Home » Additional Features

Curriculum Recommendations for Growing Number of Two-Year College Data Science Programs

1 April 2019 No Comment

Brad Thompson teaches statistics at Delaware Technical Community College and is an instructional designer with the Center for Creative Instruction and Technology (CCIT). Thompson assists CCIT’s work to implement new teaching methods, curricula, and technologies aimed at improving student retention and learning.


In response to the growing interest in data science education at two-year colleges, the American Statistical Association (ASA) hosted the Two-Year College Data Science Summit (TYCDSS) in May 2018. The project was led by Rob Gould of the University of California, Los Angeles and Roxy Peck of the California Polytechnic State University in San Luis Obispo.

With support from the National Science Foundation (NSF), the summit assembled 72 educators, researchers, and practitioners in statistics, mathematics, computer science, and data science. Summit participants included faculty from two- and four-year colleges, along with representatives from industry, government, and nonprofits. In addition to Gould and Peck, the steering committee included Nicholas Horton, Randy Kochevar, Brian Kotz, Mary Rudis, and Brad Thompson.

A primary goal of the summit was to produce a report with curricular guidelines and recommendations to assist two-year colleges in establishing and maintaining data science programs. This report is now complete and available.

The summit and report considered three types of data science programs: (1) associate degree programs for students who intend to transfer to a four-year institution; (2) associate degree programs for students aiming to go directly into the workforce; and (3) credit-bearing certificate programs. While these three types of programs may share many similarities, they are each unique in their program outcomes, curriculum, and challenges.

One of the primary features of the report is a set of recommended program outcomes viewed through the lens of each program type. These are broken into the following categories, each with its own set of objectives:

  • Computational Foundations
  • Computational Thinking
  • Statistical Foundations
  • Statistical Thinking
  • Statistical Modeling
  • Data Management and Curation
  • Mathematical Foundations
  • Productivity Foundations

These general program outcomes, along with their associated learning objectives (which can be found in the report), were developed with input from two days of discussion among summit participants. In addition, recent research and curricular frameworks from the Park City Math Institute Data Science Initiative and the National Academy of Sciences Committee on Envisioning the Data Science Discipline were consulted to help frame discussion and writing.

Data science education at two-year colleges remains a new and relatively unproven endeavor. The TYCDSS report accounts for this by addressing many of the unique challenges each type of data science program may encounter. These include faculty preparedness and professional development, establishing partnerships with local industry, and a general lack of consensus on what exactly data science is and what specific qualifications are necessary to work in the field.

Because a primary goal of most community and two-year colleges is to prepare students to be competitive in the workforce, one of the most critical challenges to starting a new certificate or associate degree program in data science is to ensure the employability and marketability of graduates. While many job postings in data science and analytics may still list a bachelor’s or graduate degree as a minimum requirement, an encouraging outcome of the summit was the consensus among industry professionals that there is definitely a need for “entry-level” data scientists and two-year colleges have a role and opportunity to help meet this demand.

The report provides recommendations to aid two-year colleges in preparing capable practitioners in data science who can demonstrate mastery and working knowledge of concepts including data cleaning, modeling, and visualization; statistical thinking and programming; and database management.

Similarly, two-year colleges developing programs for students intending to transfer to a four-year institution need to develop a program that also satisfies articulation agreements. This should be accomplished while providing sufficient general education, mathematics, statistics, and computer science coursework to prepare students to seamlessly transfer into their junior year of a data science bachelor’s degree program.

To meet the intended program outcomes and learning objectives while also overcoming a variety of challenges, the TYCDSS writing team makes seven recommendations for two-year colleges developing new programs in data science. These are based on discussions held at the summit and subsequent deliberations. The report further elaborates on each recommendation by providing additional information, examples, and research to support them.

It is recommended by the TYCDSS committee that two-year colleges developing new programs in data science should do the following:

  1. Create courses that provide students with a modern and compelling introduction to statistics that, in addition to traditional topics in inferential statistics, includes exploratory data analysis, the use of simulations, randomization-based inference, and an introduction to confounding and causal inference.
  2. Ensure students have ample opportunities to engage with realistic problems using real data so they see statistics as an important investigative process useful for problem solving and decision-making.
  3. Explore ways of reducing mathematics as a barrier to studying data science while addressing the needs of the target student populations and ensuring appropriate mathematical foundations. Consider a “math for data science” sequence that emphasizes applications and modeling.
  4. Design courses so students solve problems requiring both algorithmic and statistical thinking. This includes frequent exposure to realistic problems that require engaging in the entire statistical investigative process and are based on real data.
  5. Expose students to technology tools for reproducibility, collaboration, database query, data acquisition, data curation, and data storage while also requiring students to develop fluency in at least one programming language used in data science and encouraging learning a second language.
  6. Infuse ethical issues and approaches throughout the curriculum in any program of data science.
  7. Foster active learning and use real data in realistic contexts and for realistic purposes. Programs should consider portfolios as summative and formative assessment tools that both improve and evaluate student learning.

Along with compiling the work achieved at the summit and throughout the writing process, the TYCDSS report also provides a list of current data science programs at various two-year institutions and a brief examination of their curricula and program requirements. Previous efforts and existing research are also discussed as additional and beneficial resources for two-year colleges preparing to develop programs in data science. Links to these resources, along with the final report and a March 2019 webinar, are available on the TYCDSS website.

TYCDSS Writing Teams

Certificate programs
Brian Kotz
Mary Rudis
Joyce Malyn-Smith

Associate degree for transfer programs
Nicholas Horton
Mark Daniel Ward
Julie Hanson

Associate degree for direct-to-work
Kathy Kubo
Rebecca Wong
Brad Thompson
Roxy Peck

Rob Gould served as an editor and supported the three groups.

1 Star2 Stars3 Stars4 Stars5 Stars (No Ratings Yet)

Comments are closed.