Data Analytics

Master of Science

www.uis.edu/dataanalytics/
Email: tnguy2@uis.edu
Office Phone: (217) 206-8338
Office Location: WUIS #3

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.

Advising

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 Courses25
MAT 113Business Calculus4
or MAT 115 Calculus I
MAT 121Applied Statistics3
CSC 302Discrete Structures4
CSC 225Computer Programming Concepts I3
CSC 275Computer Programming Concepts II3
CSC 385Data Structures and Algorithms4
DAT 332Matrix Analysis and Numerical Optimization4
Required Courses28
CSC 472Introduction to Database Systems4
DAT 502Introduction to Statistical Computation4
DAT 550Advanced Statistical Methods4
CSC 573Data Mining4
DAT 552Introduction to Machine Learning4
DAT 553Big Data Analytics4
DAT 554Data Analytics Capstone 14
Electives (choose two):8
DAT 444Operations Research Methods4
or MAT 444 Operations Research Methods
CSC 561NoSQL Databases4
CSC 562Data Visualization4
CSC 572Advanced Database Concepts4
DAT 570Advanced Topics in Data Analytics4
Total Hours36
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 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.

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.

Advising

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 a minimum of 36 credit hours including 28 required credit hours, eight elective credit hours and four credit hours capstone course 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-.
  • Capstone Course- DAT 554.
  • Students must maintain a minimum GPA of 3.0 on a scale of 4.0.
Prerequisites25
Business Calculus
Calculus I
Applied Statistics
Discrete Structures
Computer Programming Concepts I
Computer Programming Concepts II
Data Structures and Algorithms
Matrix Analysis and Numerical Optimization
Required Courses28
Introduction to Database Systems
Introduction to Statistical Computation
Advanced Statistical Methods
Data Mining
Introduction to Machine Learning
Big Data Analytics
Data Analytics Capstone 1
Electives8
Operations Research Methods
Operations Research Methods
NoSQL Databases
Data Visualization
Advanced Database Concepts
Advanced Topics in Data Analytics
Total Hours36
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 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.

Degree Program Program Type Dept Application Materials and Admission Criteria Prerequisite Course Requirements Department ADM Review Dept Conditional Admits Dept Appeal Process
MS Data AnalyticsOn campus & Online*Completed a Bachelor's degree with a minimum undergraduate GPA of 3.0 on a 4.0 scale

*Completed all prerequisite courses with grade B- or better

*Provide written evidence of ability to perform at a high academic level by submitting a personal and academic statement

*Students whose native language is not English must submit an official score report from the TOEFL or IELTS submitted directly from the testing center. The only exception is for students who have worked or studied for 5 years or more in Australia, Canada, New Zealand, the Republic of Ireland, the U.K., the U.S., or South Africa. The minimum score required for TOEFL is 550 (PBT), 213 (CBT) or 79 (IBT). The minimum score required for IELTS is 6.5 (academic module).

Online Only: Non-US residents that are not residing in the US are not admitted into the online master's program.
*MAT 113 or MAT 115, MAT 121, CSC 302, CSC 225, CSC 275, CSC 385, DAT 332

N/AYesN/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.

DAT 472. Introduction to Database Systems. 4 Hours.

Examine of file organizations and file access methods, as well as data redundancy. Studies various data models including relational, heretical, network, and object-oriented. Emphasis given to the relational data model SQL, the data definition and manipulation language for relational databases, is described, including database security. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 275. Same as CSC 472.

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 550. 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 551. Data Mining. 4 Hours.

This course teaches advanced techniques for discovering hidden patterns in the rapidly growing data generated by businesses, science, web, and other sources. Focus is on the key tasks of data mining, including data preparation, classification, clustering, association rule mining, and evaluation. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 385. Same as CSC 573.

DAT 552. Introduction to Machine Learning. 4 Hours.

Machine learning explores the design and the study of algorithms that can learn from data or experience, improve their performance, and make predictions. The course provides an overview of many concepts, techniques, and algorithms in machine learning, including supervised learning, unsupervised learning, reinforcement learning, and neural networks. Course information: Prerequisites: DAT 550, CSC 385, and DAT 332.

DAT 553. Big Data Analytics. 4 Hours.

This course teaches concepts and techniques in managing and analyzing large data sets for data discovery and modeling. Focus is on big data management, storage solutions, query processing, analytics, and big data applications. Topics include: introduction to Hadoop and YARN, MapReduce, Apache Spark, Big Data Warehousing with Hive and Spark SQL, large scale recommender systems and Large Scale Clustering and Classification. Course information: Prerequisites: CSC 385, CSC 472, CSC 573.

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: Prerequisites: DAT 552 and DAT 553.

DAT 565. Advanced Database Concepts. 4 Hours.

Study of the implementation of relational database management systems. Topics include database design algorithms, query implementation, execution and optimization, transaction processing, concurrency control, recovery, distributed query processing, and database security. One of the following advanced database topics will also be discussed: deductive databases, parallel databases, knowledge discovery/data mining, data warehousing. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 472. Same as CSC 572.

DAT 566. NoSQL Databases. 4 Hours.

Traditional data management techniques (schema-driven databases) do not meet the need to manage the varying storage techniques and technologies used for today?s data. NoSQL (Not only SQL) databases have emerged as a means of managing distributed, high-volume, complex data. This course will use a hands-on laboratory approach to explore the different types of NoSQL Databases. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 385 and CSC 472. Same as CSC 561.

DAT 568. Web Analytics. 4 Hours.

This course focuses on the algorithm and techniques for automatic discovery of useful patterns from the structure, usage, and content of web resources. Topics include: link analysis, search, social network analysis, structures data extraction, information integration, opinion mining and sentiment analysis, web usage mining, and unstructured text processing. Course Information: Prerequisites: CSC 573.

DAT 569. Data Visualization. 4 Hours.

This course is designed to help students acquire the knowledge and skills to analyze information and, more importantly, to draw conclusions from analysis. This course is not about using advanced mathematics to solve problems. It?s about learning to use computer technology, especially visualization (graphs, histograms, pie charts), to look at and understand data in a more intuitive and visual manner. Course information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 385. Same as CSC 562.

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.