Download Approved Module Information for Data Mining and Business

Approved Module Information for BNM842, 2014/5
Module Title/Name: Data Mining and Business Intelligence
Module Code: BNM842
School: Aston Business School
Module Type: Standard Module
New Module? Not Specified
Module Credits: 15
Module Management Information
Module Leader Name
Email Address
Telephone Number
Office
Ali Emrouznejad
[email protected]
0121 204 3092
rM nO 816
Level Description:
Masters
Available to Exchange Students?
Yes
Module Learning Information
Module Aims:
To teach students the fundamentals of business intelligent and its application to business decision making
Module Learning Outcomes:
- 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
Indicative Module Content:
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, decision trees and their applications in business intelligence.
Week 6: Clustering and association rules including hierarchical and k-means clustering, Kohonen networks, Apriori
Week 7: Accessing and collecting data from the Web and introduction to text mining
Week 8: Data mining models in real-world applications: Case Studies such as risk management & fraud detection
Week 9: Revision and in class test
Week 10: Examination
International Dimensions:
The course material is virtually exclusively technical but where applications of the methods are concerned examples will be drawn
internationally.
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.
Links to Research:
The techniques presented in this module are widely used in academic research. Where possible, examples will be given of
applications published in journal articles
Corporate Social Responsibility:
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.
Module Delivery
Methods of Delivery & Learning Hours (by each method):
Method of Delivery
Learning Hours
Lecture:
27 hours
Seminar:
120 hours
Independent Study:
Total Learning Hours:
3 hours
150 hours
Learning & Teaching Rationale:
- 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.
Module Assessment
Methods of Assessment & associated weighting (including approaches to formative assessment as well as summative):
Assessment
Type
Category
Duration/
Submission
Date
Common
Modules/
Exempt
from
Anonymous
Assessment
Weight
Marking
Details
Coursework
Details
Coursework
Details
Individual
Assignment
No
50%
-
-
30%
1:00hrs
-
20%
Group
Assignment
-
Class Test
Details
-
Open Book
-
Total:
Method of Submission:
Both Hard Copy and Electronic Copy
Assessment Rationale:
The module is assessed 50% individual assignment, 30% Group assignment and 20%
computer based test.
Feedback Rationale:
Feedback will be given to students by feedback sheets for both individual and group assignment.
100%