Data Analytics

Contact Information

Website: www.uis.edu/data-analytics
Email: dat@uis.edu
Office Phone: (217) 206-8338
Office Location: WUIS #3

Masters Degree
Graduate's Certificate

The M.S. in Data Analytics, an OPT-STEM program, 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. The degree is offered in both on-campus and online formats. Applications are accepted each spring and fall semester. The Data Analytics program may, at its own discretion, accept new students in the summer semester, and consider accepting students under conditional admission, thereby allowing students to take classes at UIS to complete the program's entrance requirements. Upon the completion of all entrance requirements, the student will be fully admitted.

Students must have completed a course in data structures and algorithms to be considered for admission to the master’s degree program.

Note: Students enrolled in the M.S. in Data Analytics degree program are not eligible for the graduate certificate in Data Analytics.

The Master's Degree

Advising

On acceptance, students are assigned 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.

Program Learning Outcomes

Exhibit understanding of the key technologies in data storage, data management, data warehousing and analysis.
Demonstrate theoretical and practical knowledge of foundational statistical methods to collect data, analyze and draw inferences from data and interpret the results.
Use data analytics methods and tools to preprocess and clean data sets; discover patterns; extract knowledge from raw data sets; represent, interpret, and evaluate data and predict future behavior, patterns, and trends.
Manage, store, and analyze big data sets that cannot be handled using traditional database systems.
Exhibit the ability to apply various elements of statistics and computer science to solve real-world problems and use appropriate visual, verbal, and written media to effectively present and communicate findings.
Demonstrate the ability to apply Artificial Intelligence and other emerging technologies to analyze data for complex perceptual problems such as computer vision and natural language understanding.

Requirements

Prerequisites
CSC 225	Computer Programming Concepts I	3
CSC 275	Computer Programming Concepts II	3
CSC 302	Discrete Structures	4
CSC 385	Data Structures and Algorithms	4
DAT 332	Matrix Analysis and Numerical Optimization	4
or MAT 332	Linear Algebra
MAT 113	Business Calculus	4
or MAT 115	Calculus I
MAT 121	Applied Statistics	3
Total Hours		25

Required Courses
CSC 472	Introduction to Database Systems	4
CSC 532	Introduction to Machine Learning	4
CSC 534	Big Data Analytics	4
CSC 535	Deep Learning	4
DAT 502	Introduction to Statistical Computation	4
DAT 530	Advanced Statistical Methods	4
DAT 554	Data Analytics Capstone ¹	4
Electives (Choose two)		8
CSC 533	Data Mining
CSC 561	NoSQL Databases
CSC 562	Data Visualization
CSC 570	Advanced Topics in Computer Systems (A.I. for Cybersecurity or Natural Language Processing)
CSC 572	Advanced Database Concepts (A.I. for Cybersecurity or Natural Language Processing)
DAT 444	Operations Research Methods
or MAT 444	Operations Research Methods
Total Hours		36

¹: 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.

Master's Closure

Students who take DAT 554 and do not pass must register for DAT 555 (zero credit hours, one billable hour) each subsequent fall and spring semester until the closure is complete.

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 a Student Petition. Transfer students will be required to take a minimum of 28 credit hours of Data Analytics core and elective course work at UIS.

Students must complete 28 required credit hours and 8 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.

EHS HDC Admissions
Degree Program	Program Type	Dept Application Materials and Admission Criteria	Prerequisite Course Requirements	Department ADM Review	Dept Conditional Admits	Dept Appeal Process
Data Analytics	MS	No additional admission requirements beyond the general UIS criteria	MAT 113 or MAT 115, MAT 121, CSC 302, CSC 225, CSC 275, CSC 385, DAT 332 Students must have completed course(s) equivalent to our Data Structures and Algorithms course to be considered for admission to the master’s degree program.	N/A	Yes	N/A

EHS HDC Admissions
Degree Program	Program Type	Dept Application Materials and Admission Criteria	Prerequisite Course Requirements	Department ADM Review	Dept Conditional Admits	Dept Appeal Process
Data Analytics	Graduate Certificate	No additional admission requirements beyond the general UIS criteria	The prerequisite courses must be completed with a minimum grade of B- *MAT 113 or MAT 115, MAT 121, CSC 302, CSC 225, CSC 275, CSC 385, DAT 332	Department Head	N/A	N/A

Courses

DAT 332. Matrix Analysis and Numerical Optimization. 4 Hours.

This course is an introduction to matrices and numerical optimization with applications in engineering and science. Topics include Algebra of matrices and systems of linear algebraic equations, rank, inverse, eigenvalues, eigenvectors, vector spaces, subspaces, basis, independence, orthogonal projection, determinant, linear programming and other numerical methods. Course Information: Prerequisites: MAT 115 or MAT 113 or equivalent.

DAT 444. Operations Research Methods. 4 Hours.

Quantitative methods necessary for analysis, modeling, and decision making. Topics include linear programming, transportation model, network models, decision theory, games theory, PERT-CPM, inventory models, and queueing theory. Additional topics may be chosen from integer linear programming, system simulation, and nonlinear programming. Course Information: Same as MAT 444 and PAD 431. Prerequisites: MAT 332 with grade of C or better.

DAT 502. Introduction to Statistical Computation. 4 Hours.

Explore the use of various statistical software packages, such as SAS, SPSS, and R. Topics will be selected from construction of data set, descriptive analysis, regression analysis, analysis of design experiment, multivariate analysis, categorical data analysis, discriminant analysis, cluster analysis, and presentation of data in graphic forms. Course Information: Prerequisites: CSC 225 or equivalent and MAT 121 or equivalent.

DAT 530. Advanced Statistical Methods. 4 Hours.

Topics include multiple linear regression, statistical inferences for regression model, diagnostics and remedies for multicollinearity, outlier and influential cases, model selection, logistic regression, multivariate analysis, categorical data analysis, discriminant analysis, cluster analysis. Course Information: Prerequisites: MAT 121 or equivalent.

DAT 554. Data Analytics Capstone. 4 Hours.

This is a practicum course that allows 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 Master of Data Analytics curriculum in order to address challenges from different areas. Course Information: Co-requisites: CSC 534 and CSC 535.

DAT 555. Data Analytics Capstone Continuing Enrollment. 0 Hours.

This course is required for DAT students who took but have not completed Capstone course DAT 554. Students must register for DAT 555 for zero credit hour (one billable hour) in all subsequent semesters until DAT 554 is completed. Course Information: Restricted to DAT majors.

DAT 570. Advanced Topics in Data Analytics. 4 Hours.

Topics and prerequisites vary. Students may refer to the course schedule for topics and prerequisites. Restricted to Graduate Students, Data Analytics majors or Computer Science majors.