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. The singularity which will facilitate the transition of our modern society to a science fiction-esque one is on the cusp of being realized due to the so-called data revolution, especially in the field of engineering.
Engineering Data Science and AI 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 demand for graduates with a data science background is already high and is growing rapidly across a wide range of industries worldwide. Houston, being the energy capital of the world as well as the home of a thriving healthcare industry, is also seeing a persistent demand for a workforce well-trained in data science. To provide state-of-the-art training for a data-centric workforce, the Cullen College of Engineering offers a Master of Science in Engineering Data Science and AI.
Program
The Master of Science in Engineering Data Science and AI at the University of Houston is a 10 course graduate curriculum program with both non-thesis and thesis options.
A four-year bachelor’s degree in engineering or engineering related fields, or computer science and data science and statistics is required in order to apply for the Engineering Data Science and AI program.
The degree plan consists of courses in three primary categories. These courses may be available online and face-to-face in a classroom setting. For fully online options, please review the UH Extend section of our webpage.
The Master of Science in Engineering Data Science and AI is a Science, Technology, Engineering ,and Mathematics (STEM) degree. There is a STEM OPT extension which is a 24-month period of temporary training for F-1 visa students in an approved STEM field.
Application
For information on admission requirements and application process please click here.
| Application Deadlines | International | Domestic |
|---|---|---|
| Fall Semester | March 15 (*Priority) May 15 (Regular) |
March 15 (*Priority) May 15 (Regular) |
| Spring Semester | September 15 | September 15 |
*** This program does not offer summer intake ***
Degree Plan
(30 Credit Hours Requirement)
MS in Engineering Data Science and AI program requires 30 credit hours (10 courses). We offer both thesis and non-thesis options.
Overview of Master of Science in Engineering Data Science and AI
| Graduate Seminar | 0 credit | AI ethics, policies, industry experience |
| Core Courses | 12 credits | Must take 4 core courses |
| Prescribed Electives | 12 credits | Must take 4 courses from the prescribed electives |
| Specialization | 6 credits | Must take 2 electives from a specific track |
| Total | 30 credits |
Non-Thesis Track
Must take courses as described above Thesis Track
Research and Thesis (9 credits total) replace 1 prescribed elective and 2 specialization courses |
*For students admitted in the Fall 2025 semester, onwards. If admitted prior to Fall 2025, contact your academic advisor to ensure compliance with the previous EDS degree plan.
Core Courses
12 Credit Hours/ 4 Core Courses
(for both thesis and non-thesis options)
| Course Code | Course Name | Credit Hours |
|---|---|---|
| EDS 6333 | Probability and Statistics | 3 |
| EDS 6340 | Introduction to Data Science | 3 |
| EDS 6342 | Introduction to Machine Learning | 3 |
| ELET 6303 | Applied Neural Networks | 3 |
| EDS 6011 | Graduate Seminar | 0 |
Note: Students in their first semester of the degree should enroll in three of the core courses. Students must finish the core requirements in their second semester.
Prescribed Elective Courses
Non-thesis option: 12 Credit Hours/ any 4 courses of the following
Thesis option: 9 credit hours / any 3 courses of the following
| Course Code | Course Name | Credit Hours |
|---|---|---|
| INDE 7397 or PETR 6397 |
Big Data and Analytics or Big Data Analytics |
3 |
| CIS 6397 | Text Mining | 3 |
| CIVE 6308 | Deep Learning for Engineers | 3 |
| ECE 6342 | Digital Signal Processing | 3 |
| ECE 6360 | Parallel Algorithms for GPUs and Heterogeneous Systems | 3 |
| ECE 6364 | Digital Image Processing | 3 |
| ECE 6397 | Signal Processing and Networking for Big Data Applications | 3 |
| EDS 6344 | AI for Engineers | 3 |
| EDS 6345 | Information Visualization | 3 |
| EDS 6346 | Data Mining for Engineers | 3 |
| EDS 6348 | Introduction to Cloud Computing | 3 |
| EDS 6352 | Natural Language Processing | 3 |
| EDS 6364 | Digital Image Processing | 3 |
| INDE 6334 | Predictive Data Analytics | 3 |
| INDE 6360 | Engineering Analytics | 3 |
| INDE 6372 | Advanced Linear Optimization | 3 |
| INDE 7397 | Engineering Analytics | 3 |
Specialization Elective Courses based on Tracks
Students must take any 2 electives within a track as specified below:
- Manufacturing Track
| Course Code | Course Name | Credit Hours |
|---|---|---|
| INDE 6334 | Predictive Data Analytics | 3 |
| INDE 6361 | Production Planning and Control | 3 |
| INDE 6370 | Digital Simulation | 3 |
