The MS in Big Health Data Analytics degree educates students with necessary skills to meet new challenges in a wide variety of health related fields.

Graduates of this program 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

Next information sessions

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.

Juan Wantig '18  |  Senior Analyst, Vizient Inc.

About Big Health Data Analytics

Health analytics 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.

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