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Clinical Informatics and Data Science: Core Skills

This is the first of three digital badges that comprise the Certificate in Clinical Informatics and Data Science, designed for people who are professionally engaged with health care delivery or clinical research, and who want to improve their technical skills. Each course is taught in an asynchronous manner beginning on the first day of every other month, and stays open for eight weeks. Knowledge and skills are assessed via a quiz at the end of each module. Participants must score a minimum of 80% on each quiz prior to moving on to the next module. The ideal participant for these badges should possess a background in clinical research, public health, or healthcare delivery, and have a basic understanding of statistics and computing. Familiarity with HIPAA and the health care environment is necessary. Earners of this Core Skills badge will have demonstrated an understanding of the process and methods for applying advanced machine learning and data science methods to clinical data for research, clinical, and public health reporting use. Participants will be assessed on core technical skills, data science workflow, and compliance and other legal considerations in which clinical data is collected and used. This badge is issued by the Robert Wood Johnson Medical School and the New Jersey Alliance for Clinical and Translational Science (NJACTS). Upon successful completion of the three digital badges and a final project, participants will be issued the Certificate in Clinical Informatics and Data Science. The badges are free to any Rutgers, Princeton, or New Jersey Institute of Technology student, faculty, or staff as well as individuals employed by the RWJ Barnabas Health System. Participants interested in registering for these badges should contact Noëlle Foster, Ph.D. nsf44@rwjms.rutgers.edu.

Skills / Knowledge

  • Informatics
  • Bioinformatics
  • Data Science
  • Clinical Data
  • Secondary Data Use
  • Health Information Technology
  • Python
  • R
  • SQL
  • Machine Learning
  • Neural Networks
  • Traditional Statistics
  • Clinical Research
  • Public Health Research
  • Quality Reporting