Subject Code : COM736
Assignment Task:

This item of coursework will contribute to 50% of the overall module mark. As part of this coursework, you are required to undertake data analysis and results in the visualization of a data science problem, which may require discovering trends, making predictions and/or grouping objects/events. For this, you may take up the Australian Imaging Biomarkers and Lifestyle (AIBL) flagship study of ageing data. The data can be applied and accessed. The data description can be found by using the Data Dictionary. The AIBL data were categorized into three clinical diagnostic results: Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer’s disease (AD). For this coursework, you may consider only two categories, i.e. HC and Non-HC (combining MCI and AD). Analyze the problem using the following 7-step systematic approach. 


1. First of all, clearly identify the problem to be solved and write a problem statement. 
2. Identify and locate the data-set. Study all the available information about the data-set, its source, and the method of data collection. Take a note of the format in which the data is stored. (COM736)
3. Take an in-depth look at the data. You may like to make use of an appropriate data visualization technique to make a visual inspection. Find out any anomalies in the form of missing data and/or outliers. Clean the data to ensure acceptable data quality. Make a detailed record of the data cleaning process. 
4. Select appropriate variables for the analysis. A large data-set may contain a huge range of input-output variables. A target output variable may not be influenced by all the inputs in the data-set. Also, it may be that some transformations of the selected variables will be more appropriate features for identifying the target input-output relationships than their original values. 
5. Depending upon the problem statement, perform appropriate data analysis. Often it is helpful to analyze feature separability using a multivariate visualization technique. It may require devising a classifier or regressor using an appropriate machine learning method. Justify your choice. 
6. Present the results using visualization methods that are most appropriate for showing effectively the results of your work. It may include one or more tables, graphs, charts, maps and so on. 
7. Discuss the answer obtained from the results to the question or problem posed in the problem statement made in the step-1, by addressing the following questions. Are there any limitations to the results obtained? Was the question answered partially or fully? How could the problem be handled differently? Where can you take this work further?


Report your solution to the problem in a short 4-page IEEE format paper. It is required to submit into the module assessment area of the Blackboard this word-processed paper along with the functioning code of your problem solution. You may like to present your functioning code along with program outputs through an R Markdown document. The assignment should be submitted by the end of the day on Friday in week 13 of the current semester. No extension to the deadline may be granted unless there are extenuating circumstances. Plagiarism or copying will lead to a mark of zero. 
The report should have a maximum of four pages (A4 size) in a two-column format and 11pt font size. A format of the report is also available at the module web-support area. The report may consist of the following sections: 
(i) An abstract of about 100 words at the very beginning of the report followed by three/four keywords. 
(ii) An introduction that may include a problem statement, a brief coverage of a literature survey, and objectives of the assignment. 
(iii) A materials and methods section that describes the experimental procedure, details about the data, and clearly explains the methods/algorithms used/developed. 
(iv) A performance evaluation section contains experimental results, analysis, and comparative evaluation. 
(v) A conclusion section highlighting the main achievements, limitations, and recommendations for possible future developments. 
(vi) A reference section with full detail of cited references, articles, book chapters, etc. 

 

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  • Posted on : June 01st, 2019

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