Data Science Transfer Pathways from Associates to Bachelors Programs A Preprint

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Data Science Transfer Pathways from Associate’s

to Bachelor’s Programs

A Preprint

Benjamin S. Baumer ∗

Statistical & Data Sciences

Smith College

Northampton, MA 01063

bbaumer@smith.edu

Nicholas J. Horton

Mathematics & Statistics

Amherst College

Amherst, MA 01002

nhorton@amherst.edu

January 9, 2023

Abstract

A substantial fraction of students who complete their college education at a public university

in the United States begin their journey at one of the 935 public two-year colleges. While the

number of four-year colleges oﬀering bachelor’s degrees in data science continues to increase,

data science instruction at many two-year colleges lags behind. A major impediment is the

relative paucity of introductory data science courses that serve multiple student audiences

and can easily transfer. In addition, the lack of pre-deﬁned transfer pathways (or articulation

agreements) for data science creates a growing disconnect that leaves students who want to

study data science at a disadvantage. We describe opportunities and barriers to data science

transfer pathways. Five points of curricular friction merit attention: 1) a ﬁrst course in data

science, 2) a second course in data science, 3) a course in scientiﬁc computing, data science

workﬂow, and/or reproducible computing, 4) lab sciences, and 5) navigating communication,

ethics, and application domain requirements in the context of general education and liberal

arts course mappings. We catalog existing transfer pathways, eﬀorts to align curricula across

institutions, obstacles to overcome with minimally-disruptive solutions, and approaches to

foster these pathways. Improvements in these areas are critically important to ensure that

a broad and diverse set of students are able to engage and succeed in undergraduate data

science programs.

Keywords

articulation

associate’s programs

bachelor’s programs

course design

curriculum

data

acumen ·data analytics ·two-year colleges ·community colleges

Accepted for publication in the Harvard Data Science Review

1 Introduction

Two-year colleges (historically known as community colleges or junior colleges) play a critical role in higher

education in the United States.

As of 2020, these 935 public institutions enroll more than 4.7 million students (Duﬃn 2021). Two-year colleges

provide associate’s degrees that lead directly to employment, as well as options to transfer to bachelor’s

programs. In our home state of Massachusetts, the enrollment at the 15 public two-year colleges is comparable

to that of the undergraduate enrollment of the University of Massachusetts system. While average tuition

varies considerably by state, two-year colleges are the most eﬀective and aﬀordable option for many students.

Blumenstyk (2021) describes two-year colleges as:

∗

More information about the Massachusetts Data Science Pathways project can be found at

https://dsc-wav.

github.io/ma-ds-pathways

arXiv:2210.12528v2 [stat.OT] 6 Jan 2023

A preprint - January 9, 2023

“the keystone for the nation’s plan to help more people earn a postsecondary credential.”

By all accounts, job prospects in data science are excellent, due to high salaries, expansive job growth, and

comfortable working conditions. According to Glassdoor, data scientist is the #3 job in America for 2022,

and has ranked among the top three every year since 2016. The US Bureau of Labor Statistics reports

a mean annual wage of $103,930 for data scientists, and estimates that jobs will grow 22% for Computer

and Information Research Scientists and 33% percent for Mathematicians and Statisticians over the next

ten years. A number of companies have reported that they can’t ﬁnd suﬃcient skilled candidates for these

positions (

http://oceansofdata.org/projects/mentoring-new-data-pathways-community-colleges

Due to the nature of the work, data scientists have adapted smoothly to working remotely, an increasingly

relevant factor that should only improve employment prospects. The high probability of ﬁnancial success

for graduates in data science stands in stark contrast to the increasingly dim prospects for many master’s

students in other ﬁelds. Korn and Fuller (2021) conclude that 38% of master’s programs at top-tier private

universities in the U.S. don’t deliver on the promise of earnings that exceed debt incurred to pay for tuition.

Providing equitable access to these desirable jobs is a challenge that is symptomatic of larger issues of class

and income inequality in the United States. Several national reports (e.g., Rawlings-Goss et al. (2018),

National Academies of Science, Engineering, and Medicine (2018), and National Academy of Engineering

and National Academies of Sciences, Engineering, and Medicine (2016)) recognize this challenge and call

for tighter partnerships between two- and four-year colleges. If the ﬁeld of data science is serious about

diversifying its workforce, then there must be paths to high-paying jobs in data science that begin at two-year

colleges, which enroll a much larger fraction of historically under-served students than four-year colleges.

The ongoing National Science Foundation’s (NSF) Data Science Corps (DSC) program focuses on creative

approaches to developing a competitive and diverse workforce in data science. Through our roles as leaders

of the NSF-funded DSC-WAV (Wrangle, Analyze, Visualize) program we have had the opportunity to

engage in data science projects with community organizations and to work with partners at several two-year

colleges to foster new courses and programs. This work included organizing a Symposium on Data Science at

Massachusetts Two-Year Colleges for academic leaders on June 13, 2022 and faculty development workshops

in 2021 and 2022.

