Program Overview

Introduction to the Master of Science in Data Analytics

The Master of Science in Data Analytics is an interdisciplinary program that provides students with a comprehensive understanding of data analytics, covering both the foundational mathematical knowledge of data science and the computational methods and tools for preprocessing, interpreting, analyzing, representing, and visualizing data sets. The degree is offered in both on-campus and online formats.

Program Overview

The program aims to equip students with the skills and knowledge necessary to apply data analytics techniques in real-world organizational settings. Applications are accepted each spring and fall semester, and the program may accept new students in the summer semester under conditional admission.

Admission Requirements

• Students must have completed a course in data structures and algorithms to be considered for admission to the master's degree program.
• International students residing outside of the U.S. are not eligible for admission to the online program.
• Students must maintain a minimum GPA of 3.0 on a scale of 4.0 to be fully admitted.

Degree Requirements

Students must complete 28 required credit hours and 8 elective credit hours to earn the Data Analytics degree. The required courses include:

• CSC 472: Introduction to Database Systems
• CSC 532: Introduction to Machine Learning
• CSC 534: Big Data Analytics
• CSC 535: Deep Learning
• DAT 502: Introduction to Statistical Computation
• DAT 530: Advanced Statistical Methods
• DAT 554: Data Analytics Capstone

Elective Courses

Students must choose two elective courses from the following options:

• CSC 533: Data Mining
• CSC 561: NoSQL Databases
• CSC 562: Data Visualization
• CSC 570: Advanced Topics in Computer Systems (Containerization/BigData or A.I. for Cybersecurity)
• CSC 572: Advanced Database Concepts
• DAT 444: Operations Research Methods
• MAT 444: Operations Research Methods

Capstone Project

The capstone project requires students to apply the knowledge and skills learned throughout the curriculum to a real-world organizational setting. Students must provide a well-written report and an oral presentation to effectively communicate their findings.

Advising and Grading Policy

• Students are assigned an academic advisor upon acceptance into the program.
• Students must earn a grade of B- or better in all courses that apply toward the degree and maintain a cumulative 3.0 grade point average to graduate.
• Graduate students who do not maintain a 3.0 grade point average will be placed on academic probation.

Transfer Courses

Students are allowed to transfer a maximum of eight graduate semester hours with a grade of B or better, subject to evaluation and approval on a case-by-case basis. Transfer students must take a minimum of 28 credit hours of Data Analytics core and elective course work at the university.