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