Master of Science in Engineering Data Science

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 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 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.


The Master of Science in Engineering Data Science at the University of Houston is a non-thesis, 10 course graduate curriculum program. A four-year bachelor's degree in engineering or engineering related field is required in order to apply for the Engineering Data Science program. The curriculum is comprised of three primary categories. These courses are available online and face-to-face in a classroom setting.



For information on admission requirements and application process please click here.

Degree Plan

Required/Core Co​urses
Prefix and Number Course Title SCH
CIVE 6358 Deep Learning for Engineers 3
MECE 6397 Learning Meets Systems and Controls 3
or equivalent
ECE 6397 Introduction to Machine Learning 3
COSC 6397 Deep Learning 3
or equivalent
INDE 6333 Probability Statistics for Engineers 3
or equivalent


Prescribed Elective Courses: Choose Four of the Following
Prefix and Number Course Title SCH
COSC 6339 Big data analytics 3
ECE 6397 Signal processing and networking for big data applications 3
ECE 6397 Embedded systems and the internet of things 3
MATH 5386 Regression and linear models 3
MATH 6382 Probability models 3
MATH 6397 Multivariate Statistical Analysis 3
MATH 6365 Automatic learning and data mining 3
ECE 6397 Machine learning 3
COSC 6335 Data mining 3
COSC 6342 Machine learning 3
COSC 6380 Digital image processing 3
COSC 6340 Database systems 3
COSC 6368 Artificial intelligence 3
COSC 6336 Natural language processing 3
COSC 6376 Cloud computing 3
ECE 6364 Digital image processing 3
ECE 6354 Digital video 3


Elective Courses
Prefix and Number Course Title SCH
BIOE 6305 Brain Machine Interfacing 3
BIOE 6309 Neural Interfaces 3
BIOE 6340 Quantitative Systems Biology & Disease 3
BIOE 6342 Biomedical Signal Processing 3
BIOE 6346 Advanced Medical Imaging 3
BIOE 6347 Introduction to Optical Sensing and Biophotonics 3
BIOE 6345 Biomedical Informatics 3
BIOE 6305 Brain Machine Interfacing 3
CIVE 6380 Introduction to Geomatics/Geosensing 3
CIVE 6382 Lidar Systems and Applications 3
CHEE 6367 Advanced Proc Control 3
ECE 6376 Digital Pattern Recognition 3
ECE 6397 Sparse Representations in Signal Processing 3
ECE 6378 Power System Analysis 3
ECE 6342 Digital Signal Processing 3
ECE 6333 Signal Detection and Estimation Theory 3
ECE 6315 Neural Computation 3
ECE 6397 GPU Programming 3
ECE 6397 High Performance Computing 3
ECE 6325 State-Space Control Systems 3
INDE 7397 Engineering Analytics 3
INDE 6370 Operation Research-Digital Simulation 3
INDE 6336 Reliability Engineering 3
INDE 7340 Integer Programming 3
INDE 7342 Nonlinear Optimization 3
INDE 6363 Statistical Process Control 3
INDE 6372 Advanced Linear Optimization 3
MECE 6379 Computer Methods in Mechanical Design 3
MECE 6397 Data Analysis Methods 3
SUBS 6397 Guide to Engineering Data Science 3
SUBS 6397 Real-Time Data Processing 3
PETR 5397 Big Data and Analytics for Petroleum Engineers 3
PETR 6397 Application of Data Analytics to Petroleum Engineering 3
PETR 6322 Practical Aspects of Integrated Petroleum Reservoir Mgmt. 3

Spring 2022 Courses

The courses eligible for the MS program offered in the Spring of 2022 are:

Core Corses

  • CIVE 6358 - Deep Learning for Engineers
    MECE 6397 - Learning Meets Systems and Controls 
  • ECE 6397 - Introduction to Machine Learning
    COSC 6397 - Deep Learning
  • INDE 6333 - Probability Statistics for Engineers

Prescribed Electives

  • ECE 6397 - Big Data Analysis and Applications
    INDE 7397 - Big data and analytics
    COSC 6339 - Big Data Analytics
  • ECE 6364 Digital Image Processing 
    COSC 6380 - Digital Image Processing 
  • INDE 6372 - Advanced Linear Optimization


  • ECE 6381 - Sparse Representations for Signal Processing
  • BIOE 6306 - Advanced Artificial Neural Networks 
  • BIOE 6309—Neural Interfaces
  • BIOE 6340—Quantitative Systems Biology and Disease
  • BIOE 6342—Biomedical Signal Processes
  • PETR 6397 - Big data analytics
  • COSC 6373 - Computer Vision
  • CIVE 6393 - Geostatistics

Note that “or” indicates equivalent courses. If you take both, they will NOT be counted as two separate courses.

Academic Requirements

For each graduate program offered, students must have an overall G.P.A. of 3.0 or higher. Any graduate student who receives a grade of C+ or lower in four graduate courses (whether or not in repeated courses) is ineligible to receive an advanced degree at the university.

Each student assumes responsibility for being familiar with the academic program requirements as stated in the current catalogs of the college, university and this website.