1.1 Our contribution

The purpose of this paper is to help foster connections between two- and four-year institutions that will lead

to more transparent and ﬂexible pathways to bachelor’s degrees in data science. While we use Massachusetts

as our primary example, we believe that that the insights and approaches we suggest may be useful to other

states. We focus exclusively on data science, including cognate disciplines of mathematics, computer science,

and statistics only as they relate to data science.

We begin by brieﬂy surveying the landscape of data science in higher education nationally (Section 2). The

lack of existing transfer pathways make a bachelor’s degree in data science burdensome for a two-year college

student to achieve without signiﬁcant—and probably unreasonable—foresight and perseverance through

administrative and bureaucratic obstacles. We use two hypothetical community college students named

Alice and Bob to illustrate how these obstacles impede student progress. In Section 3, we use the bachelor’s

program in data science at UMass-Dartmouth (which we see as representative of a curricular consensus in

data science) as an example, analyze potential transfer pathways, and identify ﬁve points of friction. Our

analysis leads directly to recommendations that could provide explicit pathways in data science with relatively

few new courses and modest impact on existing programs (Section 4). We conclude with ﬁnal thoughts in

Section 5.

2 Background and related work

2.1 Data science programs in higher education

Since Cleveland (2001)’s action plan for data science, the ﬁeld has continued to blossom within academia.

Academic data science can be aspirationally described using a pyramid, with doctoral degrees rare but

important for leadership and research in the ﬁeld. Master’s degrees are the next level, with larger numbers

and considerable job opportunities. For established disciplines, bachelor’s programs (oﬀered at four-year

colleges) and associate’s programs (oﬀered at two-year colleges), make up the third and fourth levels of the

pyramid, with larger and larger numbers of students obtaining these degrees. Jobs are available at each level,

A preprint - January 9, 2023

with the potential for interested students to pursue more advanced degrees in order to deepen skills and

expand their work opportunities. However, workforce opportunities remain opaque to too many students.

As an emerging discipline, data science has not yet matured to that extent, with master’s programs leading

the way, bachelor’s programs on the rise, and associate’s program lagging behind.

Several doctoral programs in data science now exist in the United States (National Academies of Sciences,

Engineering, and Medicine 2020) and their graduates are now beginning academic and workforce careers.

Far more common are master’s programs in data science and data analytics, which are oﬀered by many

universities (both online and in-person). Nationally, the growth in the number of master’s degrees granted in

analytics and data science is dramatic, with more than 45,000 degrees reported in 2020 by the Institute of

Advanced Analytics.

While the study of data science at the graduate level continues to evolve, its footprint is already substantial.

The growth of these programs makes it possible for students at the undergraduate level to more easily identify

future programs of graduate study. What undergraduate majors should best prepare a student for graduate

study in data science? Computer science, statistics, and mathematics are the closest cognate disciplines, and

while statistics is not always available as an undergraduate major, it is taught everywhere and can be folded

into either a computer science or mathematics major, both of which are available at virtually any institution.

Historically rarer (but increasingly less so) are bachelor’s degrees in data science and related ﬁelds (e.g.,

data analytics). These programs make up the next level of the pyramid, with larger numbers of students

potentially entering the workforce (National Academies of Science, Engineering, and Medicine 2018). The

options—which are certain to grow in the coming years—already provide two-year college students who are

interested in data science with visible future programs of study.

Gould et al. (2018) identiﬁes six associate’s degree programs in three states, including New Hampshire,

Pennsylvania, and Minnesota. A number of exemplary associate’s data science programs have been established

in recent years (Amstat News 2022). Many others have been created across the nation.2

2.2 Key concepts in two-year college education

Two-year college students typically pursue associate’s degrees that come in two ﬂavors: terminal or transfer.

Many associate’s degrees are terminal (often called associate’s-to-workforce), in that they are designed to

prepare students for employment directly upon completion. Other associate’s degrees are designed to prepare

students for a smooth transfer to a four-year institution (and even a speciﬁc bachelor’s degree program at

that institution) upon completion. For example, Springﬁeld Technical Community College oﬀers multiple

degrees in computer science. The Computer Systems Engineering Tech program prepares students for various

systems administration jobs after two years of study. Conversely, the Computer Science Transfer program

prepares students to transfer to a bachelor’s program in computer science, with most students presumably

intending to transfer to one of the UMass campuses.

We use the term pathway to describe a route that a two-year college student could take to obtain a bachelor’s

degree. Associate’s degrees designed for transfer, as described above, are the most well-trod starting places

for such pathways. But even with an associate’s degree for transfer in hand, pathways are not always obvious.

Many states, including Massachusetts and California, have highly visible public websites that map transfer

pathways from two-year colleges to public four-year colleges. However, not all of these many-to-many possible

pathways are mapped. For example, Bunker Hill Community College oﬀers a Computer Science Transfer

associate’s degree, but there is no corresponding mapping to any of the UMass campuses in the system (see

Section 3).

