Health Data Science
Graduate Academic Certificate


Number of Courses: 4 | Number of Credit Hours: 12 


The healthcare landscape continues to rapidly evolve as the digitization of health progresses with the advancements in technology. Health data is being generated at a pace never witnessed before; new models of health services and health information delivery continue changing; and the available technology is making healthcare more consumer-centric. All of this means that health informatics and health information professionals need to be equipped with the competencies in health data science. 

The purpose of the Health Data Science Graduate Academic Certificate is to educate professionals who can apply data science methods and techniques to health-related problems. 

Individuals who complete the Health Data Science GAC will be able to: 

  1. Describe the framework of modern healthcare and the current challenges facing data-driven healthcare. 
  2. Identify methods and tools related to managing computable health data, information, and knowledge. 
  3. Manipulate, organize, analyze, and visualize health data to improve the delivery of healthcare. 

Courses for the Health Data Science GAC are offered both face-to-face at the Denton campus and online.  Students will take three (3) required courses and one (1) elective course for a total of four (4) courses (12 credit hours). 

All 12 credit hours of coursework taken for the Health Data Science GAC can be applied to the Master of Science in Information Science with a Concentration in Health Librarianship or the Master of Science in Health Informatics

Course Requirements

Required Courses (Students will complete all of the following courses): 

  • HINF 5637 Introduction to Health Informatics
    Overview of health informatics and its subfields and history. Relationship of key information science concepts to health informatics. Application of artificial intelligence and data science techniques to health informatics. Introduction to major health information systems and evidence-based healthcare. Exploration of future trends. 
  • HINF 5770 / INFO 5770 Introduction to Health Data Analytics
    Introduction to key concepts and principles of health data analytics. Topics covered include the life cycle of health data analysis, such as data acquisition, data preprocessing, data integration, descriptive statistics, and statistical inference. In addition, principles of health research methods and the basics of Python and its libraries for data processing and statistical analysis are introduced.
  • HINF 5771 / INFO 5771 Application of Health Data Analytics
    Presents advanced topics of health data analytics by focusing on applications and practices. Topics include probability, statistics, regression, classification, clustering, evaluation, and machine learning algorithms such as deep learning. Prepares students to preprocess, analyze, visualize data, and use advanced statistical tools to make decisions on health risk factors, outcomes, costs, among others. Using Python and its libraries for data processing and machine learning will be introduced. 

Elective Courses (Students will take one (1) course from the following list):

  • INFO 5707 Data Modeling for Information Professionals
    Designed to meet the needs of the information industry for data modeling and database design for text and multimedia applications. Focus on the application of data modeling technologies to library and information science practice and research. Class projects provide hands-on experience in designing and implementing database systems for information service–oriented organizations such as libraries, museums, publishers and bookstores.
  • INFO 5709 Data Visualization and Communication
    Introduces principles and techniques for data visualization for creating meaningful displays of quantitative and qualitative data to facilitate decision-making. Emphasis is placed on the identification of patterns, trends and differences among data sets.
  • HINF 5365 Information Systems in Healthcare 
    Overview of the health care environment and health information stakeholders, major information systems used in health care and research settings, perspectives on the technology and management of health information systems. 
  • INFO 5505 Applied Machine Learning for Data Science
    Introduction to concepts of machine learning and widely adopted machine learning algorithms including regression, clustering, support vector machine, and neural network. Defines complex modern machine learning architectures in Google TensorFlow and Keras frameworks using Python programming language. Introduces the applications of machine learning to computer vision with Convolution Neural Networks (CNN), natural language processing with Recurrent Neural Network (RNN), and information retrieval with RNN and CNN.

Some courses listed may have prerequisites. Students should consult with the program advisors prior to enrolling in the individual courses.

Ready to Apply?

Students are admitted following the holistic review of the application. Successful applicants will have demonstrated competence in mathematics/programming through a combination of prior training, coursework and/or relevant work experience. The steps to apply for the Health Data Science GAC are below:

1. Apply to the University of North Texas Toulouse Graduate School via ApplyTexas

  •   Send transcripts from previous schools attended to the UNT Toulouse Graduate School. Official transcripts may be sent from previous institutions via email to

2. Complete the Department of Information Science GAC Application Form and attach a current resume to the application.

NOTE: Students who are awarded Academic Certificates and later apply for admission to the M.S. in Information Science or the M.S. in Health Informatics programs will be required to submit the additional materials needed for admission to the M.S. programs.

If you are a current student in the Department of Information Science and you are applying for a GAC, please complete the Application for Concurrent Graduate Academic Certificate Programs (EUID and UNT password login required) so that your academic certificate program will show up on your transcript. If you do not complete the form before your graduating semester, the Toulouse Graduate School will not accept your request for the certificate.

Once You Are Admitted

Once admitted, you will be assigned an advisor who will assist you in getting enrolled for classes and beginning the Graduate Academic Certificate Program.

Academic Certificate Completion Form and Request to Receive Your Certificate

Once you complete your course work, please submit the Request for Graduate Academic Certificate of Completion form to receive your certificate.

Contact Information: 

Title Contact E-mail
Director, Health Informatics/Health Librarianship Program Dr. Ana D. Cleveland
Asst. Dir., Student Support Services Rachel Hall
Department Chair Dr. Jiangping Chen