BioHealth Analytics

Emphasis Area Overview

The University of Missouri’s Data Science and Analytics MS BioHealth Analytics Emphasis equips students with data analytics skill sets to process, mine, and interpret large-scaled genomics and medical records for bio and healthcare industry.


The course will introduce the foundational concepts of genomics and bioinformatics. Genomics is a combination of biological and computational methods that explore the roles of DNA, genes, and proteins on a very large scale. However, understanding how to interpret the results depends (at least) on a basic understanding of biology. The course does not assume a student has a biological background and it will cover the concepts necessary to implement genomics methods.

3 Credit Hours

The integration of multiple types of omics data set such as genomics, epigenomics, transcriptomic, proteomic and metabolomics are very important to understand the pathophysiology of human complex diseases. This course will describe the basic concepts of Multiple types of Omics datasets and databases. This course will also focus on various tools and its application in knowledge discovery from multi-omics data set and its challenges related to preprocessing, analysis and visualization. Hands-on computer experience will be provided through web resources and Jupyter notebook environment.

3 credit hours

The course covers the basic concepts surrounding the analysis of health data. Topics include ethics and regulations of protected health data, healthcare data standards, and statistical analysis and dissemination techniques suitable for health care settings. Project work involves accessing and analyzing real (de-identified) health care data. This course focuses on health data analysis that is done in industry, insurance, hospitals and research. Practical, hands-on course with focus on fundamental data science skillsets such as programming in Python and data carpentry.

3 Credit Hours

This course covers advanced topics in health data analysis. Students will learn about research informatics and clinical trials, and advanced statistical methods used in health data analysis. Additionally, students will be exposed to new forms of health data processing such as free text data, image data, and longitudinal data. Students will explore the use of machine learning and AI in health care settings, and applied clinical informatics in the form of decision support. Project work involves accessing and analyzing real (de-identified) health care data.

3 Credit Hours


Graduates of the Master’s of Science in Data Science and Analytics who pursue the BioHealth Analytics emphasis area will achieve the following educational objectives:

  • Possess an in-depth understanding of the data analytics needs in bio industry and healthcare systems in the US and worldwide;
  • Have the ability to practice their analytic skill-sets to applications in agriculture, human medicine, and health information systems in genomics, proteomics, phonemics, and electronic medical records;
  • Be able to present and interpret their data analytic results with actionable plans in the bio and healthcare industry.