Master of Science (MSc) - Introduction to Health Data Science: Applied Machine learning and statistical learning in Epidemiology CH&E 6056   Introduction to Health Data Science: Applied Machine learning and statistical learning in Epidemiology
CREDIT HOURS: 3
Health data science refers to analysis of large volumes of complex data using statistical learning and machine learning approaches. The course is designed to deliver in two parts. First part covers descriptive data summery and visual displays of big data. Second part guides students through analytical steps of numerical and textual data analytical tools. Focus will be on basic applied machine learning techniques, text using RapidMiner software and statistical learning on numerical data mining, data visualization, pattern recognition and predictive analytics using R software.
FORMAT:
  • Lecture
  • Seminar
  • Discussion

FORMAT COMMENTS: Each class begins with a briefing of an outline of the objectives and the connection with prior knowledge. We will spend the last one hour working with the data using Software R and RapidMiner. The online quiz is opened after the class and students have 30 minutes with the next 36 hours to finish.
LAB HOURS PER WEEK: 0
TUTORIAL HOURS PER WEEK: 0
COREQUISITES: None
PREREQUISITES: Elementary statistics equivalent to CHE5019 and/or biostatistical modeling CHE 6019 or equivalent course.
CROSS-LISTING: None
RESTRICTIONS: None
EXCLUSIONS: None