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Health Data Analytics Certificate

Introducing the Graduate Certificate in Health Data Analytics

This sequence of three courses provides in depth training on the latest methods for analyzing medical data, including artificial intelligence and machine learning. The certificate is targeted to those with an undergraduate degree in bioengineering or similar background, in order to build skills in a new and emerging growth area with high interest and value to industry. The credential earned has the potential to improve prospects for hiring, broaden career options, and increase salary.

This is a 3-semester course sequence of graduate level courses (3 semester credit hours per course) in our department of Bioengineering. They are the same courses our graduate students are enrolled in. Completion of three courses earns a noted on your official transcript. These courses can be used in fulfillment of the requirements for a MS in Bioengineering.

Current students in UTD graduate programs in Bioengineering are also eligible to earn the Certificate.

What courses do I need to take?

The following courses form the certificate:

BMEN 6367: Artificial Intelligence in Biomedical Engineering

This course covers the basic principles of artificial intelligence (AI) and its biological and medical applications. With recent progress in digitized data acquisition, machine learning, and computing infrastructure, AI is increasingly used in various fields of life. New breakthroughs and technologies are emerging from technology companies and research institutes at a rapid pace. Medicine is identified as one of the most promising application areas, and AI is changing the landscape of healthcare and biomedical research. This course will provide the basic principles of AI technologies, outline recent breakthroughs and their applications, and identify the challenges for further progress in medical AI systems. The course will also be featured by invited guest speakers who have real-world AI application experiences. All students should have extensive experience in computer programming and be familiar with Python programming.

BMEN 6303: Introduction to Machine Learning

Machine learning is an increasingly important tool for understanding datasets in Biomedical Engineering. The machine learning models introduced will include linear and logistic regression, classification using Naïve Bayes, Decision Trees, Support Vector Machines, and clustering. This course will use the python programming language, and requires basic programming skills not necessarily in python. Students choose a dataset for the final project which may come from their research.

BMEN 6328: Data Science in Digital Health

This project-based course will teach systems analysis and data science techniques using digital health data. The project will have students: collect data on themselves throughout the semester using a provided Ōura Ring wearable device (at no cost to the student); build a secure system to store, access, and analyze the data; and use signal processing and machine learning approaches to identify and analyze trends in collected signals. Lectures will cover a variety of topics, including systems analysis, data science, and digital health. No prior knowledge of biology or human health is required. Prerequisites: Signals and Systems (BMEN 3302 or EE 3302 or equivalent), Statistics (ENGR 3341 or equivalent).

Admission Criteria

GPA of 3.0 in a bachelor’s degree of Engineering or Computer Science

Course background: Calculus – 2 semesters, Biology – 1 semester

how to apply

For Current UTD students

Undergraduate UT Dallas students who recently graduated or current UT Dallas undergraduates who will be graduating soon may qualify to apply using the Graduate UT Dallas Quick Admit application:

Then you can apply through your UTD Student Center in Orion

For Non – UTD students

  • Visit https://utdallas.my.site.com/TX_SiteLogin?startURL= and create a sign-in using your name and email.
  • Choose the Certificate for the Level you are applying for.
  • Select Erik Jonsson School of Engineering and Computer Science
  • Select the Health Data Analytics
  • Select the Term you would like to start and finish the application!

Certificate Requirement

  • Courses must be completed within 5 years
  • Total of three courses, 9 SCH are required
  • Must earn a 3.0 or better in each course with average GPA of 3.3 for award of the Certificate

Time Frame/ How Long

The time frame for the certificate is 2-3 semesters:

  • Fall 2024
    • BMEN 6367: Artificial Intelligence in Biomedical Engineering
  • Spring 2025
    • BMEN 6303: Introduction to Machine Learning
    • BMEN 6328: Data Science in Digital Health (option 1)
  • Spring 2026
    • BMEN 6328: Data Science in Digital Health (option 2)

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