Data Science Pedagogy and Practice Workshop

A short workshop for instructors at UC Berkeley
June 5-7, 2017


Learn pedagogical methods and technical tools for data science, and get help integrating them into your own teaching. This workshop will cover how students in the Foundations of Data Science class (Data 8) have been introduced to computational and statistical concepts through hands-on analysis of real-world data, and it will support instructors from all disciplines in exploring how to teach courses that can connect with and enrich this approach.

The Data Science Education Program offers an interdisciplinary curriculum that provides a foundation for Berkeley undergraduates of all majors to engage capably and critically with data.  The Foundations of Data Science class already serves over 1,000 students per year across about 60 majors and fulfills statistics requirements in 90% of the majors that have one.  


This summer workshop is for instructors who are interested in adding data analysis and computing to their courses, instructors who are considering converting current materials into formats students who have taken Data 8 will be familiar with, instructors who would like to try out possibilities for an entry-level “connector” course in their own discipline, and instructors who are curious about data science as an approach to research and teaching. The course is open to all faculty, lecturers, and postdocs. 

What is the prerequisite knowledge? Do I have to know statistics or computer programming?  

No!  David Wagner, a professor of computer science researching cryptography and computer security, will be conducting the workshop with illustrations from Data 8, which has no pre-requisites.  Additionally, lab assistants will be available for support.

Core Workshop Topics (3 Days, June 5-7):

  • Underlying Pedagogical Principles of Data Science

  • Computational Thinking: Algorithmic transformations, representation and visualization

  • Inferential Thinking: Making conclusions based on sampling and resampling

  • A Data-Centered Computing Environment: tables in the cloud, computational documents

  • Student Panel

  • Group lunch on Wednesday

Optional Curriculum Integration Extension Activities (1.5 Days, June 8 & 9)

  • Learn to create assignments within the Jupyter notebook system

  • Sample lectures & labs from applied courses (Humanities, Social Sciences, Natural Sciences, Engineering)

  • Connector instructor panel (instructors across campus who have taught using the methods)

  • Learn what support is available to instructors from the Data Science Initiative including potentially student support for curriculum development over the summer

  • Plan a class or lab & receive feedback in discussion with other participants & connector instructors


June 5-7 from 9am to 4pm, with the optional curriculum integration extension on June 8-9.  Complete activities (2-3 hours) on your own the week before for an introduction to the tools.  Technical support will be available in person.


On campus computing lab, location TBD

How do I sign up?  

Fill out the interest survey. Contact Sarah Reynolds ( with additional questions.

Feedback From Last Year’s Faculty Summer Workshop

“I loved the integration of the statistical concepts and the computing; seeing really is believing, and I think this will motivate students to study statistics in a more rigorous way as well (especially those who are more reluctant to engage the math)”

“Really, it was all pretty useful. I especially appreciated seeing the actual course instructors do some teaching, which gives me a sense of what the students are covering. It was also very valuable to have the opportunity to try some labs and get used to the Jupyter notebook.”

“Not only did it stretch my brain and make me think in ways I haven't since high school, i.e. I'd completely forgotten about the order of operations, it also prompted some ideas for my own research, as well as projects that students might pursue, especially if they've had the Data8 course.”

“I found it very inspiring both in its contents (thinking about coding and stats) and in its very positive atmosphere.”

“It was very interesting to have [the facilitator/instructor] go back and forth between being teachers to a group of virtual undergraduates and being colleagues to the teachers in the room.”

“I really enjoyed the lectures and discussion. I'm glad students are getting some of the theory behind how data is studied and presented--and how much/some of it is subjective.”