Data Science Symposium

Opportunities for Massachusetts Community Colleges


June 13, 2022

Background reading

We encourage participants read the following prior to the Symposium:

  1. Fostering and Simplifying Data Science Transfer Pathways in Massachusetts
    Baumer and Horton (2022)
    Read high level whitepaper summary
  2. Report from The Two-Year College Data Science Summit
    Gould, et al. (2018)
    Read Executive Summary only!


Registration (8:30 am)

Please arrive at Neilson Library, Room 102 in time to enjoy coffee, fruit, pastries and conversation together before we begin.

Welcome and Introductions (9:00 am)

As noted in the pre-reading, the growth of academic data science has been remarkable. Community colleges play a critical role in future data science pathways since they provide unique and affordable access and career pathways for students. Data science can help drive local, high impact data science discovery and bolster the local workforce.

Today’s symposium is part of a broader effort by the NSF-funded Data Science Corps Wrangle-Analyze-Visualize (DSC-WAV) project to foster the development of flexible and inclusive data science pathways in Massachusetts.

The symposium is the day when key leadership and faculty are present. Our goal for the day is two-fold: advocate for data science amongst these leaders and identify barriers and opportunities to support students for success in this area. Where is the passion to make data science skills and capacities available to your students? What questions will these students be able to answer about their local community and their lives? How can we help them build these transferable skills while also making a local impact?

Together we hope to develop a roadmap to help make data science opportunities available to all community college students in Massachusetts.

Opening Address (9:15 am)

  • Data Science at MassMutual: Building Capacity Through Industry-Education Partnerships
    Marc Maier
    Head of Enterprise Technology & Experience

Marc will share MassMutual’s experience building a data science capability through their Data Science Development Program, highlighting how partnerships with academic institutions created a pipeline of diverse and exceptional talent. We will discuss how we formed those partnerships, defined success, and evolved the program as our needs changed and the field grew and matured. We will end the talk by briefly discussing the role that community colleges could consider playing when it comes to educating students in the field and what industry can do to provide support and partnership.


Breakout group #1 (9:45 am)

In small breakout groups, participants will assess the opportunities and challenges to delivering high-quality data science education at their Community College, with a focus on supporting student success. We encourage participants to introduce themselves first then use the provided post-it notes to collect and collate ideas and reflections that can be added to the whiteboards in the room.

Suggested prompts:

  • What aspects of the opening address resonated with you?
  • How can data science help your students build transferable skills?
  • What challenges do you face at your institution?
  • What supports, structures, and resources would be helpful?

Break (10:15 am)

Panel Discussion (10:30 am)

What’s happening in Massachusetts: Innovative approaches and programs to data science in Community Colleges

Tentative list of speakers:

Breakout group #2 (11:15 am)

In small breakout groups, participants will discuss aspects of the innovations in Massachusetts Community Colleges panel that resonated with them as well as identify next steps at their institution.

Suggested prompts:

  • what aspects of the panels resonated among your group?
  • what do next steps look like at your institution?

Lunch (12:00 pm)

Lunch will be served at the Smith College Conference Center on College Lane, a short walk west towards Paradise Pond from Neilson Library. We encourage participants to reflect on the content from the morning, with an eye towards meeting the need for data science instruction and future transfer pathways in Massachusetts.

Keynote Address (1:15 pm)

Data science is quickly becoming the new currency for sound decision making and policy development. Our data revolution is not just about big data, but the emergence of all sizes and types of data. Advances in information technology, computation, and statistics now make it possible to access, repurpose, integrate, and analyze massive amounts of data. These advances are allowing our students to move from simple data analytics to “doing data science.” Join me in my data science journey that began in community college and progressed to leading a major research organization focused on serving the public good in all aspects of life.


Breakout group #3 (2:00 pm)

In small groups, one for each institution, participants will work to synthesize key findings from the symposium and identify barriers, and enumerate specific next steps. These findings will be collated and shared as part of the closing session. Our goal is to build momentum towards a frictionless experience for community college students pursuing data science in Massachusetts. (The faculty development workshop that continues Tuesday through Friday will build on these materials.)

