Data Analytics Education Needs AI and Human Skills
Artificial intelligence (AI) is completely revolutionizing how organizations do work. But despite fears that AI could replace up to half of entry-level white-collar jobs, labor statistics for those working in data science paint a different picture.Â
According to the , “data scientist” is the fastest growing occupation in professional, scientific, and technical services, with 42% growth expected by 2033, also with the highest wages predicted for database architects ($134k median).
Students considering a career in data science face a lot of change and a lot of opportunity, for which we as educators must prepare them. While jobs in data analytics are growing, there is a caveat. The growth has to do with the propensity of the job function to require AI skills. This  shows that data science is the leading category of tech job postings requesting AI skills.Â
To be career-ready and future-ready as data scientists, students must become more AI-enabled, and employers agree. The CompTIA  from April 2025 shows the highest increase of skill training investment by employers in AI ranks data analytics third behind only AI and cybersecurity. With this reality, it’s important for higher education institutions to create programs that equip students with the AI skills needed to thrive.
Degree Programs Informed by Industry Experts
To ensure WGU School of Technology data analytics degree programs adapt and evolve to current employer demands, I meet regularly with the WGU Data Program Industry Advisory Board. This group of senior executives from industry, technology, education, finance, retail, manufacturing, energy and the military play a critical role in helping us to validate emerging skills in the marketplace and build programs that adequately prepare students for the modern workplace. What they shared with me at a recent meeting about the rapid change and important balance between human decisions and AI tools echoes the statistics I mentioned above.
On the acceleration of change, advisory board member , AI innovation lead at Google, said, “The pace of change, especially in the world of AI, is just outstanding. It’s unprecedented change in the way we do things. It’s changing how the enterprises are structured, not just in the day-to-day tasks but also structured from department and functional perspective.”Â
His advice for students about to graduate? “Get ready to change.”
The Needed Mix of Human Skill and AI Capabilities
Those who graduate and continue to invest in their careers will evolve with their roles. AI excels at automating repetitive tasks, but human judgment is still needed to interpret those insights, make critical decisions, and adapt strategies to assess risks.
For example, Klebanov said that the ability to explain data trends or technical domain expertise to the right audiences is an important skill that’s going to remain very, very human. No matter how advanced our technology gets, he urged all to remember that we’re still humans making decisions, signing capital, allocating teams, and designing the direction of projects.
Advisory board member , enterprise architect of data and AI at IBM and three-time WGU alumnus, agreed. “People are the ones who need the technology, who use the technology, who buy the technology, who implement the technology and who make decisions on the technology. If we cannot influence the people, the technology often doesn’t really matter.”
Preparing the AI-Fluent Data Scientists Today’s Employers Need
Higher education institutions have a responsibility to prepare students for the realities of today’s AI work landscape. As the leader in online, competency-based, affordable, and tech-enabled education, WGU is preparing data science professionals to step into AI roles with the technical and power skills employers need. For example, the M.S. in Data Analytics with a decision process concentration incorporates programming, math, and business influence skills throughout the program. It combines decision intelligence, process engineering, project management, unification with human decisions and a master’s level data analytics core curriculum together into one offering.
91ÖĆƬł§Â data analytics degrees also emphasize power skills like communication, collaboration, critical thinking and leadership. These skills will become more marketable as AI takes over more mundane, entry-level tasks.
Advisory board member , vice president of HR analytics and data governance executive at Bank of America and a WGU alumnus, said, “We’re not trying to get rid of people. We’re trying to automate our process and make things better for our people. That’s where the industry is headed: being able to use analytics tools like Tableau and Alteryx, to help you do your job better — not take away your job, but help you improve at it.”
Designing a Data Analytics Program for the AI Era
What these conversations reinforce is that the future of data analytics is not a competition between humans and AI — it’s a collaboration. The modern analyst is expected to understand automation, leverage AI tools, and still exercise judgment, communication and ethical decision-making. That balance is shaping how we evolve data analytics programs.
91ÖĆƬł§â€™s B.S. and M.S. programs in data analytics are intentionally designed around a simple premise: analysts must be able to work with AI, not be replaced by it. That means strengthening three capabilities simultaneously.
First, we continue to prioritize technical foundations — Python, SQL, data modeling, visualization, and modern analytics tooling — because AI amplifies strong fundamentals rather than replacing them. Students still need to understand how systems work in order to evaluate and trust automated outputs.
Second, we are embedding AI literacy and applied use directly into analytics workflows. Rather than treating AI as a separate specialty, we frame it as part of everyday problem-solving. Learners practice using emerging tools responsibly, validating results, and understanding limitations.
Third, we elevate human-centered skills as a core part of analyst identity. Communication, critical thinking, and decision-making are not soft add-ons — they are the differentiators that allow analysts to translate automated insights into real organizational impact.
The advisory board’s message is clear: the analysts who thrive will be those who combine technical fluency with human judgment. Our responsibility as a university is to prepare graduates who are not only AI-enabled, but adaptable — professionals who can grow, evolve and thrive as technology evolves.Â
