help instructors of upper-division Data-Enabled courses to deliver and teach material in labs and office hours
• Data Peer Consultant: offer peer-to-peer consulting, debugging, and tutoring for students working in data science across the Berkeley campus
Each week will include role-specific responsibilities as well as team check-ins for students to reflect collectively on progress, receive feedback and support, and work through challenges.
The course is Pass / No Pass and will be assessed on attendance, written reflections, role-specific work, and final presentations. Questions about the expectations and structure of the written reflections and final presentation will be addressed during the first meeting. Students will not be permitted to pass the course if they have more than one unexcused absence or more than three excused absences.
This student led course is supported by collaboration between the University Library, D-Lab, and the Division of Data Sciences.
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