Module Descriptors
DATA MINING
COMP40012
Key Facts
Digital, Technology, Innovation and Business
Level 4
20 credits
Contact
Leader: Euan Wilson
Email: E.D.Wilson@staffs.ac.uk
Hours of Study
Scheduled Learning and Teaching Activities: 65
Independent Study Hours: 135
Total Learning Hours: 200
Assessment
- Presentation - 1000 words and ten minutes weighted at 50%
- Written Report - 1500 words weighted at 50%
Module Details
Module Learning Outcomes
1) Critically discuss and evaluate domain-specific techniques for processing Data Mining with respect to different disciplines.
Knowledge and Understanding
2) Research, analyse and critically evaluate best practices in Data Mining to generate and process data. Learning, Analysis, Problem Solving
3) Test and develop systematic analytical approaches to problem solving, presenting logical and coherent written arguments for choices made in relation to Data Mining.
Problem Solving, Communication, Application
Knowledge and Understanding
2) Research, analyse and critically evaluate best practices in Data Mining to generate and process data. Learning, Analysis, Problem Solving
3) Test and develop systematic analytical approaches to problem solving, presenting logical and coherent written arguments for choices made in relation to Data Mining.
Problem Solving, Communication, Application
Module Additional Assessment Details
Assignment 1 (30%)
A Presentation: 15 minutes which discusses the range of issues associated with traditional data mining. All illustrated by an undertaken project. Learning Outcomes 1 and 2.
Assignment 2 (70%)
A Written Report: A 2000 word report including a proof of concept: An executive report, emulating a real case scenario where you are in a work place and are given a small data mining project and are required to write an executive report to explain the problem, its characteristics and how it has been approached. Learning Outcomes 1 to 3.
A Presentation: 15 minutes which discusses the range of issues associated with traditional data mining. All illustrated by an undertaken project. Learning Outcomes 1 and 2.
Assignment 2 (70%)
A Written Report: A 2000 word report including a proof of concept: An executive report, emulating a real case scenario where you are in a work place and are given a small data mining project and are required to write an executive report to explain the problem, its characteristics and how it has been approached. Learning Outcomes 1 to 3.
Module Indicative Content
This module develops the following –
- Overview of data mining principles and techniques:
- Data Mining related disciplines (e.g. using OLAP etc.),
- Classification
- Clustering
- Social media processing and sentiment analysis
- Web, spatial and temporal mining
- Handling missing data
- Classification, link analysis/association, rule mining
- Probabilistic and statistical methods
- Overview of data mining principles and techniques:
- Data Mining related disciplines (e.g. using OLAP etc.),
- Classification
- Clustering
- Social media processing and sentiment analysis
- Web, spatial and temporal mining
- Handling missing data
- Classification, link analysis/association, rule mining
- Probabilistic and statistical methods
Module Learning Strategies
The module is delivered as a practical session a week. This will take the form of workshops and will cover the content described above. The content will be flexible but serves the anticipated outcomes. Flexibility in terms of the case studies is essential to address the student’s specific needs and also to keep up with the rapidly evolving nature of the subject domain. Therefore, these workshops will be used:
a. To discuss case studies
b. To test and experiment with some use cases and small examples, allowing for peer learning and hands-on practice.
c. To work on student projects
d. To deliver presentations for the assessment.
The VLE will be used for discussing the case studies outside classes and provide the students with relevant material to support their learning.
a. To discuss case studies
b. To test and experiment with some use cases and small examples, allowing for peer learning and hands-on practice.
c. To work on student projects
d. To deliver presentations for the assessment.
The VLE will be used for discussing the case studies outside classes and provide the students with relevant material to support their learning.
Module Texts
These are indicative only. Texts are updated on an annual basis and when you start to study this module, you will be referred to an online reading list, currently provided through Keylinks. You are advised not to buy any textbooks for this module without checking the online reading list.
Harper, J. (2019) Data Science For Business: How To Use Data Analytics and Data Mining in Business, Big Data For Business, Springer, ISBN:9781386573241
Du, H., (2013) Data Mining Techniques and Applications: an introduction Cengage ISBN 978-84480-891-5
Marr, B. (2016 ) Big data in practice : how 45 successful companies used big data analytics to deliver extraordinary results Wiley ISBN: 1119231388; 9781119231387
Data Protection Act 2018 and GDPR 2018
Harper, J. (2019) Data Science For Business: How To Use Data Analytics and Data Mining in Business, Big Data For Business, Springer, ISBN:9781386573241
Du, H., (2013) Data Mining Techniques and Applications: an introduction Cengage ISBN 978-84480-891-5
Marr, B. (2016 ) Big data in practice : how 45 successful companies used big data analytics to deliver extraordinary results Wiley ISBN: 1119231388; 9781119231387
Data Protection Act 2018 and GDPR 2018
Module Resources
NoSQL Datastores
Relational datastores
Weka or similar mining tool
Relational datastores
Weka or similar mining tool
Web Descriptor
This module develops and explores areas such as data mining principles and techniques, social media processing, probabilistic and statistical methods, and related disciplines.