Please note
This document only provides information for the academic year selected and does not form part of the student contract
School:
School of Computing and Engineering
Credit Rating:
15
Level (including FHEQ):
M (FHEQ Level 7)
Graded or Non Graded:
Graded
Version Valid From:
2023-09-18
Module Leader:
Isa Inuwa-Dutse
Version Number
2023.03
Learning Methods
Guided Independent Study
Synopsis
Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such … For more content click the Read More button below.
Learning Strategy
The University’s Virtual Learning Environment will be used to support distance learners throughout their learning experience, providing key ideas and themes through lecture content, and a variety of asynchronous activities. Substantive issues will be presented through case studies and research papers. Wherever possible, issues will be related to the students’ … For more content click the Read More button below.
Outline Syllabus
• Introduction to data mining and knowledge discovery, the value of data• Data preparation and pre-processing• Supervised learning: Predictive modelling using Regression, Decision Trees, Neural Networks• Unsupervised Learning: Cluster analysis• Key concepts, tools and approaches for data mining on unstructured data sets• Social impact of data mining• Current research and … For more content click the Read More button below.
Learning Outcomes
On successful completion of this module students will
1.
Justify and critically discuss the key concepts of data mining (including legal implications such as GDPR) and the breadth of areas of application.
2.
Make appropriate modifications to large datasets to prepare the data for analysis and exploration.
3.
Select appropriate data mining techniques in order to enable exploration of large datasets.
4.
Interpret and evaluate the results of the analysis to draw conclusions and make informed decisions.
Formative Assessment
Assessment 1: Other
Summative Assessment
Assessment 1: Portfolio
Assessment 2: Portfolio
Assessment Criteria
Task 1: Students are required to comment on a regular basis on a topic posted by the tutor that relates to the topics covered in the course. This will not be assessed on quality of content but rather on the relevance and the timeliness of the contribution. Students will be … For more content click the Read More button below.
My Reading
Reading List