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Module Maintenance
Academic Information
Module Code MN-3024 Academic year 24/25
Full Title Data Mining
College Humanities and Social
Sciences
Level 3
Department Business External Credit Level FHEQ 6 / HESA 3
Module Type Taught/Lecture Based Credits 15
ECTS Credits 7.50
Formal Contact Hours 30
Placement Hours 0
Notional Hours 150
Module Fee 0
Location BAY CAMPUS
Contact Hours Description 10 x 2 hour lectures10 x 1 hour seminars/computer labs
Module synopsis to be printed in the catalogue
The module is designed to provide students with practical and applied knowledge of how to conduct data mining
activities for business and management purposes. This includes conceptual approaches and key concepts in data
mining as well as the statistical and modelling techniques necessary to analyse large data sets to generate
meaningful business intelligence. The module takes a data driven approach to operation of data analysis.
Notes to be printed in Catalogue
Delivery of both teaching and assessment will be blended including live and self-directed activities online and on campus.
This module is available to incoming exchange/visiting students, if there are any linked pre-requisites students will
need to provide a copy of their transcript to assess suitability. Please email employability management@swansea.ac.uk for more information.
Delivery Method
All Programmes will employ a blended approach to delivery using the Canvas Digital Learning Platform for live and
self-directed online activity, with live and self-directed on-campus activities each week. Students may also have the
opportunity to engage with online versions of sessions delivered on-campus
Is this module placement
based Not applicable
Module to be delivered in
collaboration with another
organisation
No
Percentage taught in
Welsh
0%
Module Aims
The module aims to prepare students for undertaking analysis of large data sets. The module aims to make
students aware and informed on the benefits and applications of data mining for business or management.
Learning Outcomes
On completion of this module students should be able to:
Appraise the value of data mining
Investigate the application of a range of statistical techniques to analyse large data sets
Evaluate critically the outputs from data mining research and speak meaningfully to the usefulness of such data
outputs
Critique the trade offs involved in data mining and the need to balance conceptual appeal with statistical validity
Transferable Skills
Decision making
Evaluation skills
Independent learning
IT skills
Lateral thinking
Numeracy
Problem solving
Research skills
Syllabus
Introduction to data mining
Data Input: concepts, instances and attributes
Data output: knowledge representation (linear models, rules, trees)
Simple Algorithms
Statistical modelling
Validation and evaluating output
Applied data mining: decision trees, classification rules, association rules
Extended linear modelling and prediction
Data transformation
Each lecture has an accompanying seminar on the same topic, except for:
Week 5 – coursework preparation seminar
Week 10 – revision session and worked mock-exam.
Are there any challenges which might affect a disabled student being able to satisfactorily undertake the teaching
and learning methods of this module
Where a need has been identified at recruitment, or at any later stage, an assessment will be made in conjunction
with the student and the Disability Office. The School will make reasonable adjustments and/or develop alternative
arrangements in conjunction with the student. Support material for this module will be available online. With the
consent of the Module Co-ordinator students may record lectures for personal use. If necessary student note takers
and support workers can attend classes. If access to a particular area is restrictive then the University will alter the
venue for the course to allow full access. Alternative forms of assessment will be considered if appropriate.
Reading List
Title & Author Publisher
Data mining : practical machine learning tools and
techniques / Ian H. Witten, Eibe Frank, Mark A. Hall,
Christopher J. Pal.
Cambridge, MA : Morgan Kaufmann is an imprint of
Elsevier 2017
Assessment Information
Module Rules
Introduction to data mining / Pang-Ning Tan, Michael
Steinbach, Anuj Karpatne, Vipin Kumar.
Harlow : Pearson Education Limited 2019
Introduction to data mining / Pang-NingTan, Michael
Steinbach and Vipin Kumar.
Essex : Pearson 2014
Method of moderation to use for the predominant assessment method Moderation by sampling of
the cohort
Module components
Assessment Type Mark Scheme Assessment Month Weighting
Coursework 2 marking scheme assessment UG November 50
Coursework 1 marking scheme assessment UG October 50
Component descriptions
Assessment 1: Coursework, Individual written 2500 words (50%)
Assessment 2: Coursework, Individual written 2500 words (50%)
Quantitative coursework, with students being instructed and assessed on, for example, statistical modelling, use of
various machine learning algorithms and validation and interpretation of their outputs (2,500 words equivalent)
In what ways will students receive feedback on assessed work, including formal examinations
Coursework: Written feedback via Canvas plus drop-in session for students who would like individual feedback on
their performance.
Assessment provision for students with disabilities
Where a need has been identified at recruitment, or at any later stage, an assessment will be made in conjunction
with the student and the Disability Office. The School will make reasonable adjustments and/or develop alternative
arrangements in conjunction with the student. Support material for this module will be available online. With the
consent of the Module Co-ordinator students may record lectures for personal use. If necessary student note takers
and support workers can attend classes. If access to a particular area is restrictive then the University will alter the
venue for the course to allow full access. Alternative forms of assessment will be considered if appropriate.
Provide details of how students would redeem failure in the module
Level 6 module, resit not usually permitted
Semester TB1 Coordinator Dr H Eskandari
Module Lecturers
Lecturer Name Percent Taught
Dr GR Burkhardt 52
Administrative Information
Printed on: 13/09/2024 17:20:51
Dr H Eskandari 48
Module delivered by non
university employee
Module Teaching and Academic Subject Area
Code JACSName Department HECOS
Teaching Load % L100 economics (100450 L100) SOMB
Requisite modules
Co-requisite Pre-requisite Non-requisite
None None None
New Canvas site required Yes
Taught with another
module that uses the same
Canvas site
No
If Yes, please enter the
module code(s)
What activities do you intend to use e-learning for
In general – distribution of module materials and communication with students.
Lecture and Seminar information will be made available through Canvas along with a full Module Handbook and
assessment brief.
Use of Internet resources and digital library.
What assessment activities do you intend to you use e-learning for
Return of written feedback via Canvas.
Release of marks through Gradecentre.
Plagiarism detection via Turnitin.
Student capacity 350
How often will the module
run during the session 1 If more than once, when
Does the module encroach
on other subject areas No
Does the module replace
an existing module No
LTC Authoriser Professor NC Piercy LTC Authorise Date 10 Mar 2014