MS in Data Science

Program Overview: MS in Data Science


The Data Science program, jointly offered by Computer Science in Engineering and Mathematics in Arts & Sciences, prepares students for leadership positions in data analytics, information management, and knowledge engineering. Upon completing the program, graduates will have skills in computer programming, statistics, data mining, machine learning, data analysis and visualization that enable solving challenging problems involving large, diverse data sets from different application domains.

The Master of Science in Data Science will provide graduate students with advanced education and training in the rapidly emerging fields of data analytics and discovery informatics, which integrates mathematics and computer science for the quantification and manipulation of information from a cognate area of application (e.g., science, engineering, business, sociology, healthcare, planning). Emphasis is placed on merging strong foundations in information theory, mathematics and computer science with current methodologies and tools to enable data-driven discovery, problem solving, and decision-making.

This program is designed for professionals and organizational leaders who want to take on greater IT responsibilities and for people who want to transition into a career that uses computer information science to support decision making. The purpose of the program is to prepare students for employment in professional fields that require data analysis and representation, and a flexible, broad understanding of informatics. This program will appeal to students who want to learn technological and analysis tools used by leading science, engineering, business, academic, government and social organizations. Further, this program is designed to accommodate individuals with career or undergraduate degree in business, engineering, computer science, physical/life/social sciences, mathematics, liberal arts and education who desire to enhance their data analytics and information science skills and credentials.

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


CIS 452/552

Database Design


DSC 520/EAS520

Computational Methods


DSC 530

Data Visualization Workshop


DSC 550

Data Science Practicum






SubTotal # Core Credits Required



Elective Course Choices (Total courses required = 5) 


Elective in application domain



Elective in application domain



Elective in application domain



Elective in application domain



Elective in application domain



SubTotal # Elective Credits Required


Curriculum Summary

Total number of courses required for the degree


Total credit hours required for degree



Program Steering Committee

Dr. Ramprasad Balasubramanian (COE/CIS)
Dr. Paul Bergstein (CIS)
Dr. Gary Davis (MTH) - Chair
Dr. Sigal Gottlieb (MTH)
Dr. David Koop (CIS)
Dr. Donghui Yan (MTH) 

MS in Data Science Course Summary

Required courses by discipline


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.

Computer Science

CIS 452 - Database Systems (3 credits)

Prerequisites: CIS 280

Use of DBMS software in the development of an information system.  Overview of the ANSI/SPARC Study Group on Database Management Systems model.  Relational database model techniques.  Emphasis on user views necessary to support data management and retrieval.


CIS 552 Database Design (3 credits)

Prerequisite: CIS 452 or equivalent, or permission of instructor The relational, hierarchical, and network approaches to database systems, including relational algebra and calculus, data dependencies, normal forms, data semantics, query optimization, and concurrency control on distributed database systems.

Data Science Courses 

DSC 520 Computational Methods (3 credits)

Prerequisites: Approval of instructor and student’s graduate committee

Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.

DSC 530 Data Visualization (3 credits)

Prerequisites: DSC/EAS 520

Project-based course on advanced data visualization techniques. Topics may include: scalable visualization methods, multi-dimensional data analysis, network visualization, geospatial visualization, and interactive visualization. Ethical issues in data science.

DSC 550 Data Science Practicum (3 credits)

Prerequisite: completed 18 credit hours of graduate coursework in data science major

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted. 

Technical Elective Courses (examples):

MTH 463 - Math Modeling (3 credits)

Selected topics from the areas of linear programming, dynamic programming, Markov chains and game theory.  Mathematical model building will be developed through the use of numerous case studies from the natural and social sciences, e.g., ecological models, network models, scheduling models, urban structure, traffic flow, growth, etc.

MTH 464/564 Simulations (3 credits)

Re-written description: Deterministic and nondeterministic simulation.  Random number generators, Monte Carlo techniques, discrete simulation techniques and simulation computer languages (e.g. GPSS, SIMSCRIPT) are studied.  Standard Simulations Models, such as the national economy model, inventory control, banking, blackjack, etc., will also be covered.

MTH 473/573 Numerical Linear Algebra (3 credits)

Prerequisites: MTH 353, 361; or permission of instructor

An introduction to numerical linear algebra. Numerical linear algebra is fundamental to all areas of computational mathematics. This course will cover direct numerical methods for solving linear systems and linear least squares problems, stability and conditioning, computational methods for finding eigenvalues and eigenvectors, and iterative methods for both linear systems and eigenvalue problems.

MTH 474/574 Numerical Optimization (3 credits)

Prerequisites: MTH 353, 361; or permission of instructor
An introduction to constrained and unconstrained optimization. Numerical optimization is an essential tool in a wide variety of applications. The course covers fundamental topics in unconstrained optimization and also methods for solving linear and nonlinear constrained optimization problems.

