MS in Data Science

The Data Science program, offered jointly by the department of Computer & Information Science in the College of Engineering and the department of Mathematics in the College of Arts & Sciences, provides advanced education to prepare students for professional positions in data analysis, informatics, data-driven decision-making, and related fields.

You'll gain a strong foundation in information theory, mathematics and computer science with current methodologies and tools to enable data-driven discovery, problem solving, and decision-making.

Prospective students: learn more about the MS in Data Science

Program goals

  1. Meet the growing regional and national demand for high-level information systems/science skills;
  2. Provide a path for individuals from diverse fields to rapidly transition to data science career paths;
  3. Enable established information technology and computing professionals to upgrade their technical management and development skills;
  4. Prepare graduates to apply data science techniques for knowledge discovery and dissemination to assist researchers or decision makers in achieving organizational objectives;
  5. Establish stronger ties to alumni to enhance opportunities for continued learning and leadership;
  6. Create innovators, entrepreneurs, business professionals who will lead the development of next generation information systems.

Learning outcomes

At the time of graduation, students will:

  • be able apply contemporary techniques for managing, mining, and analyzing big data across multiple disciplines;
  • be able to use computation and computational thinking to gain new knowledge and to solve real-world problems of high complexity;
  • have the ability to communicate their ideas and findings persuasively in written, oral and visual form and to work in a diverse team environment;
  • apply advanced knowledge of computing and information systems applications to areas such as networking, database, security and privacy, and Web technologies;
  • be better prepared for career advancement in all areas of information science and technology;
  • be committed to continuous learning about emerging and innovative methods, technologies, and new ideas, and be able to bring them to bear to help others; 
  • have an appreciation for the professional, societal and ethical considerations of data collection and use

Graduate program curriculum outline

Major Required (Core) Courses (Total # of courses required = 5)

Course Number

Course Title

Credit Hours

MTH 522

Mathematical Statistics

3

CIS 452/552

Database Design

3

DSC 520/EAS520

Computational Methods

3

DSC 530

Data Visualization Workshop

3

DSC 550

Data Science Practicum

3

 

SubTotal # Core Credits Required

15

Elective Course Choices (Total courses required = 5)

TBD

Elective in application domain

3

TBD

Elective in application domain

3

TBD

Elective in application domain

3

TBD

Elective in application domain

3

TBD

Elective in application domain

3

 

SubTotal # Elective Credits Required

15

Curriculum Summary

Total number of courses required for the degree

10

Total credit hours required for degree

30

MS in Data Science Course Summary

Data Science Core Courses

MTH 522 Mathematical Statistics (3 credits)

Prerequisite: Graduate standing and one course in statistics

Introduction to mathematical concepts and methods essential for multivariate statistical analysis. Topics include basic matrix algebra, eigenvalues and eigenvector, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and applications in multivariate statistical analysis.

CIS 452 - Database Systems (
3 credits)  OR CIS 552 - Database Design (3 credits)
DSC 520 - Computational Methods (3 credits)
DSC 530 Data Visualization (3 credits)
DSC 550 Data Science Practicum (3 credits)

Data Science Technical Electives 

MTH 463 - Math Modeling (3 credits)
MTH 440/540 - Mathematical and Computational Consulting (3 credits)
MTH 464/564 Simulations (3 credits)
MTH 473/573 Numerical Linear Algebra (3 credits)
MTH 474/574 Numerical Optimization (3 credits)
CIS 430 – Data Mining and Knowledge Discovery (3 credits)
CIS 431 - Human and Computer Interaction (4 credits)
CIS 454 - Computer Graphics (3 credits)
CIS 455 - Bioinformatics (3 credits)
CIS 467 Image Analysis and Processing (3 credits)
CIS 490 Machine Learning (3 credits)
CIS 550 - Advanced Machine Learning (3 credits)
CIS 554 - Advanced Computer Graphics (3 credits)
CIS 555 - Advanced Bioinformatics (3 credits)
CIS 563 MultiAgent Systems (3 credits)
CIS 569 -Visual Analytics (3 credits)
CIS 581 - Design and Verification of Information Systems (3 credits)
CIS 585 - Image Processing and Machine Vision (3 credits)
CIS 602-01 - Pattern Analysis (3 credits)
      Note: CIS 602 is a special topics course. Only Pattern Analysis is approved as an elective.

MIS 432 - Business Data Systems (3 credits)

Prerequisites: At least junior standing; MIS 322; for business majors only

Students demonstrate their mastery of the analysis and design processes acquired in earlier courses by designing and constructing databases to meet the information needs of users. Topics covered include data models and modeling techniques, information engineering, database design and implementation, data quality and security, and the client/server environment.

MIS 433 Advanced Database E-Business Applications Development (3 credits)

Prerequisites: MIS 432 and senior standing; for business majors only

Focuses on advanced database techniques and issues for e-commerce applications including web-based database application development and data warehousing design. The course provides extensive opportunities for applying and extending database concepts learned in BIS 432 (Business Data Systems) through hands-on use of web-based database applications development tools that are commonly used in the business field. Students complete a major project. 

MIS 670 - Managing Information (3 credits)

Managing information by understanding, designing and controlling the information processing activities of an organization. The course explores how firms gather, represent, process and distribute information and knowledge to employees and customers. A sample of the topics covered in the course includes: gathering information, or business intelligence; storing information, or information architectures; information/data modeling; processing information, or process modeling; knowledge management; data mining; and distributing information, or e-commerce brokerage and disintermediation.

MIS 681 - Business Intelligence and Knowledge Management (3 credits)

MKT 671 Marketing Research (3 credits)

Successful marketing by collecting, analyzing and interpreting information. This course offers an understanding of the different marketing information needs of the organization. The conception, planning and performance of marketing research projects are discussed as an objective basis for marketing strategies. Topics include definition of research objectives, data sources, research design, interpretation of data and evaluation of research proposals and results. The course focuses on applying marketing research concepts to solving real-world problems through written and video cases, applied research exercises and experiential research development projects.

POM 500 Statistical Analysis (3 credits)

A case study approach involving the following statistical concepts: descriptive statistics, probability, sampling, probability distribution, statistical estimation, chi-square testing, analysis of variance and simple regression-correlation analysis. 

POM 681 - Business Analytics and Data Mining (3 credits)

PSY 502 Statistical Methods in Psychology (3 credits)

Prerequisite: PSY 205 or similar statistics course and graduate standing in Psychology

Advanced study of statistical methods in psychology including analysis of variance and regression. Previous experience with the SPSS statistical program is suggested. This course is intended for those who have completed an undergraduate statistics course.