The Master’s Degree

The M.S. degree in Data Analytics (MSDA) is offered in on-campus, online, and blended formats. On-ground students will have the option of taking online or blended classes as well. The degree aims at providing an interdisciplinary approach to data analytics that covers both the foundational mathematical knowledge of data science and the computational methods and tools for preprocessing, interpreting, analyzing, representing, and visualizing data sets.  Applicants to the online MSDA 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. Transfer students will be required to take a minimum of 28 credit hours of MSDA 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 MSDA 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 MSDA program (see Admission Requirement 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
MAT 113 Business Calculus 4
or MAT 115 Calculus I
MAT 121 Applied Statistics 3
CSC 302 Discrete Structures 4
CSC 225 Computer Programming Concepts I 3
CSC 275 Computer Programming Concepts II 3
CSC 385 Data Structures and Algorithms 4
DAT 332 Matrix Analysis and Numerical Optimization 4
Required Courses 28
CSC 472 Introduction to Database Systems 4
DAT 502 Introduction to Statistical Computation 4
DAT 550 Advanced Statistical Methods 4
CSC 573 Data Mining 4
DAT 552 Introduction to Machine Learning 4
DAT 553 Big Data Analytics 4
DAT 554 Data Analytics Capstone 1 4
Electives (choose two): 8
DAT 444 Operations Research Methods 4
or MAT 444 Operations Research Methods
CSC 561 NoSQL Databases 4
CSC 562 Data Visualization 4
CSC 572 Advanced Database Concepts 4
DAT 570 Advanced Topics in Data Analytics 4
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 MSDA curriculum in order to address business challenges. The students must provide a well-written report and an oral presentation to effectively communicate their findings.