Further complicating matters are articulation agreements, which provide an explicit transfer pathway between

one speciﬁc associate’s degree program and one speciﬁc bachelor’s program. These agreements may be

negotiated between public or private four-year institutions. While these one-to-one articulation agreements

are helpful, they are not as visible as the many-to-many mapped pathways.

The Academic Data Science Alliance Data Science and the American Mathematical Association of Two-Year

Colleges (AMATYC) have created listings that are likely incomplete. Unfortunately, no comprehensive census of

programs is readily available.

A preprint - January 9, 2023

2.3 Outcomes for undergraduates

The choice of which ﬂavor of associate’s degree to pursue has consequences for the two-year college student.

Many workforce roles for data scientists exist at the bachelor’s level (De Veaux et al. 2017; National Academies

of Science, Engineering, and Medicine 2018), and the number is growing (Gould et al. 2018).

For those who choose further study, the bachelor’s-to-master’s transition is characterized by ﬂexibility and

adaptation, because graduate schools know that they will receive applications from students who attended a

wide variety of undergraduate schools, and who studied highly variable subjects therein. Moreover, bachelor’s

programs typically involve at least 120 credit hours of study, which often provides ample ﬂexibility for a

student to deviate from any pre-deﬁned curricular path. From our own experiences, we know that it is not

uncommon for a traditional bachelor’s student to major in say, economics, only to then decide before their

senior year that they want to pursue a master’s degree in data science, load up on statistics and computer

science courses in their senior year, and still put together a competitive graduate school application.

It is important to remember that dramatically less ﬂexibility is available for the associate’s-to-bachelor’s

transition, since for two-year college students, every credit counts. We recognize that for most two-year college

students, any credit that doesn’t count towards their associate’s degree program or their pre-deﬁned transfer

pathway may be considered a “waste” of both time and money. California has been a leader in fostering

smoother articulation of courses between two-year and four-year institutions (see

https://assist.org

). But

while the California system provides a clear solution for existing pathways, the larger diﬃculties with transfer

pathways are longstanding (Blumenstyk 2021). In Massachusetts, although most students who enroll in

two-year college program after high school intend to transfer to a bachelor’s degree program, relatively few

actually do so (Murnane et al. 2022).

Longer-term, alternative options, including associate’s-to-workforce programs (Rawlings-Goss et al. 2018;

Gould et al. 2018) are desirable but outside the scope of this paper. Associate’s programs in cybersecurity,

information technology, and web development—designed as terminal degrees—have proven eﬀective in

workforce development and the same potential exists for data science.3

2.4 Data science curricula

Undergraduate curricula in data science are now beginning to coalesce. De Veaux et al. (2017) provide

curriculum guidelines for undergraduate majors in data science that are endorsed by the American Statistical

Association. The “Data Science for Undergraduates: Opportunities and Options” consensus study (National

Academies of Science, Engineering, and Medicine 2018) provided a number of recommendations and ﬁndings

relevant to undergraduate data science programs and outlined key aspects of data acumen. The Association for

Computing Machinery (ACM) Data Science Task Force enumerated computing competencies for undergraduate

data science curricula (Danyluk et al. 2021), and syllabi from example courses. Gould et al. (2018) provides

curricular guidelines for two-year college programs in data science. Comprehensive textbooks (Wickham and

Grolemund 2016; Baumer, Kaplan, and Horton 2021) and course materials (Çetinkaya-Rundel 2020) support

the teaching of a variety of diﬀerent introductory data science courses. Donoho (2017) ruminates on the

nature of data science as a standalone scientiﬁc discipline.

In 2019, the National Center for Education Statistics unveiled a new series of Classiﬁcation of Instructional

Programs (CIP) codes for data science (30.70). These new codes allow the federal government to track the

growth of programs in data science and should result in an improved ability to quantify how many students

are studying data science.4

In what might be an important stamp of legitimacy, ABET (Accreditation Board for Engineering and

Technology) has begun accrediting its ﬁrst undergraduate data science programs, with plans to expand to the

graduate and associate’s levels.

In contrast to the 45,000 master’s graduates in data science referenced earlier, according to Statista (Duﬃn 2021),

there were more than a million associate’s degree recipients in the United States during the 2018-2019 academic

year.We believe that associate’s degree candidates represent an untapped data resource.

Until recently, the new CIP codes were not classiﬁed as STEM disciplines, which had negative implications for

the immigration status of international students. Eﬀorts by the Academic Data Science Alliance and others led to

reclassiﬁcation of the data science CIP code.

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DataScienceTransferPathwaysfromAssociate'stoBachelor'sProgramsAPreprintBenjaminS.BaumerStatistical&DataSciencesSmithCollegeNorthampton,MA01063bbaumer@smith.eduNicholasJ.HortonMathematics&StatisticsAmherstCollegeAmherst,MA01002nhorton@amherst.eduJanuary9,2023AbstractAsubstantialfractionofstudentswho...

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