CIS 430 – Data Mining and Knowledge Discovery (3 credits)

Prerequisites: CIS 360

Designed to provide students with a solid background in data mining and knowledge discovery concepts, tools, and methodology, as well as their applicability to real-world problems.  A variety of data mining techniques will be explored including memory-based reasoning, cluster detection, classification, neural networks, and finding understandable knowledge in large sets of real world examples.  Some related topics such as web and multimedia mining will be discussed. Students will gain hands-on experience in data mining techniques using various data mining software packages and tools.

CIS 431 - Human and Computer Interaction (4 credits)

Prerequisites: CIS 362 or permission of instructor

Theory and principles for constructing usable software systems.  Cognitive and effective aspects of users.  The impact of user characteristics on design decisions.  The construction and evaluation of the user interface.  Sensory and perceptual aspects of interfaces, task structure, input modalities, screen layout, and user documentation.  Individual concerns for systems such as personal productivity tools, real-time control systems, instructional software, and games.

CIS 454 - Computer Graphics (3 credits)

Prerequisites: At least junior CIS standing

Graphics devices.  Two dimensional and three dimensional image representations and transformations.  Graphics systems software architecture; graphics standards; packages.

CIS 455 - Bioinformatics (3 credits)

Prerequisites: CIS 360 and CIS 361 or permission of instructor

Introduction to the field of bioinformatics.  This course addresses the analysis of information present in biological systems.  This course presents an overview of the applications of computing technologies such as: analysis of protein sequence, pattern matching, biomodeling and simulation, and biological data visualization.  It also provides algorithms and methods on a selection of computational problems.  Hands on experience with tools and data.

CIS 467 - Image Analysis and Processing (3 credits)

Prerequisites: CIS 360

Fundamentals in image analysis and processing. Topics in image processing such as display and filtering, image restoration, segmentation, compression of image information,  warping, morphological processing of images, wavelets, multi-resolution imaging  and unitary transforms are discussed.

CIS 490 Machine Learning (3 credits)

Prerequisites: CIS 360

Constructing computer programs that automatically improve with experience is the main task of machine learning.  The key algorithms in the area are presented.  Learning concepts as decision trees, artifical neural networks and Bayesian approach are discussed. the standard iterative dichotomizer (ID3) is presented, the issues of overfitting, attribute selection and handling missing data are discussed.  Neural nets are discussed in detail, examples of supervised and unsupervised learning are presented.  Instance-based learning, i.e. k-nearest neighbor learning, case-based reasoning are introduced.  Genetic algorithms are discussed on introductory level.

CIS 554 - Advanced Computer Graphics (3 credits)

Prerequisites: CIS 454 or equivalent, or permission of instructor

Three-dimensional graphics including: color, shading, shadowing and texture, hidden surface algorithms. An extensive project will be assigned, including documentation and presentation.

CIS 555 - Advanced Bioinformatics (3 credits)

Advanced coverage of computational approaches used in bioinformatics. The course focuses on algorithmic challenges in analyzing molecular sequences, structures, and functions. It covers the following topics: Sequence comparison, assembly and annotation. Phylogenetic analysis. RNA secondary structure. Protein structure comparison, prediction, and docking. Microarrays, clustering, and classification. Genome, Hapmap, SNPs, and phenotypes. Proteomics and protein identification. Determining protein function and metabolic pathways.

CIS 563 MultiAgent Systems (3 credits)

Introduction to multiagent systems and distributed artificial intelligence. The course examines the issues that arise when groups or societies of autonomous agents interact to solve interrelated problems. Topics include defining multiagent systems and their characteristics, reasoning about agents’ knowledge and beliefs, distributed problem solving and planning, coordination and negotiation, the organization and control of complex, distributed multiagent systems, learning in multiagent systems, and applications in the following domains: internet information gathering, electronic commerce, virtual markets, workflow management, distributed sensing network, distributed planning and resource allocation.

CIS 581 - Design and Verification of Information Systems (3 credits)

Prerequisites: CIS 580 or equivalent, or permission of instructor

Sound design methodologies and technologies in development and maintenance of information systems/business systems with special emphasis on workflow management systems. An applied course that emphasizes the formal approach, this course also addresses issues of adaptability and flexibility of information systems and their evaluation. The course supports concurrent execution of information systems during the design stage and adopts and applies various forms of Petri nets.

CIS 585 - Image Processing and Machine Vision (3 credits)

Prerequisites: Graduate standing and permission of the instructor

Foundations of image processing and machine vision. Students apply and evaluate topics such as edge detection, segmentation, shape representation, and object recognition. Stereo vision and motion analysis will be covered in detail including calibration, range images, change detection, motion correspondence, and 2-D and 3-D tracking. Important research papers will be discussed in class.

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.

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. 

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.