Key Takeaways
- Data science and data analytics are two genuinely different career tracks – one builds systems, the other interprets them.
- MS in Data Science programs center on programming, machine learning, and mathematical modeling; MS in Data Analytics programs focus on business intelligence, visualization, and decision support.
- Data scientists earn more on average than data analysts, though the gap varies by source; BLS median figures put data scientists at $112,590 and data analysts at $83,640 annually.
- In Kansas, most graduate programs lean toward analytics – true data science programs with deep ML and AI coursework are rarer than program names suggest.
- Newman University’s MS in Data Science is one of the few genuinely technical options in the state, built around Python, machine learning, and applied AI – not just dashboards and business reporting.
These two degrees appear side by side on every program comparison list, and they sound nearly identical. They aren’t. The difference shows up in the coursework, the jobs, the salaries, and the day-to-day work that follows. Getting clear on which one fits before enrolling saves significant time and frustration.
Two Degrees, Two Different Jobs
The simplest framing: data science is about building, and data analytics is about interpreting. A data scientist writes code, constructs machine learning models, and creates systems that make predictions or automate decisions. A data analyst examines existing datasets, finds patterns, and turns those patterns into recommendations a business can act on.
Neither path is superior. They are different jobs that happen to share a word. The problem is that most people choose between them based on program titles rather than curriculum – and the titles can be misleading. Newman University’s MS in Data Science addresses this directly, positioning itself as a genuinely technical program built around machine learning and programming, not business reporting with a science label.
What Each Degree Actually Teaches
Data Science: Build, Program, Model
A data science master’s goes deep into the technical foundations of the field. Expect heavy Python programming, advanced mathematics (calculus, linear algebra), statistics, relational databases, distributed computing, and a meaningful sequence of machine learning coursework. The goal is to graduate someone who can build predictive models, train algorithms, and engineer data systems – not just read their output.
The skill set is demanding by design. Data science draws from computer science, mathematics, statistics, and ethics. Programs that take it seriously will reflect that in the curriculum, not just in the program name.
Data Analytics: Interpret, Communicate, Decide
Data analytics and business analytics programs are built around a different goal: helping organizations make smarter decisions with the data they already have. Tools like SQL, Power BI, Tableau, and R appear throughout these programs. The programming load is lighter, the business context is heavier, and the emphasis falls on visualization, storytelling, and strategic communication.
These programs also draw from statistics and data science methods, but apply them toward business intelligence rather than model-building. A business analytics degree integrates coursework from data science, statistics, and business strategy – preparing graduates to translate data for decision-makers, not to build the systems generating that data.
Career Paths Diverge Early
Data Science Roles: ML, AI, Engineering
Data science credentials open doors to roles like machine learning engineer, AI engineer, data engineer, and data scientist. These positions typically sit alongside engineering and IT teams, involve hands-on model development, and require fluency in programming and quantitative methods. Job growth for data scientist roles is projected at roughly 34% between 2024 and 2034 – driven largely by accelerating demand for AI and predictive analytics capabilities.
Analytics Roles: Strategy, BI, Decision Support
Analytics credentials lead more naturally to business intelligence analyst, data analyst, strategy analyst, and decision support roles. These positions sit closer to the business side – working with stakeholders, building dashboards, and translating complex data into plain-language recommendations. For someone coming from a business, healthcare administration, or education background, this path can be a faster and more comfortable transition into a data-focused career.
Which Pays More?
At the senior level, data science typically pays more. According to the Bureau of Labor Statistics, the median annual wage for data scientists was $112,590 in May 2024, while data analysts earned a median of $83,640. The gap reflects the higher technical bar – machine learning expertise and programming fluency are harder to hire for, and employers pay accordingly.
That said, the honest framing isn’t purely about which pays more. A skilled analyst in the right organization will out-earn a data scientist who doesn’t enjoy the work. The better question is which salary range reflects the kind of work that’s actually appealing – and which degree genuinely prepares someone for it.
How to Pick the Right Degree
Read Job Postings First
Before comparing programs, search real job postings for the roles that sound interesting. Look carefully at what they actually require:
- If postings ask for Python, machine learning, model deployment, and statistical theory – that’s data science territory.
- If postings ask for dashboards, SQL queries, business reporting, and stakeholder communication – that’s analytics territory.
Match the degree to the postings, not to the title. A program called Data Science that spends most of its time on visualization and business cases won’t prepare someone for an ML engineer role, regardless of what it’s named.
Be Honest About Your Technical Appetite
This is the question most prospective students skip. How technical do you actually want your daily work to be? Building and debugging machine learning pipelines is genuinely different from building a strategic dashboard and presenting findings to a leadership team. Both are valuable and both require real skill – they just require different kinds of skill.
For someone coming from a non-technical background, analytics can be an easier first step. A data science program built specifically for career-changers can still bridge the gap, particularly if it’s designed to build programming foundations from the ground up.
Kansas Programs: More Range Than You’d Expect
The Kansas graduate market leans heavily toward analytics. Several universities offer degrees in data analytics, business analytics, or applied statistics. These are strong programs, but most are analytics-focused rather than data science in the technical sense.
Program Names Don’t Always Tell the Full Story
This is where prospective students get tripped up. A program named Data Science might center on business intelligence and visualization. A program named Business Analytics might include a serious machine learning track. The label on the degree doesn’t guarantee the content inside.
K-State’s MS in Data Analytics offers both a Data Science track and an Applied Analytics track, giving students the ability to calibrate technical depth. KU’s MS in Applied Statistics, Analytics and Data Science is heavily quantitative, with a strong statistical foundation and connections to research-oriented roles. WSU’s MS in Business Analytics includes a Data Science track that blends technical ML content with business context. Each of these programs has a different center of gravity, and reading the actual curriculum is the only way to know which one fits.
Newman’s MS Is Genuinely Technical
Newman University’s MS in Data Science stands out in the Kansas market because it sits clearly on the data science end of the spectrum – not the analytics end with a new label. The curriculum starts with data analytics and storytelling, moves through data warehousing and engineering, and culminates in a progression of machine learning courses. Data governance, statistics for data science, and data ethics are threaded throughout.
The program was built in direct consultation with industry professionals to keep the tools and content current, and connects to the Newman Institute for AI and the Common Good – tying coursework to applied AI work rather than academic theory alone. Graduates are prepared for roles including data scientist, data engineer, machine learning engineer, and AI engineer.
A few practical details worth noting:
- No GRE required, and the program welcomes students from all prior fields of study
- 10:1 student-to-faculty ratio with small classes and real faculty access
- Available on-campus and online, making it accessible for local Wichita students and those on an F-1 visa
- Approximately 30 credits at $687 per credit, with the majority of students receiving financial aid
- Accredited by the Higher Learning Commission
For career-changers or non-technical students who want to end up in genuine data science roles – not just analyst positions – this structure matters. The program is designed to build the programming and mathematical foundations needed, not to assume them on day one.
To learn more about graduate programs in data science and the broader data education options available, visit Newman University – a Wichita-based institution offering technical, career-aligned paths into one of today’s fastest-growing fields.
Newman University
3100 McCormick
Wichita
Kansas
67213
United States
