Data Science, M.S.

Graduate Coordinator: Dr. Mark Lammers

The Department of Computer Science and the Department of Mathematics and Statistics offer a joint program of study leading to the Master of Science degree in Data Science. This program offers paths to mastery of data science and analytical tools necessary for understanding interpretation and manipulation of data and how it relates to academic and industry solutions.

The specific program learning goals, as defined in consultation with our initial local business advisors, are:

Demonstrated working proficiency in a general-purpose programming language.
Demonstrated working proficiency in a statistical programming language.
Ability to build appropriate statistical models to solve real-world analytics problems.
Proficient professional communication – the ability to communicate analytical information effectively that solves data interpretation problems specific to employer needs.

Admission Requirements

Application and supplemental documents must be submitted by the published deadline. A complete application consists of:

An application for graduate admission submitted on-line through the Graduate School website.
Official transcripts of all college work (undergraduate and graduate).
Three recommendations from individuals in professionally relevant fields.
Each applicant also must:
Have a strong overall academic record and have passed the following undergraduate courses or their equivalent: an introductory statistics course, a course in linear algebra, and a programming course.

Admissions decisions are based upon the examination of several factors, and where other indicators of success warrant, individuals who fall below the established criterion in one of the areas may be considered for admission. Such individuals may be required to take additional course work to remove deficiencies or required to demonstrate proficiency in certain areas.

Degree Requirements

The Master of Data Science degree requires a total of 36 credit hours of graduate work. Programming concepts including reading and writing data from and to raw text files, spreadsheets, and relational databases will be a part of all course work.

This is a 16 month, accelerated program. Students take 4 courses every semester. The semester is divided into 2 eight-week terms and students take 2 courses every eight-week term.

Curriculum

DSC 501 - Introduction to Computational Data Science I Credit Hours: 3
DSC 502 - Introduction to Computational Data Science II Credit Hours: 3
DSC 511 - Fundamentals of Statistics for Data Credit Hours: 3
DSC 512 - Linear Methods for Data Science Credit Hours: 3
DSC 521 - Machine Learning I Credit Hours: 3
DSC 522 - Machine Learning II Credit Hours: 3
DSC 531 - Generalized Linear Models Credit Hours: 3
DSC 532 - Categorical Data Analysis Credit Hours: 3
DSC 541 - Practicum in Data Science I Credit Hours: 1 TO 6
DSC 542 - Practicum in Data Science II Credit Hours: 3
DSC 551 - Spatial-Temporal Analysis Credit Hours: 3
DSC 592 - Special Topics Credit Hours: 3