The goal of the Master of Science in Data Analytics program is to produce highly-skilled and educated data analysts who can transform Big Data into usable information for decision makers across a variety of disciplines including business, healthcare and national security.
The curriculum combines a blend of business, information technology, marketing, mathematics and statistics coursework. Core competencies include data analytic algorithms, predictive modeling, data architecture management and analytical interpretation.
The program focuses on traditional business intelligence oriented analytics and provides students specialized expertise in the areas of data science management and data analytic algorithms. Students learn to analyze data sets and develop communication and visualization techniques to shares these insights within organizations.
Drawing upon experiential learning, students will become data savvy professionals and learn the latest tools, techniques and applications used to transform data into meaningful information. Further, they will apply their education by performing real-world data analytics through intensive practicum coursework with strategic business partners.
Admission: Fall semester
Credit Hours: 30 credit hours
Format: Daytime and evening cohorts
Duration: 12 months for daytime cohort/21 months for evening cohort
Funding: Scholarships are available
Must complete university-wide graduate admission requirements in addition to the following.
- A completed application form
- A statement of academic and personal goals
- Transcripts from all colleges and universities attended
- Official Graduate Management Admission Test (GMAT) or Graduate Record Examination (GRE) scores (no more than five years old)
- A current resume with employment or other experience
- Letters of reference
Applicants will be evaluated for success in the program based on demonstrable academic preparation and/or experience with respect to mathematics, statistics and information technology. Coursework in calculus, differential equations, stochastic processes, statistics and data mining are not required, but show foundational mathematical preparation and are preferred in some combination.
Information systems/technology courses, computer science courses, and/or professional experience related to databases, networks, distributed and cloud infrastructures, and programming are not required, but show foundational information technology preparation and are preferred in some combination.
- Data Analytics Tools and Techniques
- Data Analytics Visualization and Communication
- Data-Driven Decision Making and Design
- Data Analytics Application Studies
- Data Foundations
- Big Data Technology
- Data Analytics Algorithms
- Data Analytics Practicum