Data Science Pedagogy and Practice: A Short Summer Workshop
June 3-5, 2019
Learn data science concepts, pedagogical methods, and technical tools, and get help integrating them into your own practice. This workshop will cover some of the computational and statistical concepts that students learn in the Foundations of Data Science class (Data 8) through hands-on analysis of real-world data. It will support instructors from all disciplines in exploring how to teach courses that can connect with and enrich this approach. Open to UC Berkeley instructors, faculty, graduate students, postdocs, and other members of the campus community as space permits. No computational or statistical background required.
Graduate Student Introduction to Data Science Pedagogy
June 7, 2019
In this one-day workshop, graduate students will learn about the pedagogy of the Data Science Education Program. This workshop will cover some of the computational and statistical concepts that students learn in Data 8 and Data 100, as well as an overview of the Connector courses and Modules. It will also give experience using a cloud based computing environment. Graduate students can use these skills and resources to improve and imagine new types of teaching. Open to all UC Berkeley graduate students.
Human Contexts and Ethics (HCE) Workshop
2019 Dates Coming Soon
Berkeley’s undergraduate Data Science programs include a requirement for students to learn about the human, social, and ethical contexts in which data analytics and computational inference play a central role. With this two-day workshop, we aim to encourage and support instructors across the disciplines to develop new courses and transform existing ones to become Human Contexts and Ethics (HCE) options. The workshop will discuss the framing of the HCE requirement, review experience so far, and give support in developing new modes of pedagogy. We’ll cover approaches such as case studies, historical examples, data sources, and exercises.
Data Science Modules - Request Development Support
The Data Science Education Program provides assistance to instructors interested in adding data science modules to their existing classes through student developer teams that partner closely with you to develop materials. Data science modules are short explorations that give students the opportunity to work hands-on with a dataset relevant to your course and receive instruction on the principles of data analysis, statistics, and computing. Modules vary widely and are customized based on each instructor’s objectives and the type of course, ranging in length from one to two lectures to multiple-session workshops culminating in a data-centered project. A recent article and video explain how modules are currently being deployed.
More information: https://data.berkeley.edu/education/courses/modules
Module Support Request Form: https://goo.gl/zRxVm7
Proposals for Data Science Connector Courses
Students in Data 8 learn computational and statistical concepts from a variety of examples spanning a broad range of disciplines. Connector courses (2, 3, or 4 units, often numbered 88) are designed to build on students’ analytical knowledge from Data 8 in connection to their own interests in a specific field. Connectors can be housed in a department, cross-listed in multiple programs, or piloted as a L&S course. The Data Science Education Program seeks to broaden the suite of connector offerings available to students and increase the potential to integrate data science into existing curricula. DSEP is able to provide a range of technical, pedagogical, financial, and community-building support to you in piloting a course, as well as computer lab space and lab assistants.
More information: https://data.berkeley.edu/education/connectors
Connector interest form: https://goo.gl/s9EkN4
If you have questions about the Data Science Education Program in the Division of Data Sciences, feel free to contact us:
Cathryn Carson (email@example.com), DSEP Faculty Lead
Eric Van Dusen (firstname.lastname@example.org), DSEP Curriculum Coordinator
David Culler (email@example.com), Interim Dean, Division of Data Sciences