Management and Curation of Health Data - HDAT9400

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: 8

CSS Contribution Charge: 3 (more info)

Tuition Fee: See Tuition Fee Schedule

Further Information: See Class Timetable

View course information for previous years.


This is a core course of the Graduate Certificate 7372, Graduate Diploma 5372 and Master of Science in Health Data Science 9372.

This course is designed to equip students with the skills required to appraise the current state of knowledge, collect or obtain data, design data management strategies aligned with best practice, and appreciate the day to day practicalities of data curation for sound data management. The course will develop practical competencies in structured literature reviews (systematic reviews), critical appraisal, meta-analysis and meta-regression. Building upon this, students will develop data wrangling skills required to assemble data suitable for research purposes. Data wrangling skills will focus on the key areas of data security, data exploration, documentation of data (for example data dictionaries), data management, with the ultimate aim of creating analysis ready datasets.

The core content will be delivered through lectures. Problem Based Learning (PBL) practicals have been designed to engage students in the learning process, providing hands on practical examples in the context of Health Data Science. Active and self directed learning will be supported via the Moodle TELT.

Learning Outcomes

1. Synthesise current research knowledge through the searching, critical appraisal and assimilation of published
2. Appraise a range of database structures and data file formats.
3. Design (and document) data management plans involving data cleaning procedures and generation of metadata.
4. Evaluate data quality.
5. Develop data linkage strategies.
6. Generate syntax (code) required to produce analysis ready datasets.

Contact hours per week

Lecture: 1 hour
Tutorials: 2 hours
Web-based online learning activities: 5 hours
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