MURDOCH UNIVERSITY

ICT616 Data Resources Management

Semester 1, 2020

ASSIGNMENT 2

Assignment Information

You should submit your assignment from the ICT616 LMS site using the Assignment course tool.

Late submissions will be penalised at the rate of 10 marks per day late or part thereof.

You must keep a copy of the final version of your assignment as submitted and be prepared to provide it on request.

The University treats plagiarism, collusion, theft of other students’ work and other forms of dishonesty in assessment seriously. Any instances of dishonesty in this assessment will be forwarded immediately to the Faculty Dean. For guidelines on honesty in assessment including avoiding plagiarism, see: http://our.murdoch.edu.au/Educational-technologies/Academic-integrity/

Deliverables:

1. Online Discussion forum (5 marks): Post your proposed topic and chosen data set as well as a short plan for the project. This is required for approval of the topic. As discussed, student must select unique topics, therefore if any assignments overlap they will not be accepted. This should be done by week 11. Also any queries about the assignment deliverables should be made in the discussion forum so that other students can also benefit from the responses.
   
   
2. Oral Presentation (15 marks): You will be required to present a brief (10) minute executive summary of your project in class. This is a mandatory component of the assignment.
   
   
3. Data Mining technical report (80 marks):Details below.

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)

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

(Similar to MODELING and EVALUATION in book)

In conjunction with the submission of the report – students will also present an overview of the findings.

Notes :

All work must be submitted in ONE word document

No Email submissions allowed unless specific permission has been granted

Do not explain how to perform the techniques or provide instructions in your report, this is what the book is for. Instead spend your time explaining your findings.