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Data Science, Analytics, AI and Computational Methods

Data Science, Analytics, AI and Computational Methods

teaches knowledge and skills of data analytics and computational approaches in healthcare. Students must demonstrate competency in understanding and applying

  • high-level mathematical analysis of large data sets, 

  • predictive and prescriptive analytic methods and tools, 

  • Natural Language Processing (NLP), 

  • optimal clinical knowledge management principles at healthcare organizations, and 

  • information visualization.

Students with interest in extended training in Big Data analytics may concurrently take courses in the School of Computing’s Big Data Certificate program. While students are not expected to take every course, they are expected to gain specialized knowledge in key computational methodologies. For example, trainees may specialize in NLP and predictive analytics, where the courses blend skills in algorithm development with practical knowledge of applications of NLP in biomedicine and healthcare, biomedical knowledge resources, and characteristics of biomedical text. This enables trainees to implement NLP solutions.

Suggested Courses

BMI 6015 - Applied Machine Learning

CS 6300 - Introduction to Data Science, Artificial Intelligence

CS 6390 - Information Extraction from Text 

BMI 6114 - Deep Learning in Biomedicine

CS 6630 - Visualization

CS 6936 - Learning Semantics for NLP 

BMI 6203 - Clinical Database Design

IS 6910 - Data Mining in Healthcare

CS 5300 - Artificial Intelligence 

BMI 6115 - Biomedical Text Processing

IS 6483 - Advanced Data Mining

MATH 5080 - Statistical Inference 

BMI 6016 - Biomedical Data Wrangling



Students have the opportunity to apply for practicums to gain hands-on experience by working a semester with the ReImagineEHR team or a Sociotechnical expertise on an existing project. Must be coordinated with the team/expertise director before registering:

Affiliated faculty

DBMI: Samir Abdelrahman; Bruce Bray; Mollie Cummins, NI; Jennifer Garvin, VA; Guilherme Del Fiol; Scott Evans, Intermountain; Julio Facelli; Bryan Gibson; Peter Haug, Intermountain; Kensaku Kawamoto; Gang Luo; Scott Narus, Intermountain; Dennis Parker; Catherine Staes; Kathy Sward, NI

Non-DBMI: Laura Heermann-Langford, Nursing Informatics, Intermountain; Jeff Phillips, School of Computing Big Data Science; Heather Sobkho, 3M

PhD Application Deadlines

December 31st

It is advantageous to submit your application as soon as possible.  We will begin reviewing applications December 1, 2022.

Online Application

Biomedical Informatics at the University of Utah