MSc Module DMA Semester 2
Edited: Dr D Chen 2019/20
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Data Mining and Analysis 2019/20
Assessed Coursework Specification: Data Mining Project
Overview
This coursework assignment involves analysing a real-world dataset and
providing meaningful insights into it in order to address some business
concerns and problems identified. The objective of this assignment is to
evaluate your understanding of the basic theory, concepts, and various
algorithms in data mining, and assess your skills of applying SAS® Enterprise
Miner and SAS® Enterprise Guide to carry out a data mining project.
This team-based assignment is to be completed in groups of two. A number of
real-world datasets for this project can be downloaded from the module Moodle
site, and your team will be assigned a dataset by the module lecturer/tutor.
Your role within your team is two-fold: working as a business client and as a
data analyst. As a business client, you are expected to raise meaningful
business concerns/problems in relation to the dataset you have been given.
And as a data analyst, you are required to follow a proper data mining
methodology and apply various techniques covered in lectures to analyse your
data in order to address the business concerns and problems you have raised.
Constant discussion among team members is essential.
There are two project deliverables: a written report and a presentation (worth
70% and 10% of the total module coursework marks, respectively). Your team
is expected to submit a written report on the project and to give an in-class oral
presentation. The mark awarded for this assignment will be a team mark. The
report will be due in week 12 at tutorial (Wednesday 6th May 2020), and you
will have 10 minutes for the presentation followed by questions from the
audience.
Your module tutor will check your project progress on weekly basis. In particular
in weeks 7 and 10, you should have a detailed discussion with your module
tutor regarding your data mining project.
Tasks
You are required to undertake the following tasks:
1. Problem Identification
• Read the data description file to learn the basic characteristics of the
dataset including the data source, the nature of the data, what it is about,
the business context of the data, the total number of attributes
(dimensions), the data type of each attribute, the value range/mode,
skewness, and kurtosis of each attribute, the total number of instances,
and simple data exploration with plotting, etc.
MSc Module DMA Semester 2
Edited: Dr D Chen 2019/20
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• Identify and understand the business problem of interest with regard to
the data.
• Identify what data mining tasks need to be performed in order to address
the business problem concerned.
2. Data Preparation
• Transform the dataset into the proper format to be used by SAS®
Enterprise Miner in order to carry out the required data mining tasks.
• Choose appropriate methods for data pre-processing, including
detecting and dealing with missing values, outliers and imbalanced
attribute values, changing data type, and conducting proper
dimensionality reduction, feature extraction, data transformation, data
partition, and normalisation, etc. where appropriate.
3. Model Construction
• With the pre-processed dataset undertake the data mining tasks you
have identified. You are required to apply at least two different
algorithms for both predictive and descriptive modelling. For
predictive modelling, for example, you may use decision trees and
artificial neural networks, or decision trees and k-nearest-neighbour
based algorithm, etc. For descriptive modelling, you may choose to use
the k-means clustering and histograms/bar charts/Person’s correlation
coefficient, etc.
• In order to build the most appropriate and accurate models and identify
meaningful hidden patterns, different settings for the relevant model
parameters should be considered for each of the selected algorithms
and approaches.
4. Model Interpretation and Evaluation
• Interpret the descriptive models created.
• Compare the performances of different predictive models in terms of
accuracy, error rate, generalisation ability (over-fitting), simplicity and
cost, etc. where appropriate.
• Discuss the meaningfulness and usefulness of the models built and the
patterns revealed, and how the models and patterns can be used to
address the original business concerns. This includes both descriptive
and predictive models.
Final Report
Your final report should be well-formatted as a formal report consisting of
Cover page, Table of Contents, Abstract and References. The report should be
submitted electronically for non-originality check via Turnitin.
MSc Module DMA Semester 2 Edited: Dr D Chen
2019/20
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Data Mining Project Marking Criteria Guidelines
| Element (20% Each) | 0-4 Marks | 4-5 Marks | 5-6 Marks | 6-7 Marks | 7-10 Marks |
| Business understanding and data understanding |
Inadequate analysis of business concerns and data mining tasks. Only simple initial data exploration performed. Lack clarity and relevance. |
Adequate analysis of the key business concerns and data mining tasks. Limited initial data exploration. Probably lack some relevance. Inappropriate means. |
Clear analysis of business concerns and relevant data mining tasks. Probably lack some in-depth view. Essential initial data exploration performed. |
Clear analysis of business concerns and relevant data mining tasks to a certain depth. Sensible initial data exploration performed with appropriate means. |
Thorough and clear analysis of business concerns and relevant data mining tasks. Excellent initial data exploration with effective means. |
| Data pre-processing | Inadequate view of data quality issues. Inappropriate approaches adopted. Poor use of SAS Enterprise Guide/Miner. |
Limited consideration of data quality issues. Some appropriate approaches adopted with limited understanding and limited coverage. Limited use of SAS Enterprise Guide/Miner. |
Reasonable consideration of data quality issues. Appropriate approaches adopted with reasonable understanding and most of the main issues covered. Good use of SAS Enterprise Guide/Miner. |
Good consideration of data quality issues. Appropriate approaches adopted with clear understanding and every aspect covered. Good and flexible use of SAS Enterprise Guide/Miner. |
Thorough consideration of data quality issues. Appropriate approaches adopted with outstanding understanding. Excellent use of SAS Enterprise Guide/Miner. |
| Model construction | Inappropriate algorithms employed. Poor use of SAS Enterprise Miner. |
Some appropriate algorithms employed with limited understanding. Limited use of SAS Enterprise Miner. |
Appropriate algorithms employed with reasonable understanding. Good use of SAS Enterprise Miner. |
Appropriate algorithms employed with clear understanding. Good and flexible use of SAS Enterprise Miner. |
Appropriate algorithms employed with outstanding understanding. Modelling with excellent working knowledge of SAS Enterprise Miner. |
| Model evaluation | Poor model interpretation and comparison with regards to business concerns. No or little meaningful models/patterns provided. |
Weak model interpretation and comparison with regards to business concerns. Very limited meaningfulness. Probably lack some clarity. |
Basic model interpretation and comparison with regards to business concerns. Reasonable models/patterns created. |
Clear model interpretation and comparison with regards to business concerns. Significantly meaningful models/patterns created. |
Thorough and clear model interpretation and comparison with regards to business concerns. Excellent meaningful models/patterns created. |
| Report | Inadequate review of project findings. Lack of clarity and accuracy. Poor presentation. |
Adequate review of project findings. Probably lack of some clarity. Acceptable presentation. |
Clear review and summary of project findings. Good presentation with proper structure and layout. |
Clear and concise summary of project findings. Excellent presentation. Clear structure and layout. |
Exceptionally clear and concise summary of project findings. May raise questions for future research. Outstanding presentation. Clear structure and layout. |
MSc Module DMA Semester 2
Edited: Dr D Chen 2019/20
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Oral Presentation
The oral presentation will be assessed based on the following four criteria:
• Content
• Structure
• Delivery
• Audio-visual aids
You have ten minutes for the presentation followed by questions from the
audience.
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