Module Descriptors
DATA MANAGEMENT
COIS50585
Key Facts
Faculty of Computing, Engineering and Sciences
Level 5
15 credits
Contact
Leader: Clare Stanier
Email: C.Stanier@staffs.ac.uk
Hours of Study
Scheduled Learning and Teaching Activities: 36
Independent Study Hours: 114
Total Learning Hours: 150
Assessment
- EXAMINATION - UNSEEN IN EXAMINATION CONDITIONS weighted at 50%
- COURSEWORK weighted at 50%
Module Details
Module Texts
Data Mining - Introductory and Advanced Topics, Dunham, 2002, Prentice Hall, ISBN 0-13-088892-3
Oracle DBA Guide to Data Warehousing and Star Schemas- Scalzo, 2003, Prentice Hall, ISBN 0-13-032584-8
Oracle DBA Guide to Data Warehousing and Star Schemas- Scalzo, 2003, Prentice Hall, ISBN 0-13-032584-8
Module Special Admissions Requirements
None
Module Resources
Students will be provided with a Module Handbook including the descriptor, schedule of delivery and additional reference material to support student-centred study.
Additional case study material and practical workbooks will be provided when appropriate to the schedule.
Lecture notes will be provided electronically prior to delivery and be available throughout the running of the course.
Access to a PC with suitable word processing, diagramming and appropriate tools for use in the producing the coursework and supporting the practical workshops.
Additional case study material and practical workbooks will be provided when appropriate to the schedule.
Lecture notes will be provided electronically prior to delivery and be available throughout the running of the course.
Access to a PC with suitable word processing, diagramming and appropriate tools for use in the producing the coursework and supporting the practical workshops.
Module Learning Strategies
Delivery of the module is as follows:
1 x 1 hour lecture per week for the whole group
1 x 1 hour seminar style lecture to encourage discussion and development of issues
1 x 1 hour tutorial per week in groups of no more than 20 students
(1:n)1 (1:20)2
The following learning strategies will be applied:
- Keynote lectures introducing the main points for the topics covered by the core course material
- Student-centred workshops/tutorials to develop and underline the main topics introduced in the lectures
- Discursive sessions drawing on practical examples
Students will also be encouraged to develop their intellectual, communicative and problem solving skills.
1 x 1 hour lecture per week for the whole group
1 x 1 hour seminar style lecture to encourage discussion and development of issues
1 x 1 hour tutorial per week in groups of no more than 20 students
(1:n)1 (1:20)2
The following learning strategies will be applied:
- Keynote lectures introducing the main points for the topics covered by the core course material
- Student-centred workshops/tutorials to develop and underline the main topics introduced in the lectures
- Discursive sessions drawing on practical examples
Students will also be encouraged to develop their intellectual, communicative and problem solving skills.
Module Additional Assessment Details
Examination 2 hours (50%) (Learning outcomes 1, 2 and 3)
Coursework (50%) (Learning outcome 4)
Individual essay (3,000 words) researching current issues concerning the use of data within organisations
Coursework (50%) (Learning outcome 4)
Individual essay (3,000 words) researching current issues concerning the use of data within organisations
Module Indicative Content
The module will focus on the following key data areas:-
Data Mining - topics to include (not exhaustive): concepts, techniques, related disciplines (e.g. OLAP etc.), classification, clustering, Web, spatial and temporal mining.
Data Warehousing - architecture, star scheme, tuning, loading, etc.
Customer Relationship Management - overview of, ECRM, MCRM etc.
Decision support systems
Quality of Data
An overview of Logistics Applications
Data Mining - topics to include (not exhaustive): concepts, techniques, related disciplines (e.g. OLAP etc.), classification, clustering, Web, spatial and temporal mining.
Data Warehousing - architecture, star scheme, tuning, loading, etc.
Customer Relationship Management - overview of, ECRM, MCRM etc.
Decision support systems
Quality of Data
An overview of Logistics Applications