Data Analytics

Program Facts

Delivery Method:

Online

Start Term:

Fall, Spring

Completion Time:

10 Months

Credits:

Request info about Data Analytics

About Our Data Analytics Program

Data analytics is one of the fastest-growing fields in today’s job market. The U.S. Bureau of Labor Statistics projects 35% job growth through 2032, and organizations across industries are seeking professionals who can turn complex data into smart decisions. Slippery Rock University’s online MS in Data Analytics prepares you to meet that demand with advanced, real-world skills in analytics, machine learning, and data strategy. Ranked among the top 25 data analytics programs in the country by Tech Guide in 2024, this degree can elevate your résumé and boost your earning potential in a data-driven world.

Why Get a Data Analytics Degree?

Learn from anywhere with 100% online coursework designed to fit your schedule.
Earn your degree on your terms—complete the 33-credit program in just 10 months full-time or two years part-time.
Gain a competitive edge with a Statistical Analysis System (SAS) Academic Specialization in Data Analytics.
Build real-world skills in modeling, big data, machine learning, and modern data technologies.
Grow with expert support from dedicated faculty with real-world business experience.
Get industry-ready with an optional internship to apply your skills in a professional setting.

Accredited. Respected. Proven.

Slippery Rock University's Data Analytics program is recognized by SAS, a global leader in analytics software, allowing students to earn a valuable SAS-endorsed credential upon graduation. This recognition signifies that SRU's curriculum is aligned with the highest industry standards and prepares students with the most in-demand skills in the field. This credential makes graduates highly marketable, as employers worldwide recognize the SAS name as a benchmark for excellence in data management, machine learning, and predictive modeling.

Career Outcomes

A Master of Science in Data Analytics prepares you for a career in one of the fastest-growing fields in the world, with the U.S. Bureau of Labor Statistics projecting a 35% job growth for data scientists through 2032. Graduates are in high demand across every industry—from finance and healthcare to technology and marketing—as organizations seek experts who can translate complex data into actionable business strategy. With an advanced degree and a SAS-endorsed credential, you'll be a top candidate for a wide range of lucrative and influential roles. Common career paths for graduates include:

Data Scientist
Senior Data Analyst
Business Intelligence (BI) Developer
Data Analytics Manager
Machine Learning Engineer
Quantitative Analyst
Marketing Analytics Manager
Statistical Consultant

The knowledge and skills I've gained at SRU have directly contributed to my recent promotion and continued career growth. I would recommend this school for anyone looking for a well rounded education and one on one support from faculty.

Sarah Roudybush ‘25, Senior Associate
Product Governance at BNY

Requirements

What are the admission requirements for this program at SRU? Check out the requirements in the dropdowns of this section!

Program Director / Coordinator

Graduate Coordinator
Dr. Amanda Goodrick
Mathematics and Statistics
724.738.2352
amanda.goodrick@sru.edu

When are applications accepted?

Email graduate.admissions@sru.edu or call 724.738.2051 for information regarding this program’s graduate application deadlines.

What are all of the admissions requirements?

All applicants must submit the following materials along with a completed online graduate application and non-refundable application fee:

All students interested in data analytics or data science are welcome to apply
Official undergraduate degree transcript(s) and any graduate transcripts
Students with a GPA below 3.0 may apply for conditional admission
GRE not required

Please note: For more information, visit www.sru.edu/graduate. Transfer course evaluation information can be found on our graduate website.

Data Analytics (M.S.)