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Apr 26, 2024
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2022-2023 Graduate Catalog [Not Current Academic Year. Consult with Your Academic Advisor for Your Catalog Year]
Engineering Data Science, MS
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Cullen College of Engineering > Engineering Data Science, MS
Data science is poised to play a vital role in research and innovation in the 21st century. Google, Facebook, Amazon, and Youtube are just some prominent examples which highlight the increasing impact of data science in our day-to-day life. Engineering Data Science is a broad field that encompasses predictive modeling and data-driven design of engineering systems. Applications range from health sciences and environmental sciences to materials science, manufacturing, autonomous cars, image processing, and cybersecurity. The objective of the Master of Science program in Engineering Data Science is to provide students with a basic grounding in data science methods and tools and to enable them to put to work this knowledge in a specific branch of engineering. The program will prepare students for a variety of jobs in a market that is strongly under-supplied with data-science-savvy workers.
For more information, please visit Engineering Data Science.
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Admission Requirements
- A bachelor’s degree in engineering or engineering-related field is required in order to apply to the Engineering Data Science program.
- The Cullen College of Engineering requires a minimum of 3.0/4.0 G.P.A. over the last 60 semester hours or 90-quarter hours for admission to the graduate programs.
- Students who transfer from another graduate program must have at least a 3.0/4.0 G.P.A. on all graduate work completed.
- Applicants must submit a complete graduate application including the non-refundable application fee ($25 domestic applicants/$75 international applicants).
- Applicants must submit official GRE scores taken in the last five years.
- International students must submit additional documentation and demonstrate English language proficiency. A minimum score of 6.5 on the IELTS or 79 on the internet-based TOEFL examination is required.
- Three letters of recommendation attesting to the student’s capacity to perform in the classroom must be submitted.
- Applicants must include a statement of purpose that is consistent with the areas of interest.
For more information, please visit the Engineering Data Science program admissions website.
Degree Requirements
Credit hours required for this degree: 30.0
This degree has two options: non-thesis and thesis.
MS degree (non-thesis):
This degree program consists of a minimum of 30 credit hours of graduate course work distributed as follows:
Core courses: 9 credit hours (mandatory)
- EDS 6333 - Probability and Statistics Credit Hours: 3.0 or
- INDE 6333 - Probability and Statistics for Engineers Credit Hours: 3.0
- EDS 6340 - Introduction to Data Science Credit Hours: 3.0 or
- CIVE 6358 - Deep Learning for Engineers Credit Hours: 3.0 or
- MECE 6397 - Selected Topic Credit Hours: 3.0
Selected Topic(s):
- Learning Meets Systems and Controls
- EDS 6342 - Introduction to Machine Learning Credit Hours: 3.0 or
- COSC 6342 - Machine Learning Credit Hours: 3.0 or
- MECE 6397 - Selected Topic Credit Hours: 3.0 or
Selected Topic(s):
- Introduction to Machine Learning
- ECE 6397 - Selected Topic Credit Hours: 3.0
Selected Topic(s):
- Machine Learning and Computer Vision
Prescribed Electives: 9 credit hours (choose any 3 of the following)
- ECE 6342 - Digital Signal Processing Credit Hours: 3.0
- INDE 6372 - Advanced Linear Optimization Credit Hours: 3.0
- MATH 5386 - Regression and Linear Models Credit Hours: 3.0
- COSC 6340 - Database Systems Credit Hours: 3.0
- COSC 6336 - Natural Language Processing Credit Hours: 3.0
- ECE 6397 - Selected Topics Credit Hours: 3.0
Selected Topic(s):
- Signal Processing and Networking for Big Data Applications
- INDE 7397 - Selected Topics Credit Hours: 3.0
Selected Topic(s):
- ECE 6364 - Digital Image Processing Credit Hours: 3.0 or
- COSC 6380 - Digital Image Processing Credit Hours: 3.0
- EDS 6344 - AI for Engineers Credit Hours: 3.0 or
- COSC 6368 - Artificial Intelligence Credit Hours: 3.0
- EDS 6346: Data Mining for Engineers Credit Hours: 3.0 or
- COSC 6335 - Data Mining Credit Hours: 3.0
- EDS 6348 - Introduction to Cloud Computing Credit Hours: 3.0 or
- COSC 6376 - Cloud Computing Credit Hours: 3.0
- COSC 6339 - Big Data Analytics Credit Hours: 3.0 or
- INDE 7397 - Selected Topics Credit Hours: 3.0 or
Selected Topic(s):
- PETR 6397 - Selected Topics Credit Hours: 3.0
Selected Topic(s):
Electives: 12 credit hours (Choose any 4 of the following)
MS degree (with thesis):
This degree program consists of a minimum of 21 credit hours of graduate course work described in the non-thesis option and 9 credit hours of research/thesis work. The Master’s thesis consists of 9 credit hours and is equivalent to 3 elective courses. These 9 credit hours are to be distributed as 3 research credit hours (EDS 6398) and 6 thesis credit hours (EDS 6399 and EDS 7399) which may be taken over two or three semesters.
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