It’s no mystery that data jobs are hot. It’s been nine years since Harvard Business Review famously labeled “Data Scientist” the “sexiest job of the 21st century.” Over the past four years, job postings for roles that include “data” in the title have grown 135%. In the past year alone, demand for those same titles is up 13% (and that’s in a pandemic economy).
And demand is only poised to grow. It’s been estimated that a mind-boggling 463 exabytes (what even is that?) of data will be created each day globally by 2025. Much of that data is valuable to businesses…but only if they have someone around to make sense out of it.
In other words, the ocean of data represents an ocean of opportunity for job-seekers willing to learn the necessary skills.
The diverse world of data
And it’s not just “data scientists” that are in demand. It’s also the plethora of complementary roles that have cropped up around them in the fertile fields of data-related work.
Think of it this way. While a small business may just have one bookkeeper (and maybe hire an accountant at tax time), an established Fortune 500 company is likely to have a full-fledged accounting department. To the uninitiated outsider, everyone in that department may just be an “accountant.”
In reality though, “accounting” is a complex constellation of interrelated specialties: accounts receivable, accounts payable, tax, internal audit, payroll, etc.
Likewise, as technology has advanced and more businesses have matured in their capacity for harnessing all this data, the diversity of data jobs has grown. Sophisticated organizations today may employ a team of data workers who specialize in collecting, organizing, analyzing, querying, displaying, or interpreting data. It takes all these specialists working together to channel the fire hydrant of data into a focused stream of useful insight.
Drawing a skill-based roadmap for learners
For educators and training providers, these nuances matter. Prospective students, adult learners, and displaced workers look to their college or university to help them both clarify their goals and deliver the education they need to reach those goals. In order to provide this guidance and offer these programs, institutions themselves need to first understand the lay of the land.
To get a sense of these differences at the national level, let’s take a look at some of the top skills for three common job titles in the data world: data analysts, data engineers, and data scientists (view the full Tableau here).
Some quick takeaways
- Machine Learning and Algorithms are noticeably more prevalent in postings for data scientists than for analysts or engineers. This speaks to the role of data scientists as those who write the rules that computers follow to “learn” and adapt as they process more and more data. These dynamic programs can help businesses and consumers turn big data into huge value.
- The Extract, Transform, Load (ETL) procedure is much more important for engineers than analysts or scientists. If you plan to be a data engineer, responsible for moving and storing data, this is evidently a key competency to develop.
- Python and R are two powerful, popular programming languages for manipulating and analyzing data. Both are in demand for data analysts (23% and 17%, respectively), and data scientists (70% and 52%, respectively), but Python appears to be more popular for both roles, and the gap is most pronounced for data engineers (60% vs. 12%). The age-old debate about which language to learn will likely rage on (with the true answer always being: “it depends on your background and end goal,”) but Python appears to be ahead for now, at least in terms of how frequently it appears in job postings.
- SQL is consistently in demand across all three roles (54%, 56%, 49%). The ability to query a database (ask it questions to get the specific pieces of information you need) is useful no matter your specialization.
- Tableau (the data visualization tool used to make the above heat map!) is most popular for data analysts (25%), but also relevant for data scientists (18%) and to some extent, for data engineers (13%). Since analysts are the ones most often looking at data to draw out and communicate insight, this makes sense.
Rounding out the analysis Asking related questions
This is just a brief, high-level overview to illustrate the subtle shades of skill demand that can exist across different areas of specialization. Other questions you could ask to better understand this market would be:
- Who are the top employers hiring for these roles?
- Do in-demand skills vary by employer?
- What other institutions are offering programs that prepare students for these jobs?
Accounting for regionality
It’s also important to account for regionality when researching the labor market your grads will enter. In the above example, we looked at a national picture. For colleges and universities that cater to a particular state, county, or city, it’s important to narrow your research. Realistically (and depending on your institution’s reach, demographics, and mission), you may want to align your curriculum to skills and job roles needed by local businesses rather than national ones.
For example, here are the top employers for data scientists in Virginia vs. Texas:
Mission-critical insight, at your fingertips
This kind of labor market data is invaluable for higher ed decision makers. It provides the insight needed to guide mission-critical functions like student advising, program development and review, and employer engagement (e.g. program advisory boards and internship programs).
And while this may seem like a lot of information to gather, Emsi’s Analyst platform makes it pretty quick and easy to pull together. From there, you can export reports to share with stakeholders, and dive back in to your saved projects for further research (or to answer those inevitable follow-up questions).
Want to see Analyst in action? Use the form below to let us know what questions you’re trying to answer, and we’ll get in touch to show you how our data can help. (Or, request a free program overview report from Analyst).
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