The Master’s Degree

The M.S. in Data Analytics is offered in on-campus, online, and blended formats. On-ground students will have the option of taking online or blended classes as well.  Applicants to the online Data Analytics degree are accepted each fall semester. The Data Analytics program may, at its own discretion, accept new students in other semesters, and may consider accepting students under conditional admission, thereby allowing students to complete program entrance requirements during spring and fall terms.


On acceptance, students are assigned a member of the Data Analytics faculty to serve as their academic advisor. Before registering for the first time, the student should discuss an appropriate course of study with the academic advisor.

Grading Policy

Students must earn a grade of B- or better in all courses that apply toward the degree, and a cumulative 3.0 grade point average is required to graduate. In addition, graduate students who do not maintain a 3.0 grade point average will be placed on academic probation according to campus policy. Graduate students enrolled in 400-level courses should expect more stringent grading standards and/or additional assignments. Courses taken on a CR/NC basis will not count toward the degree.

NOTE: Students also should refer to the campus policy on Grades Acceptable Toward Master’s Degrees section of this catalog.

Transfer Courses

Students are allowed to transfer a maximum of eight graduate semester hours with a grade of B or better. They will be evaluated on a case-by-case basis and approved by Student Petition form. Transfer students will be required to take a minimum of 28 credit hours of Data Analytics core and elective course work at UIS.

Degree Requirements

Students must complete all prerequisites and 36 credit hours including 28 required credit hours and eight elective credit hours to earn the Data Analytics degree while maintaining a minimum GPA of 3.0 on a scale of 4.0 as listed below.

  • 25 hours of prerequisites. The students will not receive graduate credit for prerequisite courses.  The prerequisite courses must be completed with a minimum grade of B- before full admission to the Data Analytics program (see Admission Requirements for details).
  • 28 required credit hours with a minimum grade of B-.
  • Eight elective credit hours with a minimum grade of B-.
Prerequisite Courses 25
Computer Programming Concepts I
Computer Programming Concepts II
Discrete Structures
Data Structures and Algorithms
Matrix Analysis and Numerical Optimization
Business Calculus
Calculus I
Applied Statistics
Required Courses 28
Introduction to Database Systems
Data Mining
Introduction to Statistical Computation
Advanced Statistical Methods
Introduction to Machine Learning
Advanced Topics in Computer Systems
Big Data Analytics
Advanced Topics in Computer Systems
Data Analytics Capstone 1
Electives (choose two): 8
NoSQL Databases
Data Visualization
Advanced Database Concepts
Operations Research Methods
Operations Research Methods
Advanced Topics in Data Analytics
Total Hours 36

     The capstone project will draw upon the knowledge and skills learned throughout the entire curriculum and will ask students to apply the appropriate methods and tools for data analysis in a real-world organizational setting. The capstone course provides the opportunity to exercise different techniques for data storage, preprocessing, integration and analysis covered throughout the M.S. in Data Analytics curriculum in order to address business challenges. The students must provide a well-written report and an oral presentation to effectively communicate their findings.