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.

Program

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.

 

Application

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

Degree Plan

Credit hours required for this degree: 30.0
The MS 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 Co​urses: 9 credit hours (mandatory)
Prefix and Number Course Title SCH
EDS 6333 Probability and Statistics 3
or
INDE 6333 Probability and Statistics for Engineers 3
 
EDS 6340 Introduction to Data Science 3
or
CIVE 6358 Deep Learning for Engineers 3
or
MECE 6397 Learning Meets Systems and Controls 3
 
EDS 6342 Introduction to Machine Learning 3
or
MECE 6397 Introduction to Machine Learning 3
or
ECE 6397 Machine Learning and Computer Vision 3

 

Prescribed Electives: 9 credit hours (choose any 3 of the following)
Prefix and Number Course Title SCH
INDE 7397 Big Data and Analytics 3
or    
PETR 6397 Big Data Analytics 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 6346 Data Mining for Engineers 3
EDS 6348 Introduction to Cloud Computing 3
ECE 6342 Digital Signal Processing 3
INDE 7397 Engineering Analytics 3
INDE 6372 Advanced Linear Optimization 3

Note: Students with strong academic background and high GPA at UH may enroll in advanced data science related courses offered by departments of Computer Science or Mathematics upon approval from Program Director and the department offering the course, as well as availability of seats. 

 

Electives: 12 credit hours (Choose any 4 of the following)
Prefix and Number Course Title SCH
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 6346 Advanced Medical Imaging 3
BIOE 6347 Introduction to Optical Sensing and Biophotonics 3
BIOE 6345 Biomedical Informatics 3
BZAN 6354 Database Management for Business Analytics 3
CIVE 6393 Geostatistics 3
CIVE 6380 Introduction to Geomatics/Geosensing 3
CIVE 6382 Lidar Systems and Applications 3
CIS 6397 Python for Data Analytics 3
CHEE 6367 Advanced Proc Control 3
ECE 6376 Digital Pattern Recognition 3
ECE 6397 Sparse Representations in Signal Processing 3
ECE 6337 Stochastic Processes in Signal Processing and Data Science  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 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
IEEM 6360 Data Analytics for Engineering Managers 3
MECE 6379 Computer Methods in Mechanical Design 3
MECE 6397 Data Analysis Methods 3

 

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 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.

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.
 

Fall 2022 Courses

Core

  • NDE 6333 - Probability Stat For Engineers 
  • MECE 6397 - Machine Learning
    or
    COSC 6342 - Machine Learning

Prescribed Electives

  • INDE 6372 - Advanced Linear Optimization 
  • INDE 6360 - Engineering Analytics 
  • ECE 6337 - Stochastic Processes in Signal Processing and Data Science 
  • ECE 6342 - Digital Signal Process
  • COSC 6336 - Natural Language Processing
  • COSC 6376 - Cloud Computing
  • COSC 6380 - Digital Image Processing 

Electives

  • CIVE 6393 - Geostatistics 
  • IEEM 6360 - Data Analytics for Engr. Mgt 
  • CIS 6397 - Python for Data Analytics
  • BZAN 6354 - Database Management Tools for Business Analytics 

Please note that some more courses/sections will be offered in the fall in the Core and Prescribed electives categories. They will be confirmed in the next few weeks.

Summer 2022 Courses

  • INDE 6333 - Probablity and Statistics for Engineers  (Core)
  • BIOE 6306 - Advanced Artificial Neural Networks (Elective)
  • IEEM 6360 - Data Analytics for Engineering Managers (Elective)

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

Prescribed Electives

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

Electives

  • 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.