Close (3:00 pm)


Covid-19 Policies for Smith College Campus

  • Visitors to campus need to be vaccinated in order to attend the Data Science Symposium and Workshop.
  • If you are not feeling well or are experiencing symptoms of illness, please do not come to campus.
  • College policies mandate masking in indoor spaces, except when eating. (Masks are not required outdoors on campus.)

Speaker biographies

  • Valerie Barr (Mount Holyoke College): While actively pursuing the application of software testing to artificial intelligence systems, Valerie Barr promotes the interdisciplinary application of computing through a combination of changes to computer science curricula and courses, as well as research and course collaborations with faculty from the full range of disciplines within the liberal arts. She is very active in the computer science education community and has led significant diversity efforts for the Association for Computing Machinery.

  • Ben Baumer (Smith College) PI on the DSC-WAV project, Ben has contributed to multiple curricular development efforts at the national level, including De Veaux et al. (2017), and B. Baumer (2015). He is a co-author of B. S. Baumer, Kaplan, and Horton (2017), one of the first comprehensive textbooks on data science. Ben received the Waller Education Award from the Section on Statistics and Data Science Education of the American Statistical Association in 2019.

  • Mya T. Bowen (Roxbury Community College) Ed. D, MBA, MSPA is an Assistant Professor in the Information Systems Technology (IST) Department and the IST Program Coordinator at Roxbury Community College, Roxbury Crossing, Boston, MA. Over the past year, she has worked with colleagues to develop a Data Analytics Certificate that has been approved by RCC’s Curriculum Committee and administration with an expected start of Fall 2023.

  • Michael Harris (Bunker Hill Community College) is an Associate Professor of Computer Information Technology at Bunker Hill Community College.

  • Nicholas J. Horton (Amherst College) is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He serves as co-PI on the DSC-WAV project and has worked to provide curriculum guidelines for data science at two-year colleges. Nick serves as the co-chair of the National Academies Committee on Applied and Theoretical Statistics and has been involved in several other data science education initiatives at the National Academies. Nick is a Fellow of the ASA and AAAS.

  • Sallie Keller (University of Virginia) is an endowed Distinguished Professor in Biocomplexity, Director of the Social and Decision Analytics Division within the Biocomplexity Institute at University of Virginia and Professor of Public Health Sciences. Dr. Keller is a leading voice in creating the science of all data and advancing this research across disciplines to benefit society. Her prior positions include Professor of Statistics and Director of the Social and Decision Analytics Laboratory within the Biocomplexity Institute of Virginia Tech; Academic Vice-President and Provost at University of Waterloo; Director of the Institute for Defense Analyses Science and Technology Policy Institute; the William and Stephanie Sick Dean of Engineering at Rice University; Head of the Statistical Sciences group at Los Alamos National Laboratory; Professor of Statistics at Kansas State University; and Statistics Program Director at the National Science Foundation. Dr. Keller is an Elected Member of the U.S. National Academy of Engineering. She is a fellow of the American Association for the Advancement of Science, elected member of the International Statistics Institute, and fellow and past president of the American Statistical Association. She holds a Ph.D. in Statistics from the Iowa State University of Science and Technology.

  • Marc Maier leads a growing Data Science team at MassMutual that focuses on transforming underwriting and actuarial functions and conducts research on wellness and fairness. Marc originally joined the company in 2014 to launch the Data Science Development Program and lead some of company’s first Data Science efforts. Prior to MassMutual, Marc earned a PhD in Computer Science from UMass Amherst.

  • Ethan Meyers (Hampshire College/Yale University) is a co-PI on the DSC-WAV project. Ethan has extensive experience in data science practice and education through his work at Hampshire College, Yale University, and through his research affiliation with the Center for Brains, Minds and Machines at MIT.

  • Michelle Trim (University of Massachusetts, Amherst) is Informatics Program Associate Director and Senior Teaching Faculty at the Manning College of Information and Computer Sciences at the University of Massachusetts at Amherst. Michelle is the PI of the NSF-funded “Boosting Access to Data Science Scholars” program.