Workshop on Undergraduate Data Science Pedagogy and Practice

Time: July 16-19, 2018

Location: Social Science Matrix, 8th Floor, Barrows Hall, UC Berkeley

UC Berkeley has pioneered an innovative undergraduate “Foundations of Data Science” curriculum (http://data8.org) that takes an integrated approach to introductory computer science and statistics, allowing students to use data-driven methods to think critically about the world, draw conclusions from data, and effectively communicate results. Curriculum innovation accompanying the course is further developed in domain area “connector” courses that complement Data 8 concepts and “modules” that introduce data science into existing courses across campus.  Several universities have incorporated aspects of this novel curriculum into their data science programs, including CornellYaleUniversity of Washington, and others.  

Led by Professor David Wagner, recipient of the Berkeley Distinguished Teaching Award and Data 8 co-instructor, this workshop is intended for faculty in US bachelor’s degree-granting institutions, who are actively engaged in developing and offering a data science curriculum.

The workshop is made possible by support from the National Science Foundation, Microsoft Corporation, the West Big Data Innovation Hub, and UC Berkeley's Division of Data Sciences.

Participants will come away with an in-depth understanding of:

  • Foundations of Data Science curriculum built on computational thinking with python, inferential thinking by resampling, prediction and machine learning.

  • Conveying how to interpret and communicate data and results using a diverse array of real data sets including economic data, geographic data and social networks.

  • Guiding students in understanding how information will be incomplete and somewhat uncertain, yet inference methods can help quantify uncertainty and establish the accuracy of estimates.

  • How the material is delivered in lectures, labs, assignments and student projects.

  • The pedagogical theory underlying the integrated computing and statistics curriculum.

  • Technology foundations underlying the pedagogy platform and how to replicate it.

  • What it would take to translate the education approach and platform to your institution.

Schedule

All locations will be on the 8th floor of Barrows Hall unless otherwise stated.

Monday July 16

Time

Session

12:00pm

Welcome Lunch

1:00pm

Welcome & Overview of Data 8 and Its Origins

2:00pm

Teaching Programming in a Data Science Context

2:30pm

Break

2:45pm

How to Run a Lab in the Data Science Environment

3:00pm

Sample Lab: Intro to Notebooks, Python, and tables

3:30pm

Break

3:45pm

Group Discussion: Building Data Science Curricula at Your Institution (David Culler)

5:00pm

Welcome Reception at the Berkeley Institute for Data Sciences (David Mongeau)

Orianna DeMasiAndreas ZoglauerDiya Das, Johannes Schoneberg

Tuesday July 17

9:00am

Teaching Hypothesis Testing in a Computational Setting

9:45am

Labs: Hypothesis testing

10:30am

Break

10:45am

Teaching Confidence Intervals; Peer Instruction

11:15am

Teaching Prediction and Linear Regression

11:45am

Lunch

1:00pm

Great Data Sets and Case Studies

1:45pm

Infrastructure: Piazza, Attendance, Gradescope, Git, Notebooks, Creating Assignments

2:15pm

Break

2:30pm

Infrastructure: Autograding, okpy (Vinitra Swamy)

3:15pm

Break

3:30pm

Infrastructure: Deploying Jupyterhub (Chris Holdgraf)

4:00pm

Group discussion: Models, Reflections, and Challenges for Intro-level Pedagogy

Wednesday July 18

9:00am

Connectors, Modules, and Data-Enabled Courses (Eric Van Dusen)

10:00am

Data 100 (Deborah Nolan)

11:00am

Break

11:15pm

Data science major, minor, and program (Cathryn Carson)

12:00pm

Lunch

1:00pm

Lightning Talks 1: Visitor Presentations (Moderator: Anthony Suen)

2:00pm

Lab: Linear Regression

3:00pm

Break

3:15pm

Student Panel (Students from Data 8 and Other DS courses)

 4:15pm

Lightning Talks 2: Visitor Presentations and Discussion (Moderator: Anthony Suen)

Thursday July 19

9:00am

Lessons Learned from Data 8 (John DeNero)

10:00am

Break

10:15am

Group Breakout Reflection Session (Cathryn Carson)

11:15am

Open Discussion: What Does it take to Move Forward?

Questions

For questions please email: ds-help@berkeley.edu