Data Science and Analytics (M.S.)

Admission Requirements

  1. A bachelor’s degree from an accredited college or university with a minimum cumulative GPA of 2.5 (4.0 scale).
  2. A two- to three-page statement of intent (essay) that includes the following:
    - educational and professional objectives; and
    - an explanation of the reasons for interest in data science and analytics
  3. An interview with the program coordinator or a DSA faculty member.  The student will be contacted for an appointment after the completed application is received.

In addition, all applicants must review the Admission to a Graduate Program section in this catalog.

Program Requirements

Required Courses (18 credit hours)
CIS 512INTRODUCTION TO DATA SCIENCE AND ANALYTICS3
CIS 600MACHINE LEARNING FOR DATA SCIENCE3
MAT 616ELEMENTS OF MATHEMATICS, PROGRAMMING AND COMPUTER SCIENCE FOR DATA SCIENCE3
MAT 646INTRODUCTION TO STATISTICS FOR DATA SCIENCE3
SPF 689METHODS AND TECHNIQUES OF EDUCATIONAL RESEARCH3
XXX 690 or DSA 690 MASTER'S PROJECT3
(where XXX is the department of the student's Master's Project Adviser)
Elective Courses (12 credit hours)12
Choose four courses by advisement from the following (each course is 3 credit hours)
COMMUNICATION FOR LEADERS AND MANAGERS
DATA ANALYTICS FOR STRATEGIC COMMUNICATION
DATA ORIENTED COMPUTING AND ANALYTICS
MACHINE LEARNING MODELS IN PYTHON
DATABASES AND THE DATA SCIENCE INFORMATION LIFE CYCLE
DATA STRATEGY AND GOVERNANCE
GEOSPATIAL PROGRAMMING
INTERACTIVE AND WEB-BASED MAPPING
DATA VISUALIZATION AND STORYTELLING
PROJECT MANAGEMENT FOR MATH AND SCIENCE PROFESSIONALS
COMMUNICATION STRATEGIES FOR MATH AND SCIENCE PROFESSIONALS
Or additional elective courses by advisement
Total Credit Hours30

Students will:

  1. select and apply an appropriate statistical, mathematical or computational model for a given quandary
  2. acquire data from data scraping and open sources and understand the ethical and legal ramifications of data acquisition
  3. store, clean, organize, and manipulate real world data from multiple sources
  4. compose and present an effective oral, written report or dynamic dashboard, to a lay audience (including storytelling and data visualization) that enhances the audience’s understanding and reveals properties of the data
  5. use the appropriate software or programming application (Python, SQL, SAS, SPSS, Excel) to manage and analyze data
  6. perform effectively as a member of a team to execute a project and will understand what contributes to team success
  7. integrate context specific information into their data manipulation allowing them the flexibility to interpret data from many different environments