INFO2150 Introduction to Health Data Science Project ASSIGNMENT

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INFO2150 Introduction to Health Data Science Project ASSIGNMENT

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INFO2150
Introduction to Health Data Science
Project ASSIGNMENT
DUE DATE: Week 6
Individual Project: 15% (of the whole unit)
AIM
There are times that government / hospitals / welfare and social organisations / medical & health
researchers or professionals have to find an answer for a clinical question using existing data. The obvious
thinking is to go to a database and create a SQL to search data for the question. This task is, however,
easier said than done. The process involves a number of tasks before a dataset is ready for analysis. Some
of them are more policy and legal related than technical issues, and those tasks can be very time
consuming as well (this activity will be dealt with by another assignment in this unit of study). Once a
dataset is available, it is still not a straightforward analysis because there are missing data, bogus entries in
the field, incompatible information in a record and all sorts of bizarre phenomenon in real world data. This
assignment is liked a in-the-field project that requires each student to conduct a practical clinical data
research with our given dataset, so that a first-hand experience and knowledge can be acquired by the end
of this project.
TASKS
This is an individual assignment and you need to use complete the tasks in M1.
Task Description Percentage Due
M1 Data Profile & Presentation, Project Proposal & Data Cleaning 15 23:59 of Sat in w6
Submission Procedure
Students are expected to submit the report electronically and any supporting files, no later than
● Due date: submitted to Canvas site with the due dates specified in the above table
● Penalty: 20 marks will be deducted per calendar day. Report will not be accepted after 5 working
days from the due date.
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Task M1
• To explore and to understand the dataset
• Present data using tables and graphs, and comments
• To define a health question (or questions) that the provided dataset can help answer
• To clean up and to pre-process the dataset prior to your analysis steps
• Actual hypothesis testing is not required in M1
M1: CRITERIA-BASED MARKING
Pass
A student can only get a mark in the Pass grade if he/she completes all of the following items:
1. Submission of soft copy of the report detailing the data analysis and any necessary files to
eLearning site.
2. Basic description of data profiling method.
3. Presentation of basic descriptive statistics of the raw data.
4. Basic description of data cleaning process.
5. A simple report written.
Credit
A student can only get a mark in the Credit grade if the student has already met the Pass grade with the
completion of all of the following items:
6. Detailed analysis and discussion of the raw data.
7. Appropriate use of data analysis methods.
8. Elaborate discussion of data cleaning process with rationale.
9. Meaningful discussion over the cleaning results.
10. A good explanation and discussion of the proposed research question and how it is supported
by the dataset.
Distinction
A student can only get a mark in the Distinction grade if the student has already met the criteria in the
Credit grade with the completion of all the following items:
11. Propose a research question (hypothesis to be validated by statistical testing) for M2.
12. Good use of visualisation to present scientific information.
13. Appropriate data cleaning process with justification for the research question.
14. Able to explain the significance of the research question in the health context.
15. Able to develop a plan with necessary steps for the research question in M2.
High Distinction
A student will only be considered a mark in the High Distinction grade if the student has already met the
criteria in the Distinction grade and at least two of the items from 16 to 20:
16. Various data profiles reported.
17. Demonstrate good knowledge to the profiling with other health-related data.
18. Professional report presentation.
19. Creative visualisation of scientific information.
20. Able to discuss the results with additional relevant clinical and health data (government or
research publications).

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