Data Science, MSDS

Program Description

The MSDS program develops knowledge and skills across the disciplines of computer science and statistics. Graduates will understand the theory and design of statistical methods and models, as well as computing systems and algorithms, empowering them with the ability to make informed decisions in their exploration and testing of data. They will devise methods, systems, and architectures for information discovery, and have the foundations to learn and understand state-of-the-art developments in this rapidly evolving field.

Admission Requirements

Students must have a Bachelor of Science or Arts in any degree from a regionally accredited academic institution. The transcripts of the student must convey a 3.0 GPA average across two semesters of calculus-based mathematics courses, one semester of programming, one semester of statistics at any level, and one semester in applied linear algebra. An overall undergraduate GPA of 3.0 is also required for regular graduate status. Students may be admitted conditionally if they have an undergraduate grade point average of 2.7 or above or are lacking two semesters of calculus-based mathematics, one semester of programming, one semester of statistics, or one semester of applied linear algebra. Students may also replace missing prerequisite courses or demonstrate competency in the face of a low GPA, with a verified certificate of completions in calculus, programming, statistics, linear algebra, or foundations of data science from a massively open online course hosted by Coursera, EdX, or Udacity. Students conditionally admitted by lack of background courses will be required to complete some additional course work beyond the requirements of the MSDS degree program.

Learning Outcomes

Students graduating from this program will have the ability:

To process and analyze data effectively
Develop models to represent the data and understand its nature
Gain insight into the data
Utilize a variety of tools to analyze data including machine learning and visualization

Core (16 Credit Hours)

DS 7730 - Fundamentals of Data Science Credit Hour(s): 3
STT 6660 - Statistical Methods I Credit Hour(s): 4
CS 6700 - Database Management Systems Credit Hour(s): 3
CS 6840 - Intro Machine Learning Credit Hour(s): 3
CEG 7560 - Visualization & Image Processing for Cyber Security Credit Hour(s): 3

Electives (5-8 Credit Hours)

CEG 6360 - Distributed Systems & Cloud Computing Credit Hour(s): 3
CEG 7370 - Distributed Computing Credit Hour(s): 3
CEG 7380 - Cloud Computing Credit Hour(s): 3
CS 6270 - Optimization Techniques Credit Hour(s): 3
CS 6280 - Combinatorics and Graph Theory Credit Hour(s): 4
CS 6370 - Parallel Programming for Many-Core GPUs Credit Hour(s): 3
CS 6710 - Introduction to Data Mining Credit Hour(s): 3
CS 6850 - Foundations of Artificial Intelligence Credit Hour(s): 3
CS 7200 - Algorithm Design and Analysis Credit Hour(s): 3
CS 7700 - Advanced Database Systems Credit Hour(s): 3
CS 7720 - Advanced Data Mining Credit Hour(s): 3
CS 7800 - Information Retrieval Credit Hour(s): 3
CS 7810 - Metadata Representation Languages Credit Hour(s): 3
CS 7830 - Machine Learning Credit Hour(s): 3
STT 6110 - Applied Time Series Credit Hour(s): 3
STT 6210 - Sampling Design Credit Hour(s): 3
STT 6610 - Theory of Statistics I Credit Hour(s): 4
STT 6620 - Theory of Statistics II Credit Hour(s): 4
STT 6670 - Statistical Methods II Credit Hour(s): 4
STT 7020 - Applied Stochastic Processes Credit Hour(s): 3
STT 7440 - Applied Multivariate Analysis Credit Hour(s): 3
STT 7670 - Applied Regression Analysis Credit Hour(s): 3

Project Option (6 Credit Hours)

DS 7960 - MSDS Capstone Project Credit Hour(s): 1 to 6

Thesis Option (9 Credit Hours)

DS 7950 - Master’s Thesis Research in Data Science Credit Hour(s): 1 to 6

Co-op or Internship (6 Credit Hours)

DS 7960 - MSDS Capstone Project Credit Hour(s): 1 to 6