Data Science and Analytics (M.S.)
Admission Requirements
- A bachelor’s degree from an accredited college or university with a minimum cumulative GPA of 2.5 (4.0 scale).
- 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 - 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
| Code | Title | Credit Hours |
|---|---|---|
| Required Courses (18 credit hours) | ||
| CIS 512 | INTRODUCTION TO DATA SCIENCE AND ANALYTICS | 3 |
| CIS 600 | MACHINE LEARNING FOR DATA SCIENCE | 3 |
| DSA 688 | EXPERIENTIAL LEARNING IN DATA SCIENCE AND ANALYTICS | 3 |
| MAT 616 | ELEMENTS OF MATHEMATICS, PROGRAMMING AND COMPUTER SCIENCE FOR DATA SCIENCE | 3 |
| One of the following "plus" courses (3 credit hours) | 3 | |
| PROJECT MANAGEMENT FOR MATH AND SCIENCE PROFESSIONALS | ||
| COMMUNICATION STRATEGIES FOR MATH AND SCIENCE PROFESSIONALS | ||
| DATA STRATEGY AND GOVERNANCE | ||
| One of the following statistics courses (3 credit hours) | 3 | |
| INTRODUCTION TO STATISTICS FOR DATA SCIENCE | ||
| BIOLOGICAL DATA ANALYSIS | ||
| 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 SCIENCE TOOLS IN ENERGY ENGINEERING | ||
| APPLIED TIME SERIES ANALYSIS IN BANKING RISK MANAGEMENT (1) | ||
| RENEWABLE DISTRIBUTED GENERATIONAND STORAGE | ||
| SMART GRID FROM SYSTEMS PERSPECTIVE | ||
| MACHINE LEARNING FOR MATERIALS SCIENCE IN CLEAN ENERGY | ||
| GEOSPATIAL PROGRAMMING | ||
| INTERACTIVE AND WEB-BASED MAPPING | ||
| DATA VISUALIZATION AND STORYTELLING | ||
| PROJECT MANAGEMENT FOR MATH AND SCIENCE PROFESSIONALS (This course cannot count for both a "plus" and elective) | ||
| COMMUNICATION STRATEGIES FOR MATH AND SCIENCE PROFESSIONALS (This course cannot count for both a "plus" and elective) | ||
| METHODS AND TECHNIQUES OF EDUCATIONAL RESEARCH | ||
| Or additional elective courses by advisement | ||
| Total Credit Hours | 30 | |
Students will:
- select and apply an appropriate statistical, mathematical or computational model for a given quandary
- acquire data from data scraping and open sources and understand the ethical and legal ramifications of data acquisition
- store, clean, organize, and manipulate real world data from multiple sources
- 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
- use the appropriate software or programming application (Python, SQL, SAS, SPSS, Excel) to manage and analyze data
- perform effectively as a member of a team to execute a project and will understand what contributes to team success
- integrate context specific information into their data manipulation allowing them the flexibility to interpret data from many different environments