The Rise of Data Science and Data Analytics Programs
While the rise of AI has received the lion’s share of national attention in recent months, let’s take a moment today to remember its not-so-distant cousins: data analytics and data science. While these fields may have stepped out of the spotlight for now, they are still going strong.
Data analytics and data science, for example, remain among LinkedIn’s 2023 Most-In Demand Skills. And they are a key feature of GMAC’s 2023 survey in which prospective graduate management students rank business analytics/data science as the second most important piece of a management program’s curriculum.
With these concepts fully permeated across the labor market, what do we know about the state of data analytics and data science programs at colleges and universities?
In 2020, the National Center for Education Statistics (NCES) released a long-anticipated, once-in-a-decade update to its Classification of Instructional Program (CIP) codes to add several specific to data science and data analytics for program reporting—a step many hoped would make it easier to understand the program landscape. Despite this, understanding program performance remains tricky. This is because actual program reporting among schools lags long-awaited code updates.
To get the best handle on this market, Eduventures uses several CIP codes as a proxy. Figure 1 tracks completions across all certificate and degree levels between 2012-2021 for our data science/data analytics proxy which includes a combination of codes specifically developed in 2020 and several others these programs are still often reported to in practice.
Figure 1 shows that over a period when all completions grew by 9%, the overall data science/data analytics market proxy grew by over 700% revealing an explosion of student demand—growing from just under 6,000 completions in 2012 to over 46,000 completions in 2021.
About 96% of all completions are at three credential levels: master’s (68%), bachelor’s (23%), and postbaccalaureate certificate (5%). Compared to the entire market (across all fields of study and at all credential levels), the data science/data analytics market is over-indexed at the master’s (68% vs. 16%) and postbaccalaureate certificate (5% vs. 1%) levels, and under-indexed at the bachelor’s (23% vs. 40%) level. This underscores that graduate programming has driven much of this field’s growth.
Driving the Growth
Certainly, one reason for the explosive student demand around data science/data analytics programs is how ubiquitous the aligned skills have become across industry sectors and individual jobs over the last five years. For example, an isolated search for job postings using key words “data analysis” reveals occupations like Software Developers, Management Analysts, Market Research Analysts, and Sales Managers. Topping the list, however, is Data Scientists with over 400,000 unique job postings since June 2018, according to Lightcast.
To understand the rise of data science/data analytics programming, one just needs to observe the rise of the Data Scientist occupation. Figure 2 details the 10 fastest growing occupations from the last decade and the 10 fastest forecasted occupations of the next decade.
Fastest Growing Occupations, Past and Future
|2012-2021 Occupational Growth||2023-2032 Occupational Growth (F)|
|Occupation*||2012-2021 %||Occupation*||2023-2032 %|
|Mathematical Science Occupations, All Other||1,265%||Nurse Practitioners||38%|
|Data Scientists||339%||Data Scientists||32%|
|Information Security Analysts||162%||Statisticians||28%|
|Nurse Practitioners||145%||Information Security Analysts||28%|
|Financial Examiners||125%||Medical and Health Services Managers||27%|
|Market Research Analysts and Marketing Specialists||124%||Software Developers||25%|
|Web and Digital Interface Designers||112%||Epidemiologists||23%|
|Human Resources Specialists||108%||Logisticians||23%|
|Entertainment and Recreation Managers, Except Gambling||107%||Computer and Information Research Scientists||22%|
*Occupations with bachelor’s or above typical education at entry
Data Scientist occupations, which include jobs like Data Mining Analysts, Data Analytics Specialists, and Business Intelligence Developers, grew by almost 340% over the last decade (from 41,000 to 170,000 jobs)—well above the 15% average. Looking ahead, these jobs are projected to grow by 32% over the next decade when all jobs are projected to grow by 9%. In both cases, Data Scientists are the second fastest growing occupation.
Beyond Data Scientists though, many of the fastest-growing occupations (projected), including Statisticians, Logisticians, and Computer and Information Research Scientists, rely on some of the same skill sets. With continued projected growth and evidence all around us pointing to the demand for aligned skills, there are few signs pointing to a future dip in demand.
The Bottom Line
Schools with active programs in the data science/data analytics market, or those considering a program launch, should keep in mind the following considerations:
- Be aware of the alternative provider. Outside of the research of NCES, the data science/data analytics field is massive. A “data science” key word search on Coursera nets over 3,300 courses, projects, specializations, certificates, and more offerings, and “data analytics” nets over 2,000. Many of these are offered by companies like IBM, Google, and Meta. Given the over-indexed activity at the graduate level in these fields, a significant proportion of this prospect pool are workers seeking to fine-tune these increasingly critical skills. Understand the target audience and competitive landscape in this space before diving in.
- Look for opportunities to integrate skills in your undergraduate curriculum. The below-average undergraduate activity in the data science/data analytics field doesn’t mean there is no opportunity at this level. Rather, it just might look different. Given the range of sectors and roles seeking data science/analytics skills, it is increasingly important for undergraduates to enter the workforce with at least a foundational understanding of the core concepts in these programs. Schools should find ways to integrate this content across undergraduate programs.
- Consider which programs are right for your school. While this analysis has treated data science/data analytics as a single market given the challenges of clearly differentiating these programs, they are two distinct concepts. The former focuses more on building, cleaning, and modeling data sets, while the latter focuses more on the process of analyzing data to inform insights and decision-making. Carefully assess what your school is best equipped to offer and what your unique prospect pool is looking for.
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