Statistical Foundations for Health Data Science - HDAT9200
Faculty: Faculty of Medicine
School: School of Medical Sciences
Course Outline: MSc Health Data Science
Campus: Sydney
Career: Postgraduate
Units of Credit: 6
EFTSL: 0.12500 (more info)
Indicative Contact Hours per Week: 10
CSS Contribution Charge: 3 (more info)
Tuition Fee: See Tuition Fee Schedule
Further Information: See Class Timetable
View course information for previous years.
Description
This is a core course of the Graduate Certificate 7372, Graduate Diploma 5372 and Master of Science in Health Data Science 9372.
Health data is often complex and noisy. Obtaining actionable insights (or revealing the hidden signals) from such data requires the utilisation of probabilistic concepts. Thus a solid understanding of the principles of statistics is intrinsic to Health Data Science. The aim of this first course in probability theory is to introduce the foundations required to understand such phenomena.
The course design is highly innovative and novel. Statistical computing is used to gain a sound understanding of statistical theories and concepts. Specifically, this course draws on the practical application of Monte Carlo algorithms, which are a very effective method of statistical computing. Once this illustrative approach has (a posteriori) demonstrated a theory, it will then be stated formally.
The core content will be delivered through a flipped approach utilising audio-visual excerpts on the Moodle TELT platform, supported by presentations from Centre for Big Data Research in Health (CBDRH) experts. Statistical computing will be used as the process that drives the content. Peer instruction via discussion during face-to-face sessions will offer support in the form of collaborative learning. Active participation will be encouraged throughout, along with a reflective outlook.
Health data is often complex and noisy. Obtaining actionable insights (or revealing the hidden signals) from such data requires the utilisation of probabilistic concepts. Thus a solid understanding of the principles of statistics is intrinsic to Health Data Science. The aim of this first course in probability theory is to introduce the foundations required to understand such phenomena.
The course design is highly innovative and novel. Statistical computing is used to gain a sound understanding of statistical theories and concepts. Specifically, this course draws on the practical application of Monte Carlo algorithms, which are a very effective method of statistical computing. Once this illustrative approach has (a posteriori) demonstrated a theory, it will then be stated formally.
The core content will be delivered through a flipped approach utilising audio-visual excerpts on the Moodle TELT platform, supported by presentations from Centre for Big Data Research in Health (CBDRH) experts. Statistical computing will be used as the process that drives the content. Peer instruction via discussion during face-to-face sessions will offer support in the form of collaborative learning. Active participation will be encouraged throughout, along with a reflective outlook.
1. Critique the Frequentist and Bayesian frameworks of probability
2. Appraise probability distributions and their summaries
3. Evaluate the role of Causation, Bias, and Confounding in Epidemiology
4. Investigate epidemiological risks and rates
2. Appraise probability distributions and their summaries
3. Evaluate the role of Causation, Bias, and Confounding in Epidemiology
4. Investigate epidemiological risks and rates
Lecture: 1 hour
Tutorials: 2 hours
Web-based online learning activities: 7 hours
Tutorials: 2 hours
Web-based online learning activities: 7 hours
Study Levels
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