AALAC Workshop
Data Science in the Liberal Arts
Spring 2021

Description: The goal of this workshop is to help define the role of Computer Science programs/departments in the emerging Data Science programs at AALAC institutions. The practice of Data Science, while currently grounded in Computer Science, Mathematics, and Statistics, spans and impacts intellectual enquiry in all disciplines across the campus.

Small liberal arts colleges like the AALAC member institutions are facing numerous challenges due to increased student interest and resource limitations. Taking on the additional commitments for Data Science programs will further stretch what are already overburdened programs. This workshop will present an opportunity for faculty in Computer Science and other disciplines to discuss and deliberate about these challenges and how best to address them and to effectively engage with the Data Science initiatives on their campuses.

This multi-day workshop will bring together faculty from Computer Science and other disciplines at AALAC colleges to examine, explore, and participate in the shaping of a vision for the education and scholarship of Data Science in liberal arts institutions. We invite faculty from colleges that have already implemented Data Science programs, as well as those who are either in the process of creating one, and those who are creating programs that are focused more on the Data Analytics domain.

Topics & Outcomes: We will focus on curricular structures and how those have balanced Computer Science, Mathematics & Statistics, and exposure to other areas of study. Also of interest are the different administrative structures used to launch Data Science programs:  as a free-standing department; as an interdisciplinary program; with dedicated faculty lines or built on existing institutional resources. An important topic of discussion will be the potential for changing or aligning existing courses to introduce some Data Science concepts. Based on the discussions and deliberations, we expect that an active working group or a community will be formed whose charge will be to create one or more white papers on the shape of Data Science curricula in the liberal arts, with a particular eye toward the role of and impact on computer science programs.

Format/Dates/Times

This will be a virtual workshop spread over several two-hour sessions on Fridays from 2:00p to 4:00p (US Eastern Time) over the Spring of 2021 on the following dates:

Session#1: February 5, 2021
Session#2: March 5, 2021
Session#3: April 2, 2021
Session#4: April 30, 2021
Session#5: May 21, 2021

A more detailed agenda for each session will be provided upon registration.

Registration (is FREE) is NOW CLOSED. We have reached capacity. Please contact Deepak Kumar for further information.

If interested, please fill out the Registration Form at the link: NO LONGER ACTIVE.

Please note: Registration is an indication of interest. Space is limited. You will receive a formal confirmation of participation after registration.

Organizing Committee
Valerie Barr, Mount Holyoke College
Andrea Danyluk, Williams College
Elizabeth Evans, LACOL & Haverford College
Sorelle Friedler, Haverford College
David Kauchak, Pomona College
Deepak Kumar, Bryn Mawr College
Panagiotis Metaxas, Wellesley College
Eni Mustafaraj, Wellesley College
Richard Wicentowski, Swarthmore College

Workshop Primary Contact
Deepak Kumar
Professor of Computer Science
Bryn Mawr College
Bryn Mawr, PA 19010
dkumar@cs.brynmawr.edu

