Note: This unit version is currently being edited and is subject to change!
INFO2150: Introduction to Health Data Science (2019 - Semester 2)
| 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
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| 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 Han, Caren
Dr Poon, Josiah Dr Lam, Mary |
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| Tutor/s: | Siwen LUO | |||||||||||||||
| Timetable: | INFO2150 Timetable | |||||||||||||||
| Time Commitment: |
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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: |
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| 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. |
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| Grading: |
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| 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 http://sydney.edu.au/engineering/student-policies/ 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 https://web.timetable.usyd.edu.au/calendar.jsp
| 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 Goals
This unit contributes to the achievement of the following course goals:
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.
