ICT616-Data Resources Management

Overview

The data mining project is an assignment that allows you to conduct an investigation and analysis into an actual data set of your choosing. The analysis should follow the stages of the CRISP-DM so that a uniform approach is taken. There are numerous data sets available online, and a link to a good repository has been given in the Moodle web page earlier in the semester. You are free to choose any data set you prefer, the conditions being that

1. Data set must be freely available online so that I can download it and perform the analysis myself.
2. Students must each choose unique projects – this generally means different data sets entirely.

If you have another preferred source of data then you may request to use that instead and I’ll have a look. I can also propose sets of data, if students need additional sets. Having decided on a data set you should then post up your plans on the discussion forum for other students to view and comment. This discussion is assessed.

The overall aim of the assignment is simply to choose a data set and then mine some ‘interesting’ pattern in the data set. This should then be described and explained to the reader. The report does not require lengthy text sections and much of the content may be results of analysis and/or graphs or plots as required. The whole assignment should be submitted in a single Word document.

The marks for the report section are split evenly into two areas:

1. Data understanding and preparation
   1. Brief overview of data and business understanding (Similar to ORGANIZATIONAL UNDERSTANDING in book) (max 1 page)
   2. Descriptive stats on data, overview of the format and types. Explain what you did to clean up the data, what you found and so on. May be supplemented with diagrams or plots (Similar to DATA PREPARATION in book).
   3. Data cleaning, transformation, filtering applied as required (No space guideline for this, simply use up as much space as you need)

1. Analysis and understanding
   1. Appropriate analysis chosen
   2. Quality of results
   3. Explanation of interesting patterns found
   4. Interpretation of results