Emphasis Areas

Data Science is unique and requires a tailor-made approach to fit each field.

Mizzou’s Data Science & Analytics MS multi-disciplinary program focuses on the emphasis areas best fitting your company and career.

BioHealth Analytics

Gain knowledge needed to draw insights from the growing amount of health care data available to shape patient care delivery, pioneer advancements through research, better understand the functions and behaviors of humans and diseases and ultimately change lives.

Geospatial Analytics

Help businesses, government agencies, the military and nonprofit organizations manage the wealth of geospatial data gathered from geographic information systems (GIS), GPS, satellites, social media, location sensors and mobile devices and gain complex insight into human behavior, population shifts, weather patterns and a region’s current and historical characteristics.

High-Performance Computing

Develop the skill to use powerful tools and technologies for mining, storing, analyzing and visualizing large-scale data sets, including networking, architecture, parallel processing, algorithms and other computational techniques.

Strategic Communication & Data Journalism

Grow your knowledge of key tools, languages and visualization techniques to you unlock the full power of big data for developing more meaningful campaigns or transforming numbers into a breaking story.

Human Centered Science Design

Explore the influence of human factors on the data science and analytics field and transform into a stronger visual communicator, a more socially aware analyst and someone who is well-versed in user-experience principles for every stage of the information lifecycle.

Master’s Program Requirements

Students will work closely with faculty mentors, gain experience through solving real-word Big Data issues, explore concepts targeted to their emphasis and more.

The capstone project teams up students with faculty & members of industry for hands-on experience with large data sets and the latest technology and techniques.

Required Credits

30 Hours

  • 16 Credit Hours of Data Science Core Curriculum
  • 9 Credit Hours of Emphasis Area courses
  • 2 Credit Hour Case Study
  • 3 Credit Hour Capstone

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