Course

Health Data Science: Dissertation (Part B) - HDAT9902

Faculty: Faculty of Medicine

School: School of Medical Sciences

Course Outline: MSc Health Data Science

Campus: Sydney

Career: Postgraduate

Units of Credit: 12

EFTSL: 0.25000 (more info)

Indicative Contact Hours per Week: 1

CSS Contribution Charge: 3 (more info)

Tuition Fee: See Tuition Fee Schedule

Further Information: See Class Timetable

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Description

This course is the workplace/internship research option of the Master of Science in Health Data Science 9372.

This course (in combination with Part A: HDAT9901) is the part-time version of 'Health Data Science: Dissertation' (HDAT9900). Collectively, Parts A and B offer a full session of extensive (directed) independent research. The learning from the Graduate Diploma 5372 scaffolds to this 'real-world' project. In addition to developing sound project management skills, this course facilitates the bigger picture - the Health Data Science pipeline is experienced from start to finish.

Support is given via fortnightly supervisory meetings, supplemented with additional workshops dependent on specific project requirements. An additional early checkpoint involves the development and submission of study protocol and literature review. The final outputs will mirror those of a real world academic setting. Specifically the production of a manuscript to the specifications of a peer reviewed journal relevant to the project and of publishable standard. The project is also to be disseminated orally via a 15 minute presentation (including 5 minutes of questions and answers).

Students are required to complete Graduate Diploma 5372 to a satisfactory standing to be admitted onto this course. The choice of project could either be selected from an offered list of projects or developed from students proposals, dependent on the availability of a suitable supervisor and agreement on project topic.

Learning Outcomes

1. Plan and manage a project through the Health Data Science pipeline.
2. Formulate relevant research questions.
3. Critically appraise existing literature.
4. Design and execute a Data Management Plan.
5. Design and execute a Data Analysis Plan.
6. Disseminate and discuss findings.
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