Download Data Mining and Forecasting (CB9040)

UNIVERSITY OF KENT
MODULE SPECIFICATION TEMPLATE
SECTION 1: MODULE SPECIFICATIONS
1.
Title of the module
Data Mining and Forecasting (CB9040)
2.
School or partner institution which will be responsible for management of the module
Kent Business School
3.
Start date of the module
Spring of 2011, updated for January 2015
4.
The number of students expected to take the module
30 - 80
5.
Modules to be withdrawn on the introduction of this proposed module and consultation with
other relevant Schools and Faculties regarding the withdrawal
None
6.
The level of the module (e.g. Certificate [C], Intermediate [I], Honours [H] or Postgraduate [M])
Level M
7.
The number of credits and the ECTS value which the module represents
15 credits (7.5 ECTs)
8.
Which term(s) the module is to be taught in (or other teaching pattern)
Spring
9.
Prerequisite and co-requisite modules
CB969, or equivalently, fundamentals of statistics for management and business
10. The programmes of study to which the module contributes
MSc Management Science and MSc Business Analytics
11. The intended subject specific learning outcomes
11.1
Apply statistical techniques that deal with data mining and forecasting
11.2
Analyse the results accurately and present the outcomes in a way that is a wider
audience can understand
11.3
Use of statistical software SPSS for real life data
11.4
Understand and critically discuss research issues within the area of data mining
12. The intended generic learning outcomes
12.1
Being able to think critically and be creative
12.2
The ability to conduct research into business and management issues either individually
or as part of a team through research design, data collection, preparation, analysis,
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synthesis, and reporting
12.3
Using information and knowledge effectively by scanning and organising data,
synthesising and analysing in order to abstract meaning from information and to share
knowledge
12.4
Effective use of ICT
13. A synopsis of the curriculum
Exploratory data analysis

Graphical Techniques, Distribution fitting, Testing for normality, choosing the most
appropriate test, interpreting the results.
Association



Tests of association (Chi-square tests)
Measures of association (Goodman-Kruskal’s gamma, Kendal Tau)
Discovery of relationship between variables in large datasets
Regression and Classification

Multiple linear regression, Classification, and their performance measures
Clustering and factor analysis

Factor analysis; cluster analysis
Time series modelling

The Holt-Winters approach, the Box-Jenkins approach
Data mining process model

Cross Industry Standard Process for Data Mining (CRISP-DM)
The computer package SPSS will be used for this course including EXCEL in some occasions.
14. Indicative Reading List
Most will be based on specially designed class experiences and specially written lecture notes.
Students will have to read papers from the Journal of the Operational Research Society,
Operational Research Insight, Interfaces, the Journal of Forecasting, the International Journal of
Forecasting, Omega, and others as the opportunity arises. Some books that are of relevance, but
students will not be required to purchase are:
 Witten, I.H, Eibe F. and Hall, M.A. (2011) Data Mining: Practical Machine Learning Tools
and Techniques (The Morgan Kaufmann Series in Data Management Systems).
 Hair, J, et al (2005), Multivariate Data Analysis (6th ed.) Prentice Hall, NY
 Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998). Forecasting. Wiley.
 O’Donovan, T.M. (1983). Short term forecasting. Wiley.
 Box, G.E.P. and Jenkins, G.M. (1970) Time Series Analysis. Holden Day.
 Lewis, C.D. (1975). Demand analysis and inventory control. Saxon House.
 Harvey, A.C. (1981). Time series models. Academic Press.
15. Learning and Teaching Methods, including the nature and number of contact hours and the
total study hours which will be expected of students, and how these relate to achievement of
the intended module learning outcomes:
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Hours
Subject LOs
Generic LOs
Lectures
24
11.1 – 11.4
-
Terminals
24
11.1 – 11.4
12.2 – 12.4
Independent study
102
11.1 – 11.4
12.1 - 12.4
Total hours
150
16. Assessment methods and how these relate to testing achievement of the intended module
learning outcomes
Weighting
Subject LOs
Generic LOs
Two In-Class Open
Book Tests
30% each
11.1, 11.2, 11.3
12.1 – 12.4
One data analysis
report – 1000 words
40%
11.1, 11.2, 11.3
12.1 – 12.4
17. Implications for learning resources, including staff, library, IT and space
A computer with SPSS installed for each student in the terminal
18. The School recognises and has embedded the expectations of current disability equality
legislation, and supports students with a declared disability or special educational need in its
teaching. Within this module we will make reasonable adjustments wherever necessary,
including additional or substitute materials, teaching modes or assessment methods for
students who have declared and discussed their learning support needs. Arrangements for
students with declared disabilities will be made on an individual basis, in consultation with
the University’s disability/dyslexia support service, and specialist support will be provided
where needed.
19. Campus(es) where module will be delivered:
Canterbury
SECTION 2: MODULE IS PART OF A PROGRAMME OF STUDY IN A UNIVERSITY SCHOOL
Statement by the School Director of Graduate Studies: "I confirm I have been consulted on the above
module proposal and have given advice on the correct procedures and required content of module
proposals"
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Director of Graduate Studies
Date
…………………………………………………
Print Name
Statement by the Head of School: "I confirm that the School has approved the introduction of the
module and, where the module is proposed by School staff, will be responsible for its resourcing"
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Head of School
Date
…………………………………………………….
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UNIVERSITY OF KENT
Print Name
Module Specification Template
Last updated February 2013
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