WGU's Data Analytics Degrees Align to Real Jobs 

At WGU, our mission is rooted in helping learners build skills that translate directly into opportunity. As the workforce evolves rapidly, especially across data analytics, AI and applied decision-making, it’s more important than ever that academic programs stay aligned with real-world needs.

The WGU Data Program Industry Advisory Board is a group of exceptional leaders from industry, technology, education, finance, retail, manufacturing, energy and the military that play a critical role in helping us to validate emerging skills in the marketplace. 

We identify the most important skills that certain jobs need and build degree programs that teach those skills. This means that when our students earn a degree, it can have a significant impact on their economic mobility and situation.

1. Human Skills Are Required for Technical Jobs

“We’re definitely looking for people with the right technical skills, but the real 'must-have' is the ability to actually apply those skills to solve problems. More importantly, they need to be able to take those complex analytics and translate them for stakeholders who don’t have a tech background.” 
— Dr. Kai Zhou, data scientist, Human Capital Management, San Antonio Independent School District

“Critical thinking skills are very, very important. We actually don’t provide our junior employees with AI coding assistance tools in integrated workflows because we don’t want them to become a crutch. Junior level individuals and interns have to demonstrate their critical thinking abilities. Then, when you put these tools in the hands of experienced developers, the productivity improvements are strong.”  
— Jeremy Graybill, director of data science, advanced analytics and software engineering, LyondellBasell (LYB)

At WGU, our tech degrees emphasize power skills like communication, collaboration, critical thinking and leadership because we recognize that our students become more employable as AI takes over more mundane, entry-level tasks.

2. Upskilling for the AI Revolution

“We already have a lot of solutions around machine learning, AI and generative AI internally, but we’re looking for more scalable solutions to transform the way we’re working. I think that’s the biggest ask from our leaders. They have seen the successful stories from different parts of business. How can we get all the learnings together to speed up and deliver enterprise-level solutions, whether using machine learning AI, generative AI, AI agents, or all of the above?”  
— Dr. Bo Hu, director of data science and AI, ConocoPhillips

“A trend within the company at the moment is that there’s an immense amount of training going on, teaching the analytics people first how to utilize generative AI and create agents. Next, we are looking for more ways to automate our processes as much as possible to reduce costs and optimize production.”  
— Dr. Mustafa C. Kara, data science and analytics team lead for customer insights, Chevron

WGU’s M.S. in Data Analytics with a Decision Process Engineering Concentration incorporates programming, math and business influence skills throughout the program. It combines decision intelligence, process engineering, project management, integration of human decision-making, and a master’s level data analytics core curriculum together into one offering. 

3. Python Still Rules

“When I’m doing hiring, a must-have requirement is knowing Python. I expect my teams to know Python and coding languages. Yeah, AI can generate code, but it doesn’t know my bank’s firewall. It doesn’t know my bank’s specific idiosyncrasies. At the end of the day, there’s so much you can do with just knowing that core language.”  
— Nolan Argiento-Hill, senior vice president of HR analytics and data governance executive, Bank of America, and WGU alumnus 

“At Ford, we have various tools but, because some of the licenses become too expensive, on the programming side we mostly use Python and GitHub. The majority of my team is doing forecast modeling, and we often use Python, the machine learning model, at the same time that we use the statistical model.” 
— Yi Lu, senior director of business and sales planning analytics, Ford Motor Company

The B.S. in Data Analytics includes courses in Python, scripting and programming, while the M.S. in Data Analytics teaches about machine learning, modern analytic tools and languages including Python, R, SQL and Tableau.

Translating Industry Insight Into Program Design 

What stood out from this advisory conversation is not just which skills are in demand, but how quickly the role of the data analyst is changing. Today’s analysts are expected to combine technical fluency, AI awareness and business judgment in ways that didn’t exist even a few years ago. That shift is directly shaping how we evolve our data analytics programs. 

Our goal is not to chase trends, but to design a program identity that reflects how analytics is actually practiced in modern organizations. The advisory board reinforced three priorities that guide program decisions: 

• Analytics is a communication discipline, not just a technical one. 
  Employers emphasized that successful analysts translate insights into action. That’s why our curriculum pairs Python, SQL and analytics tooling with data storytelling, stakeholder communication and applied decision-making. We are preparing analysts who can influence outcomes, not just produce reports. 
• AI is becoming embedded in everyday analytics work. 
  AI is no longer a niche specialization — it is part of the analyst’s workflow. Our program is evolving to integrate AI literacy, practical applications and responsible use so graduates understand how to work alongside emerging technologies rather than treat them as separate domains. 
• Demonstrated skill matters more than theoretical exposure. 
  Hiring managers want to see evidence of capability. The structure of the data analytics program emphasizes applied projects, real tools and portfolio-ready work, so learners graduate with proof of what they can build and deliver.

These industry conversations help ensure the data program remains grounded in workforce reality. They allow us to continually validate what we teach, refine program direction and keep the focus where it belongs: preparing graduates who are immediately effective and adaptable in a fast-moving analytics landscape.

Looking Ahead

I’m deeply grateful to our advisory board members for contributing their time, expertise and insight. Together, we will continue to strengthen program quality, workforce relevance and student success.

If you’re interested in partnering with us, whether as an employer, industry collaborator, or future advisory board member, please visit the partnerships page. We’re building something meaningful, and we’re doing it together.