DATA621
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Advanced Statistical Modelling
Graduate Science Education (O)
MD - Cumming School of Medicine
Subject
DATA - Data Science
Description
An introduction to the fundamental statistical methods used in health data science including interpretation and communicating the results of these methods. Explores modelling using an epidemiological paradigm such as the assessment for modification and confounding. Introduces fundamental health research methods including study design and the evidence hierarchy.
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
Course Attributes
Fee Rate Group(Domestic) - H, Fee Rate Group(International) -G, GFC Hours (3-0)
Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.
Component
LEC
Units
3
Repeat for Credit
No
Subject code
DATA
Understanding Course Information
Please refer to Course Terminology and Description to better understand how to interpret course information such as GFC Hours, pre-requisites, course levels, etc.