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INFO2150: Introduction to Health Data Science (2020 - Semester 2)

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Unit: INFO2150: Introduction to Health Data Science (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Intermediate
Faculty/School: School of Computer Science
Unit Coordinator/s: Dr Poon, Josiah
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: (INFO1003 OR INFO1903 OR INFO1103 OR INFO1110 OR INFO1910 OR DATA1002 OR DATA1902) AND (DATA1001 OR MATH1005 OR MATH1905 OR MATH1015 OR BUSS1020).
Co-Requisites: DATA2001 OR DATA2901 OR ISYS2120 OR INFO2120 OR INFO2820 OR INFO1903.
Brief Handbook Description: Health organisations cannot function effectively without computer information systems. Clinical data are stored and distributed in different databases, different formats and different locations. It requires a lot of effort to create an integrated and clean-up version of data from multiple sources, This unit provides basic introduction to the process and knowledge to enable the analysis of health data. The unit will be of interest to students seeking the understanding of the various coding standards in health industry, data retrieval from databases, data linkage issue, cleaning and pre-processing steps, necessary statistical techniques and presentation of results.

It will be valuable to those who want to work as health-related occupations, such as health informatics analysts, healthcare administrators, medical and health services manager or research officers in hospitals, government health agencies and research organisations. Having said that, a good understanding of health data analysis is a useful asset to all students.
Assumed Knowledge: Basic knowledge of Entity Relationship Modelling, database technology and SQL
Lecturer/s: Dr Lam, Mary
Dr Poon, Josiah
Dr Han, Caren
Tutor/s: Siwen LUO
Timetable: INFO2150 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Workshop 2.00 1 13
2 Independent Study 6.00 1 13

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Unassigned Outcomes
1. Student understands the role of data analysis in decision-making
2. Student understands the technical issues that are present in the collection of clinical data and the necessary of different pre-processing issues to clean-up a dataset for analysis.
3. Student can identify and select appropriate statistical techniques to summarise and analyse clinical data set, and able to articulate a justification of choice of methods.
4. Student can apply health concepts and terms to describe and analyse the role of a data analysis task.
5. Student can identify explicit and implicit requirements for carrying out a health data analysis task to meet stakeholder purposes
6. Student can communicate the results produced by an analysis pipeline, in oral and written form, including meaningful diagrams
7. Student can communicate the process used to analyse health data set, and justify the techniques & methods used.
8. Students can carry out (in guided stages) the whole design and implementation cycle of a simple health data analysis task
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Lab Activities No 20.00 Multiple Weeks 2, 3, 4,
2 Project-M1 No 13.00 Week 6 1, 2, 3, 4, 5, 6, 7, 8,
3 Case Study (Statistics) No 10.00 Week 10 3, 4, 5,
4 Presentation Yes 10.00 Week 11 1, 2, 3, 4, 5, 6, 7, 8,
5 Project-M2 No 12.00 Week 12 1, 2, 3, 4, 5, 6, 7, 8,
6 Quiz (in-class) No 25.00 Week 13 1, 2, 3, 4, 5, 6, 7, 8,
7 Health Text Analytic No 10.00 Week 13 1, 2, 3, 4, 7,
Assessment Description: Lab Activities: A student has to participate and engage in a few activities in the workshop (from w2-w12). The work in each week can worth up to 4 marks. The marks in five random weeks will be chosen as the final lab activities mark.

Presentation of Privacy. It is a group activity when members have to present on a specific privacy/ethical issue related to health data.

Project. A dataset is given such that each student has to perform necessary tasks and analysis to arrive observation and to infer conclusion. The project is to be staged in two milestones to help students achieving a final executive report.

Case Study (Statistics): Using statistical instrument to analyse a given health case.

Health Text Analytic: Students are asked to carry out text analysis tasks that have been discussed in the class.

Quiz: A quiz to assess the understanding of knowledge content of a student.
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Minimum Pass Requirement It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

See the policies page of the faculty website at for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar

Week Description
Week 1 Lecture: Introduction
Lecture: Analytics in Health Context
Week 2 Lecture: Understanding Data
Week 3 Lecture: Information Visualisation, Data Preprocessing
Week 4 Lecture: Data Quality
Week 5 Lecture: Data Linkage
Week 6 Lecture: Recalling Statistics (1): Chi-square, T-Test
Assessment Due: Project-M1
Week 7 Lecture: Recalling Statistics (2): ANOVA, Correlation and Regression
Week 8 Lecture: Statistical Interpretation from Health
Week 9 (Labour Day) no in-class lecture BUT Video Lecture: Extracting Health Info from Text
Week 10 Lecture: More about Text Analytics
Assessment Due: Case Study (Statistics)
Week 11 Lecture: Privacy and Ethics
Assessment Due: Presentation
Week 12 Lecture: Data Mining Techniques: Clustering
Assessment Due: Project-M2
Week 13 Lecture: Quiz and Health Big-Data
Assessment Due: Quiz (in-class)
Assessment Due: Health Text Analytic

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020
Bachelor of Computer Science and Technology 2015, 2016, 2017
Bachelor of Computer Science and Technology (Advanced) 2015, 2016, 2017
Bachelor of Computer Science & Tech. Mid-Year 2016, 2017
Biomedical Mid-Year 2016, 2017, 2018, 2019, 2020
Biomedical 2016, 2017, 2018, 2019, 2020
Software Mid-Year 2019, 2020
Software 2018, 2019, 2020
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 3) No 0%
(7) Project and Team Skills (Level 2) No 0%
(8) Professional Effectiveness and Ethical Conduct (Level 2) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 2) No 0%
(4) Design (Level 2) No 0%
(2) Engineering/ IT Specialisation (Level 3) No 0%
(3) Problem Solving and Inventiveness (Level 2) No 0%
(1) Maths/ Science Methods and Tools (Level 3) No 0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.