Master of Digital Health and Data Science
Unit of study table
| This page was first published on 14 November 2024 and was last amended on 3 March 2025. View details of the changes below. |
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Master of Digital Health and Data Science |
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| Students complete 48 credit points, comprising: | ||
| (i) 24 credit points of Core units of study; | ||
| (ii) 6 credit points of Data Science Elective units of study; | ||
| (iii) 6 credit points of Digital Health Elective units of study; | ||
| (iv) 12 credit points of Capstone Project units of study; | ||
Graduate Certificate in Digital Health and Data Science |
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| Students complete 24 credit points, comprising: | ||
| (i) 6 credit points of Data Science Selective units of study; | ||
| (ii) 6 credit points of Digital Health Selective units of study; | ||
| (iv) 6 credit points of Data Science Elective units of study or 6 credit points of Data Science Selective units of study; and | ||
| (iv) 6 credit points of Digital Health Elective units of study or 6 credit points of Digital Health Selective units of study. | ||
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition |
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Core units |
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| HTIN5006 Foundations of Healthcare Data Science |
6 | N HTIN4006 |
| HTIN5005 Applied Healthcare Data Science |
6 | N HTIN4005 |
| BIDH5003 Foundations of Digital Health |
6 | N HSBH5003 or HSBH3008 or BIDH3008 |
| BIDH5000 Digital Health Innovation and Implementation |
6 | A Assumed basic knowledge of health, health care and associated systems are required. Students who have not completed an undergraduate or postgraduate degree in a health profession will be asked to complete the Open Learning Environment module "Preparation for learning in the Hospital Environment", which is openly available to all University of Sydney students via Canvas. Please check the Canvas site for this unit for any information on further recommended resources |
Selective units of study (Graduate Certificate) |
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Data science selectives |
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| HTIN5006 Foundations of Healthcare Data Science |
6 | N HTIN4006 |
| HTIN5005 Applied Healthcare Data Science |
6 | N HTIN4005 |
Digital health selectives |
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| BIDH5003 Foundations of Digital Health |
6 | N HSBH5003 or HSBH3008 or BIDH3008 |
| BIDH5000 Digital Health Innovation and Implementation |
6 | A Assumed basic knowledge of health, health care and associated systems are required. Students who have not completed an undergraduate or postgraduate degree in a health profession will be asked to complete the Open Learning Environment module "Preparation for learning in the Hospital Environment", which is openly available to all University of Sydney students via Canvas. Please check the Canvas site for this unit for any information on further recommended resources |
Elective units of study |
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Data science electives |
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| INFO5306 Enterprise Healthcare Information Systems |
6 | A The unit is expected to be taken after introductory courses in related units such as COMP5206 Information Technologies and Systems (or COMP5138/COMP9120 Database Management Systems) N INFO4406 |
| HTIN5003 Health Technology Evaluation |
6 | N HTIN4003 |
| COMP9001 Introduction to Programming |
6 | N INFO1110 or INFO1910 or INFO1103 or INFO1903 or INFO1105 or INFO1905 or ENGG1810 |
| COMP9003 Object-Oriented Programming |
6 | A COMP9001 or INFO1110 or INFO1910 N INFO1113 or INFO1103 or COMP9103 |
| COMP5046 Natural Language Processing |
6 | A Knowledge of an OO programming language N COMP4446 |
| COMP5048 Visual Analytics |
6 | A Experience with data structures and algorithms as covered in COMP9103 or COMP9003 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) N COMP4448 or OCMP5048 |
| COMP5318 Machine Learning and Data Mining |
6 | A Experience with programming and data structures as covered in COMP2123 or COMP2823 or COMP9123 (or equivalent unit of study from different institutions). Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra and calculus (e.g. MATH1061 or equivalent) N COMP4318 or OCMP5318 |
| COMP5424 Information Technology in Biomedicine |
6 | A Experience with software development as covered in SOFT2412 or COMP9103 or COMP9003 (or equivalent UoS from different institutions) N COMP4424 |
| STAT5002 Introduction to Statistics |
6 | A HSC Mathematics |
| STAT5003 Computational Statistical Methods |
6 | A STAT5002 or equivalent introductory statistics course with a statistical computing component |
| BMET9925 AI, Data, and Society in Health |
6 | A Familiarity with general mathematical and statistical concepts. Online learning modules will be provided to support obtaining this knowledge N BMET2925 |
| BMET5933 Biomedical Image Analysis |
6 | A An understanding of biology (1000-level), experience with programming (ENGG1801, ENGG1810, INFO1110, BMET2922 or BMET9922) |
Digital health electives |
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| BMET5992 Regulatory Affairs in the Medical Industry |
6 | A MECH3921 or BMET3921 or AMME5921 or BMET5921 and 6 credit points of 1000-level Chemistry and 6 credit points of Biology units N AMME4992 or AMME5992 |
| IDEA9106 Design Thinking |
6 | |
| CEPI5100 Introduction to Clinical Epidemiology |
6 | |
| BETH5204 Clinical Ethics |
6 | |
| HPOL5014 Foundations of Health Technology Assessment |
6 | A Basic understanding of human physiology and the Australian health system |
| HPOL5012 Leadership for Health |
6 | A Students are expected to have at least 3 years work experience in a health practice, policy or administrative role |
| COMP5427 Usability Engineering |
6 | N COMP4427 |
Capstone project units of study |
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| BIDH5001 Digital Health and Data Science Project A |
6 | A Assumed library information systems research skills and basic knowledge of health, health care and associated ethics and governance systems are required. Students must complete a pre-capstone knowledge screening quiz or interview which will identify recommended modules for their capstone. Please check the Canvas site for this unit for any information on further recommended resources, mandatory sessions and modules P 24 credit points of (HTIN5006 or HTIN5005 or HSBH5003 or BIDH5003 or BIDH5000 or COMP9001 or INFO5306 or HTIN5003 or COMP9103 or COMP5046 or COMP5048 or COMP5318 or COMP5424 or STAT5002 or STAT5003 or BMET9925 or BMET5933 or BMET5992 or IDEA9106 or CEPI5100 or BETH5204 or HPOL5014 or HPOL5012 or COMP5427) |
| BIDH5002 Digital Health and Data Science Project B |
6 | A Assumed library information systems research skills and basic knowledge of health, health care and associated ethics and governance systems are required. Students must complete a pre-capstone knowledge screening quiz or interview which will identify recommended modules for their capstone. Please check the Canvas site for this unit for any information on further recommended resources, mandatory sessions and modules P 24 credit points of (HTIN5006 or HTIN5005 or HSBH5003 or BIDH5003 or BIDH5000 or COMP9001 or INFO5306 or HTIN5003 or COMP9103 or COMP5046 or COMP5048 or COMP5318 or COMP5424 or STAT5002 or STAT5003 or BMET9925 or BMET5933 or BMET5992 or IDEA9106 or CEPI5100 or BETH5204 or HPOL5014 or HPOL5012 or COMP5427) |
Post-publication amendments
| Date | Original publication | Post-publication amendment |
|---|---|---|
| 03/03/2025 | Assumed Knowledge (A) for BMET5933 published as: A An understanding of biology (1000-level), experience with programming (ENGG1801, ENGG1810, BMET2922 or BMET9922) |
Assumed Knowledge (A) for BMET5933 amended to: A An understanding of biology (1000-level), experience with programming (ENGG1801, ENGG1810, INFO1110, BMET2922 or BMET9922) |
