BNM842 Data Mining and Business Intelligence
Academic Year 2013/14 Number of Aston Credits:
15
Number of ECTS Credits:
7.5
Staff Member Responsible for the Module: Dr. Ali Emrouznejad, Operations and Information Management Group Aston Business School, Room 267, Extension: 3092 Email:
[email protected] Availability: see 'office hours' on door Or contact the Operations and Information Group Administrator John Morley Room 266, Extension: 5271
Pre-requisites for the Module: None
Mode of Attendance: On campus
Module Objectives and Learning Outcomes: To teach students the fundamentals of business intelligent and its application to business decision making To provide students with an understanding of the data and resources available on the web of relevance to business intelligence. To enable students to access such structured and unstructured data To present the leading data mining methods and their applications to real-world problems; To provide both the practical experience and the theoretical insight needed to reveal patterns and valuable information hidden in large data sets To enable the student to understand various algorithms of data mining so s/he can develop their own learning in the area beyond the topics covered by the course.
The student should be able to use Data Mining packages to carry out Data Mining applications.
. Module Content: Week 1:
The role of technology in business intelligence with and introduction to data mining process model for business and management + introduction to Data Mining Package
Week 2:
Data pre-processing, visualisation and exploratory analysis used in business intelligence
Week 3:
Use of neural networks in data mining and its application in risk analysis
Week 4:
Advances in neural networks with an applicant to business intelligence
Week 5:
Classification, intelligence.
Week 6:
Clustering and association rules including hierarchical and k-means clustering, Kohonen networks, Apriori
Week 7:
Accessing and collecting data from mining
Week 8:
Data mining models in real-world applications: management & fraud detection
Week 9:
Revision
Week 10:
Examination
decision
trees
and
their
applications
in
business
the Web and introduction to text
Case Studies such as risk
Corporate Connections: In this module several case studies of well-known data mining and business intelligence are used; e.g. credit card fraud detection, predicting stock market returns, and risk analysis in banking.
International Dimensions: The course material is virtually exclusively technical but where applications of the methods are concerned examples will be drawn internationally.
Contribution of Research: The techniques presented in this module are widely used in academic research. W here possible, examples will be given of applications published in journal articles
Ethics, Responsibility & Sustainability: The role of ethics, corporate social responsibility will be discussed in the context of knowledge discovery from data. W hile sustainability is not addressed explicitly, it is embodied within the concepts and techniques covered by the module, for instance by designing a data mining stream within IBM-Modeler Software.
Method of Teaching: 1.25 hour lecture per week, followed by 0.5 hour break, followed by 1.25 hour tutorial / case studies / computer lab session as appropriate. The IBM PASW Modeler and IBM PASW Statistics will be used in the practical sessions. It is essential that students attend both lectures and practical sessions in order to understand the subject. Handouts will be provided at lecture as well as the computer instructions where appropriate to create a dynamic learning environment with student hands-on participation in the application of concepts covered.
Method of Assessment and Feedback: The module is assessed 50% individual assignment, 30% Group assignment and 20% computer based test. Feedback will be given to students by feedback sheets for both individual and group assignment.
Learning Hours: These should be roughly along the lines indicated below: Pre-reading Contact Hours Directed Learning
3 27 120
Total
150
Pre-reading: BM SPSS Modeler User's Guide. General introduction to using SPSS Modeler, (ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/UsersGuide.pd f) IBM SPSS Modeler CRISP-DM Guide. Step-by-step guide to using the CRISP-DM methodology for data mining with SPSS Modeler. (ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/CRISP_DM.pd f)
The following essential and recommended readings are subject to change. Students should not therefore purchase textbooks prior to commencing their course. If students wish to undertake background reading before starting the course, many of the chapters/readings are available in electronic form via on-line library catalogues and other resources.
Essential Reading: IBM SPSS Modeler Modeling Nodes. Descriptions of all the nodes used to create data mining models.(ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/Modeli ngNodes.pdf) IBM SPSS Modeler Algorithms Guide (ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/AlgorithmsGui de.pdf). IBM SPSS Modeler Applications Guide (ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/ApplicationsGu ide.pdf) IBM SPSS Modeler Social Network Analysis User Guide. A guide to performing social network analysis with SPSS Modeler, including group analysis and diffusion analysis (ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/SNA_UserGui de.pdf). SPSS Modeler Text Analytics User's Guide. Information on using text analytics with SPSS Modeler, covering the text mining nodes, interactive workbench, templates, and other resources (ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/Users_Guide_ For_Text_Analytics.pdf).
Indicative Bibliography: Daniel T. Larose (2012) Discovering Knowledge in Data: An Introduction to Data Mining, WileyInterscience Daniel T. Larose (2006) Data Mining Methods and Models, Wiley-Interscience Carlo Vercellis (2009) Business Intelligence: Data Mining and Optimization for Decision Making, ISBN: 978-0-470-51139-8, Wiley Publisher Michael Minelli, Michele Chambers, Ambiga Dhiraj (2013) Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses, ISBN: 978-1-1181-4760-3, Wiely Publisher Rajiv Sabherwal, Irma Becerra-Fernandez (2011) Business Intelligence, Wiley Publisher Paolo Giudici, Silvia Figini (2009) Applied Data Mining for Business and Industry, 2nd Edition, ISBN: 978-0-470-05887-9, Wiley publisher Stephane Tuffery (2011) Data Mining and Statistics for Decision Making, ISBN: 978-0-47068829-8, Wiley Publisher Carlo Vercellis (2009) Business Intelligence: Data Mining and Optimization for Decision Making, ISBN: 978-0-470-51139-8, Wiley Publisher
Journals: Data Mining and Knowledge Discovery: An Springer journal, http://www.springer.com/10618 Statistical Analysis and Data Mining, http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1932-1872 IEEE Transactions on Knowledge and Data Engineering, http://www.computer.org/portal/web/tkde/ International Journal of Business Intelligence Research (IJBIR) (http://www.igi-global.com/journal/international-journal-business-intelligence-research/1168) International Journal of Business Intelligence and Data Mining (http://www.inderscience.com/jhome.php?jcode=ijbidm) Adaptive Business Intelligence (http://www.springer.com/computer/ai/book/978-3-540-32928-2)
