Data Science, Analytics and Engineering

Data scientists are consistently ranked among the top jobs in the USA, and there is an increasing need for all engineers to make use of data science tools like statistics, machine learning, artificial neural networks and artificial intelligence. Yet, the majority of engineering occupations require subject matter expertise beyond data science.

The MS program in data science, analytics and engineering with a concentration in electrical engineering provides an advanced education in high-demand data science and electrical engineering. A focus on probability and statistics, machine learning, data mining and data engineering is complemented by electrical engineering-specific courses to ensure breadth and depth in data science and electrical engineering.

All students must choose a concentration:

1. Computing and Decision Analysis
2. Electrical Engineering
3. Materials Science and Engineering
4. Sustainable Engineering and Built Environment
5. Bayesian Machine Learning
6. Computational Models and Data
7. Human Centered Applications
8. Mechanical and Aerospace Engineering

Application deadlines

Fall: December 31
Spring: July 31

The PhD program in data science, analytics and engineering engages students in fundamental and applied research as preparation for careers in academia, government or industry. The program’s educational objective is to develop each student’s ability to perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed for data analytics and engineering.

This degree program is a collaboration between the School of Computing and Augmented Intelligence and the School of Mathematical and Statistical Sciences (SoMSS) and provides a rigorous education with research and educational experiences that allow students to pursue careers in advanced research, teaching or state-of-the-art practice. Graduates demonstrate proficiency with existing methodology and significant accomplishment at advancing the state of the art in their chosen area of data science, analytics and engineering.

Application deadlines

Fall: January 15
Spring: September 15