| INDE 6383 | Engineering Design and Prototyping | 3 |
- Cybersecurity Track
| Course Code | Course Name | Credit Hours |
|---|---|---|
| CIS 6321 | Introduction to Cybersecurity | 3 |
| CIS 6323 | Cryptography and Cybersecurity | 3 |
| CIS 6337 | Digital Forensic | 3 |
| CIS 6357 | Control Systems Security | 3 |
| CIS 6397 | Data Science for Cybersecurity | 3 |
- Health Track
| Course Code | Course Name | Credit Hours |
|---|---|---|
| BIOE 6305 | Brain Machine Interfacing | 3 |
| BIOE 6309 | Neural Interfaces | 3 |
| BIOE 6345 | Biomedical Informatics | 3 |
| BIOE 6346 | Advanced Medical Imaging | 3 |
| ELET 6303 | Health Analytics and Visualization | 3 |
| ELET 6350 | Computational Health Informatics | 3 |
| ELET 6351 | Biomedical Data Mining | 3 |
- Robotics Track
| Course Code | Course Name | Credit Hours |
|---|---|---|
| ECE 6311 | Introduction to Robotics | 3 |
| MECE 6379 | Computer Methods in Mechanical Design | 3 |
| MECE 6397 | Data Analysis Methods | 3 |
| MECE 6397 | Learning Meets Controls | 3 |
| MECE 6666 | Machine Learning | 3 |
- General Track
| Course Code | Course Name | Credit Hours |
|---|---|---|
| BIOE 6301 | Statistical Methods in Biomedical Engineering | 3 |
| BIOE 6305 | Brain Machine Interfacing | 3 |
| BIOE 6306 | Advanced Artificial Neural Networks | 3 |
| BIOE 6309 | Neural Interfaces | 3 |
| BIOE 6340 | Quantitative Systems Biology & Disease | 3 |
| BIOE 6342 | Biomedical Signal Processing | 3 |
| BIOE 6345 | Biomedical Informatics | 3 |
| BIOE 6346 | Advanced Medical Imaging | 3 |
| BIOE 6347 | Introduction to Optical Sensing and Biophotonics | 3 |
| CHEE 6367 | Advanced Proc Control | 3 |
| CIS 6397 | Python for Data Analytics | 3 |
| CIVE 6380 | Introduction to Geomatics and Geosensing | 3 |
| CIVE 6382 | Lidar Systems and Applications | 3 |
| CIVE 6393 | Geostatistics | 3 |
| CNST 6308 | Data Analytics for Construction Management | 3 |
| ECE 6325 | State-Space Control Systems | 3 |
| ECE 6333 | Signal Detection and Estimation Theory | 3 |
| ECE 6376 | Digital Pattern Recognition | 3 |
| ECE 6397 | Sparse Representations in Signal Processing | 3 |
| ECE 6397 | GPU Programming | 3 |
| ECE 6397 | High Performance Computing | 3 |
| EDS 6343 | Database Management Tools | 3 |
| ELET 6350 | Overview of Computational Health Informatics | 3 |
| ELET 6353 | Applied Statistics for Technology | 3 |
| ELET 6356 | Health Analytics and Visualization | 3 |
| IEEM 6360 | Data Analytics for Engineering Managers | 3 |
| INDE 6336 | Reliability Engineering | 3 |
| INDE 6363 | Statistical Process Control | 3 |
| INDE 6370 | Operation Research-Digital Simulation | 3 |
| INDE 7340 | Integer Programming | 3 |
| INDE 7342 | Nonlinear Optimization | 3 |
| MECE 6379 | Computer Methods in Mechanical Design | 3 |
| MECE 6397 | Data Analysis Methods | 3 |
| MECE 6397 | Learning Meets Controls | 3 |
| MECE 6666 | Machine Learning | 3 |
Thesis Option
(9 Credit Hours: research / thesis work)
| Course Code | Course Name | Credit Hours |
|---|---|---|
| EDS 6398 | Research Credit Hours | 3 |
| EDS 6399 | Thesis Credit Hours | 3 |
| EDS 7399 | Thesis Credit Hours | 3 |
Note:
The research/thesis credit hours for the thesis option may be taken over two or three semesters. The thesis examination committee must be approved by the Program Director prior to the defense date. The committee must consist of at least three tenure-track faculty members with at least two committee members from within the College of Engineering.
To learn more about the thesis option or if you have a MS advisor and wish to add the thesis option to your degree plan, please contact the academic advisor at egrhpc [at] uh.edu (egrhpc[at]uh[dot]edu).
The 9 research and thesis credits together will replace 2 specialization electives and 1 prescribed elective.
Academic Requirements
Students must have an overall GPA of 3.0 or higher in order to graduate with a MS degree in Engineering Data Science and AI.
Each student should assume responsibility for being familiar with the academic program requirements as stated in the current catalogs of the college, university and this website.
- For further information on academic requirements, please review the UH Graduate Catalog
- For further information student rules and regulations, please review the UH Student Handbook
Note: Students must receive a grade of C- and above to pass a course. If a student receives a grade of D+ or below, that course will not be counted towards the completion of the degree plan. However, the grade will always be counted in the calculation of the cumulative GPA.
TUITION AND COST
The MS in Engineering Data Science and AI is a 30 credit hours (10 courses) program. Students with full-time enrollment typically complete the program in a year and a half to two years. Here is the link to the Graduate Tuition Calculator which will give you an estimate of the costs.