Engineering Data Science University of Houston

Overview

The University of Houston is recognised for its strong emphasis on research and innovation, particularly in engineering and data science. The Cullen College of Engineering is committed to providing cutting-edge education, ensuring that graduates are well-prepared to meet the demands of an evolving job market. The programme leverages Houston's unique position as a hub for the energy and healthcare sectors, offering students unparalleled opportunities for networking and career advancement. Data science is increasingly becoming integral to various industries, with companies like Google, Facebook, and Amazon leading the charge in utilising data for strategic decision-making. The Engineering Data Science programme at the University of Houston prepares students to harness this potential, focusing on predictive modelling and the data-driven design of engineering systems. Graduates are well-equipped to enter fields such as health sciences, environmental sciences, materials science, manufacturing, autonomous vehicles, image processing, and cybersecurity.

The tuition fees for the Master of Science in Engineering Data Science are as follows:

• International Fee: USD 1,089 per credit
• National Fee: USD 1,089 per credit
• Local Fee: USD 579 per credit

Visit the Fees and Funding section for a breakdown in your local currency.

The curriculum for the Master of Science in Engineering Data Science comprises 30 credit hours, structured into core and elective courses. The programme includes:

• Core Courses (9 Credit Hours)
  • Probability and Statistics
  • Introduction to Data Science
  • Introduction to Machine Learning
• Prescribed Elective Courses (9 Credit Hours)
  • Big Data and Analytics
  • Digital Image Processing
  • Signal Processing and Networking for Big Data Applications
  • AI for Engineers
  • Data Mining for Engineers
  • Introduction to Cloud Computing
  • Digital Signal Processing
  • Engineering Analytics
  • Information Visualization
• Elective Courses (12 Credit Hours for Non-Thesis or 3 Credit Hours for Thesis)
  • Brain Machine Interfacing
  • Advanced Artificial Neural Networks
  • Neural Interfaces
  • Quantitative Systems Biology & Disease
  • Biomedical Signal Processing
  • Advanced Medical Imaging
  • Biomedical Informatics
  • Database Management for Business Analytics
  • Geostatistics
  • Introduction to Geomatics/Geosensing
  • Lidar Systems and Applications
  • Python for Data Analytics
  • Advanced Process Control
  • Digital Pattern Recognition
  • Sparse Representations in Signal Processing
  • Stochastic Processes in Signal Processing and Data Science
  • Power System Analysis
  • Neural Computation
  • GPU Programming
  • High Performance Computing
  • State-Space Control Systems
  • Operation Research-Digital Simulation
  • Reliability Engineering
  • Statistical Process Control
  • Data Analytics for Engineering Managers
  • Computer Methods in Mechanical Design
  • Data Analysis Methods

Graduates of the Master of Science in Engineering Data Science programme are positioned for success in various industries. Alumni often find roles in leading companies within the energy and healthcare sectors, leveraging their expertise in data science to drive innovation and efficiency. Potential career paths include data analyst, machine learning engineer, and systems designer, among others, reflecting the diverse applications of their training in real-world scenarios.