Participants

Nicholas Horton, Mathematics & Statistics
Amherst College

Shu-Min Liao, Statistics
Amherst College

Matteo Riondato, Computer Science
Amherst College

Deepak Kumar, Computer Science
Bryn Mawr College

Amy N. Myers, Mathematics
Bryn Mawr College

Mary Osirim, Provost & Sociology
Bryn Mawr College

Marc Schulz, Data Science & Psychology
Bryn Mawr College

Adam Loy, Statistics
Carleton College

Laurie Heyer, Mathematics & Computer Science
Davidson College

Michael C. Brady, Data Analytics
Denison University

Elizabeth Evans, LACOL
Haverford College

Sorelle Friedler, Computer Science
Haverford College

Sara Mathieson, Computer Science
Haverford College

Leslie Hebb, Astronomy
Hobart & William Smith College

Leslie Myint, Statistics
Macalester College

David Shuman, Math, Statistics & Computer Science
Macalester College

Caitlin Myers, Economics
Middlebury College

Valerie Barr, Computer Science
Mount Holyoke College

Johanna Hardin, Mathematics & Statistics
Pomona College

David Kauchak, Computer Science
Pomona College

Brent Ladd, Center for Science of Information
Purdue University

Michael Spezio, Psychology, neuroscience, Data Science
Scripps College

Winston Ou, Mathematics
Scripps College

Katherine Kinnaird, Computer Science, Statistics, Data Science
Smith College

Richard Wicentowski, Computer Science
Swarthmore College

Robin HIll, Computer Science
University of Wyoming

Lee Kennedy-Shaffer, Mathematics & Statistics
Vassar College

Lillian (Boots) Cassel, Computer Science
Villanova University

Panagiotis Metaxas, Computer Science
Wellesley College

Eni Mustafaraj, Computer Science
Wellesley College

Andrea Danyluk, Computer Science
Williams College

Richard De Veaux, Statistics
Williams College

Sessions/Agenda
All sessions run on Fridays from
2:00 to 4:00p US Eastern Time
Zoom Link: Expired

Session #1 February 5, 2021

• Introductions
• Open Discussion
• Forming Subgroups
  Curriculum and Programs
  Data Science Courses
  Ethics Fairness & Bias in Data Science
  Staffing Strategies & Issues
  

Session #2 March 5. 2021
Topic: Data Science Curricula
Valerie Barr, Session lead
Zoom Link: Expired

• Panel: Shape of DS Curricula at SLACs
  (as informed by published national Curricula)
  Valerie Barr (Moderator), Boots Cassel, Andrea Danyluk, Reichard de Veaux, Nicholas Horton
• Group Breakouts (30 min)
  
  Zoom Link: Click here (password required)

March 26 through April 2, 2021
Special Film Screening/Streaming: Coded Bias
Synopsis: This film explores the fallout of MIT Media Lab
researcher Joy Buolamwini's discovery that facial recognition does not see dark-skinned faces accurately, and her journey to push for the first-ever legislation in the U.S. to govern against bias in the algorithms that impact us all.
Get tickets here: Link.
Trailer: Watch.
More info about the film: Link.

Session #3 April 2, 2021
Topic: Ethics, Fairness, and Bias in DS
Sorelle Friedler, Session Lead
Zoom Link: Expired

• Data Sources (Nick Horton)
• Integrating DS Ethics into DS Curriculum
  (Katie Kinnaird)
• Integrating DS Ethics into Data Structures & Algorithms (Sorelle Friedler)
• Integrating DS Ethics into a Statistics Course
  (Lee Kennedy-Shafer)
• Teaching Data Feminism in CS
  (Eni Mustafaraj)
• Teaching DS Ethics as a separate course
  (Mike Brady and Sam Cowling)
• 30 min Breakout Sessions on DS Ethics
• 30 mins Group Breakouts
  

Session #4 April 30
Topic: Data Science Courses in SLAC programs
Eni Mustafaraj, Session Lead
Zoom Link: Click here.

A palette of introductory courses: Why so many?

• What an intro course in DS should/could look like
  (Richard DeVeaux)
• Middlebury's Data Science Across Disciplines Course
  (Caitlin Myers)
• Scripps' 2-course sequence for DS Minor
  (Winston Ou)

Going Beyond Introductory Courses

• Haverford's CMSC260: Foundations of Data Science
  (Sara Mathieson)
• Data Science Ethics and Justice
  (Michael Spezio)

Provocation

• A distributed model for teaching Data Science skills across the curriculum
  (Leslie Hebb)

Special Talk at Bryn Mawr on May 4, 2021 at 6:00p
Race to the Future?
Reimagining the Default Settings of Technology & Society
Ruha Benjamin, Princeton University
Register: Link.

Session #5 May 21
Zoom Link:
Agenda

• Discussion
• Planning for report(s)
• Breakouts
• Future steps
  

AALAC: The Alliance to Advance Liberal Arts Colleges has the following members:

This workshop is supported by funds from AALAC, the Center for Science of Information (an NSF Science & Technology Center grant NSF CCF-0939370), and Foundations Of Data Science Institute (FODSI). Thank you!