The Rise of Undergraduate Data Science Programs

Unlike most academic fields, data science did not begin as an undergraduate discipline - but rather as a fundamentally master’s-level discipline with heavy emphasis on application. As the evolution of data created unprecedented opportunities and challenges for all sectors of the economy, master’s-level programs with heavy emphasis on analytics and predictive modeling began emerging in 2006. Doctoral programs with emphasis on innovation and research first emerged in 2015- While most students who ultimately pursued a master’s or doctoral degree in data science studied mathematics, statistics, computer science, or engineering, colleges and universities began offering formal bachelor’s-level programs in the field in 2018. Over the past several years, undergraduate programs in data science have emerged at colleges and universities at all levels across the country6. These undergraduate programs aim to fill a niche between less technically demanding “business analyst” programs offered by business departments and more technically demanding, but typically less applied, mathematics, computer science, and statistics programs.

Most undergraduate programs in data science focus on producing graduates who possess a high level of mathematical and statistical expertise, programming skills in several languages, a basic background in computer science, and a healthy sense of how to use their technical skillset to solve real-world business problems.

Bachelor’s-level programs present analytics managers with opportunities for fruitful engagement, and they may be underleveraged by the business community given the relative newness of bachelor’s degrees in the field.

As noted above, unlike engineering, computer science, or accounting, data science does not have an accrediting body or nationally standardized curriculum

(the Certified Analytics Professional or “CAP” credential offered through the organization Institute for Operations Research and the Management Sciences (INFORMS) is gaining recognition but is not a curricular credentialing body (see Chapter 6)). While there are some common courses to be found in almost all data science programs (e.g., machine learning, programming, predictive analytics, database structures), programs across the country continue to exhibit variation in expectations related to mathematics, programming languages, and areas of application. These differences are largely a function of where the program is housed. Programs in data science can by housed in the College of Computing, the College of Science, the College of Engineering, the Business School, or in a “neutral” center or institute.

It might be useful to think about the discipline of data science like tofu - the core protein is the same, but the flavors will be different depending upon the preparation.

Staying with this analogy, an undergraduate data science program housed in a College of Business may require many of the same courses as a program housed in the College of Computing - but they will have very different “flavors” at the end - making for two very different recruiting targets.

It can be a very confusing hiring market when students with the same degree - data science - have different non-standardized skills; communications, modeling, and programming are all important skills for data science students, but students' strengths and weaknesses will vary depending if they are studying in the business college, the college of computing or somewhere in between.

This is a critical concept for analytical managers working with universities to fully understand and appreciate. Consider three very different undergraduate degrees in data science highlighted in Table 3-1.

Three of the early formal undergraduate degrees in data science programs are housed at the College of Charleston7, the University of Georgia8, and the University of New Hampshire9. These universities each have well architected curriculums in data science and reflect many of the high-impact educational practices listed above. It is worth noting that the University of Georgia and the University of New Hampshire are both classified as “Doctoral University: Very High Research

Table 3.1 Comparison of Three Undergraduate Data Science Programs

College of Charleston

University of Georgia

University of New Hampshire

Academic

Location

School of Science and Mathematics

College of Arts and Sciences (Department of Statistics)

College of Engineering and Physical Science (Department of Computer Science)

Total Number of Required Credit Hours

122

120

128

Noteworthy

Courses

Students are required to have an area of emphasis for their electives (business, science, social science, arts, and humanities)

Students must take a course in data security and privacy. Elective option in Computing Ethics and Society

Students are required to take a four-credit-hour course in Professional and Technical Writing

Internship/

Capstone

Requirements

Capstone course required

Capstone course required, plus internship elective

Students must take a capstone course, the internship preparedness course, and a three-credit internship

Activity”, while the College of Charleston is classified as “Master’s College and University” (see Table 2.1).

For analytics managers who may be interested in collaborating with any of these three programs (they are all excellent), they should consider where these programs are housed within the respective universities; while all three programs have many of the same courses, the academic location will influence the different skills, strengths, and weaknesses of the students.

 
Source
< Prev   CONTENTS   Source   Next >