Introduction
The MS in Biostatistics, Concentration in Health Data Science educates students with necessary skills from big data, data sciences, and computer sciences to meet new challenges in a wide variety of health-related fields.
Graduates of this concentration will enter the workplace ready to:
- retrieve online public domain health data, provide analytics and dashboards, discover patterns in health data, design algorithms to learn from medical information, and implement natural language processing
- gain skills in health care data management, understand the process of dealing with large health care data, and be able to use R and Python programming techniques to analyze high-dimensional complex health data
- collaborate with public and global health researchers on projects that require statistical expertise for large and complex data analyses
- apply novel advanced analytical methods to analyze health data for individuals, communities, institutions (e.g., Hospitals) and health-related industries (e.g., pharmaceutical and medical insurance companies), and interpret the findings
- effectively summarize and analyze high dimensional health data such as: electronic medical records, health care quality metrics and costs, nutrition/fitness, pharmaceutical, insurance, cancer, surveillance, and micro array and biome datasets
- implement and update statistical predictive models to assess health risks, and identify risk and protective factors
- apply advanced machine and deep learning techniques to examine and analyze large or complex or sparse public and global health data sets
At a Glance: In-State
At a Glance: Out-of-State
Next information sessions
Meet the Division of Epidemiology and Biostatistics
Alumni Testimonial
Machine Learning methods such as predictive modeling, clustering, classification, and natural language processing in Python and R are taking the industry by storm. These methods coupled with the analytical background taught at UIC will allow data scientists to advance in the health care management, analytic and consulting fields.
| Senior Analyst, Vizient Inc.
About Big Health Data Science
Health data science is the process of deriving insights from patterns and correlations found in healthcare data and used to make better decisions about patient care and operations. Public and private healthcare providers generate large amounts of clinical and nonclinical data but do not necessarily have the up-to-date analytical tools to properly manage and use these data for research and healthcare operations. Health analytics involves the application of latest statistical and computational methods to health-related data as a way of improving the efficiency and productivity of health care operations.
Current rapid digitization of public and global health care data is generating massive amounts information. Experts predict the US health care data to reach mind boggling 2,314 Exabytes by 2020 (one Exabyteis 1018 bytes). Driven by mandatory requirements and the potential to improve the quality of healthcare delivery meanwhile reducing the costs, these massive quantities of data (known as big data) hold the promise of supporting a wide range of medical and healthcare functions, including among others clinical decision support for personalized care and health care operations.
Degree-Specific Courses (minimum of 42 semester hours)
The MS in Biostatistics Concentration in Health Data Science program requires a minimum of 42 semester hours and is designed for completion in 2 years when enrolled full-time.
- EPID 403 – Introduction to Epidemiology: Principles and Methods (3 semester hours) [school requirement]
- IPHS 450 – Foundations and Determinants of Public Health (3 semester hours) [school requirement]
- BSTT 426 –Python for Data Science and Large Language Models (3 semester hours)
- BSTT 510 – Biostatistics Theory I (3 semester hours)
- BSTT 511 – Biostatistics Theory II (4 semester hours)
- BSTT 523 – Biostatistics Methods I (4 semester hours)
- BSTT 527 – Statistical and Machine Learning Methods for Data Science (3 semester hours)
- BSTT 528 – Advanced Machine Learning and Artificial Intelligence for Data Science (3 semester hours)
- BSTT 529 – Health Data Science Investigations (2 to 3 semester hours)
- BSTT 535 – Categorical Data Analysis (3 semester hours)
Electives (minimum 8 semester hours)
Recommended courses for electives include BSTT 536 Survival Analysis, BSTT 537 Longitudinal Data Analysis, and BHIS 542 Artificial Intelligence.
Discovery-Based Paper
Students will complete the MS in Biostatistics, Concentration in Health Data Science through a discovery-based paper – met through BSTT 539.