Master of Science in Data Engineering

Data Engineering is an interdisciplinary and emerging field that encompasses the entire life cycle of data. This engineering discipline is concerned with acquisition, processing, security, storage, and management of data from an engineering perspective. Data engineering students will learn from experts in areas from embedded and sensor system design to privacy and security, AI and machine learning, and more. This program centers on challenges and tradeoffs in data systems design that are encountered by both working professionals as well as researchers in this critical and emerging field. The program is designed to fulfill the needs of both full-time and part-time students.

Design: Students will acquire and develop the skills required to design and implement complex systems comprised of software, hardware, and networks that support the data life cycle.

Acquisition: Students will gain the ability to design and develop methods and systems for acquiring and processing data.

Techniques: Students will learn to use and develop new techniques and modern methods that enable data systems such as data mining, machine learning, and signal processing that augment and complement conventional methods used by the data science community.

Analysis: Students will gain necessary skills to analyze data sets based on mathematics, algorithms, and data structures.


Program Overview

This 30 semester credit hour degree plan is tailored to accommodate a large variety of backgrounds in the sciences and engineering. Data Engineering is a design-oriented discipline centered around complex systems that includes areas such as: acquisition, storage, management, security, visualization, processing/analytics, analysis/science, classification/prediction, decision making, and automated intelligence.

Applicants should have experience in one or more of the following areas:

  • Introduction to SW Development
    • computer system fundamentals, introduction to programming
  • Introductory Digital Logic
    • Boolean algebra, logic gates, memory
  • Undergraduate Embedded Systems
    • introduction to assembler/C, I/O, sensors, actuators/indicators
  • Undergraduate Statistics for Engineering/Science
    • probability concepts, distributions, data sample statistics

Students are required to complete four core courses and the remaining six courses are selected from a list of elective courses. Of the required elective courses, at least three of the six courses must be at the advanced 8000 level. There are five optional "Focus Tracks": data security; data management; data engineering with intelligence and learning; data acquisition and preprocessing; and data engineering research.

Program Director

Program Director

Mitch Thornton, Ph.D., P.E.
Cecil H. Green Chair of Engineering and Professor

Program Manager

Lisa Bell