Note: This unit is an archived version! See Overview tab for delivered versions.
INFO2150: Introduction to Health Data Science (2018 - 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). |
| 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 Poon, Josiah
Dr Lam, Mary |
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| Timetable: | INFO2150 Timetable | |||||||||||||||
| Time Commitment: |
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Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.
| Attribute Development Method | Attribute Developed |
| Students learn and practice the health data analysis processes. They have to design the pre-processing task sequence and choice of analytical methods. | Design (Level 2) |
| Students are required them to apply relevant pre-processing techniques to clean up a set of given health data. Students should be capable to explain their choice of methods & tools used in the data analysis process. | Engineering/IT Specialisation (Level 3) |
| Students are able to apply various statistical methods to analyse a clean-up data. Through their assignment, students are expected to articulate the rationale and any assumption behind their decision, as well as drawing conclusion and inference from the data. | Maths/Science Methods and Tools (Level 3) |
| Students practice their written and oral communication skills through the assessments. They need to articulate well the aim, issues arising from the data characteristics, the reasons behind decision choices. They should be able to discuss and draw insights from the results through their analytical work. It is expected the students can present their results in the most appropriate graphical and tablular format. |
Communication (Level 3) |
| Students are given the context to work in team to integrate data analysis results, as well as the preparation of written report and verbal presentation. | Project and Team Skills (Level 2) |
For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.
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.
Engineering/IT Specialisation (Level 3)| Assessment Methods: |
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| Assessment Description: |
Lab Activities: A student has to participate and engage in the activities in the workshop (from w2-w12). Each week`s work worths 2 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: Write a small script to extract information from a given dataset of health text for analysis. Quiz: A quiz to assess a student`s understanding of knowledge content taught. |
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| Grading: |
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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: Presentation of Scientific Information | |
| Week 2 | Lecture: Understanding Data |
| Week 3 | Lecture: (Video session) Data Preprocessing, HL7 |
| Week 4 | Lecture: Data Quality |
| Week 5 | Lecture: Privacy and Ethics |
| Assessment Due: Presentation | |
| Week 6 | Lecture: Data Linkage |
| Week 7 | Lecture: Recalling Statistics (1): Chi-square, T-Test |
| Assessment Due: Project-M1 | |
| Week 8 | Lecture: Recalling Statistics (2): ANOVA, Correlation and Regression |
| Week 10 | Lecture: Statistical Interpretation from Health |
| Week 11 | Lecture: Extracting Health Information from Health Text |
| Assessment Due: Case Study (Statistics) | |
| 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:
| Attribute | Practiced | Assessed |
| Engineering/IT Specialisation (Level 3) | Yes | 28.25% |
| Maths/Science Methods and Tools (Level 3) | Yes | 37% |
| Information Seeking (Level 3) | No | 11.5% |
| Communication (Level 3) | Yes | 18.5% |
| Design (Level 2) | Yes | 4.75% |
| Project and Team Skills (Level 2) | Yes | 0% |
| Professional Conduct (Level 2) | 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.
