Postgraduate Course: Credit Scoring and Data Mining (MATH11040)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | Operational Research |
| Course website |
https://info.maths.ed.ac.uk/teaching |
Taught in Gaelic? | No |
| Course description | Large scale databases - data warehouse and data archives; statistical approaches (clustering, discrimination, regression); non statistical approaches, including neural networks and genetic algorithms; commercial software; applications such as clustering, segmenting and scoring. Introduction to credit scoring. Setting up a scoring system. Statistical techniques used in credit scoring. Other approaches to credit scoring. Use of behavioural scoring. Techniques used in behavioural scoring systems. Monitoring and updating scoring systems. Developments in scoring systems. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2014/15 Block 4 (Sem 2), Not available to visiting students (SS1)
|
Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
23/02/2015 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 13,
Seminar/Tutorial Hours 6,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
79 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| No Exam Information |
Summary of Intended Learning Outcomes
| Understanding of statistical and alternative methods of constructing scoring rules. Understanding how to process data prior to model building. Ability to assess and monitor a scorecard. Awareness of current and new applications of credit scoring techniques. Understanding of real life application of data mining, including clustering, segmentation and scoring. |
Assessment Information
| See 'Breakdown of Assessment Methods' and 'Additional Notes' above. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | CSDM |
Contacts
| Course organiser | Dr Julian Hall
Tel: (0131 6)50 5075
Email: |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: |
|
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|
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