2015/16 Undergraduate Module Catalogue
COMP2448 Information Systems and Databases
20 creditsClass Size: 90
Module manager: Dr Lydia Lau
Email: L.M.S.Lau@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2015/16
Pre-requisites
| COMP1440 | Computer Systems |
| COMP1551 | Core Programming |
| COMP1745 | Web Development |
This module is not approved as a discovery module
Objectives
On completion of this module, students should be able to ...- Adopt systems thinking in analysing problem situations and in formulating solutions
- Develop information systems architecture for an information driven solution
- Explain the rationale for, and role of, information modelling techniques
- Perform conceptual schema design, using an established methodology and notation
- Transform conceptual schema into relational models
- Describe relational database architecture and the functions of a database management system (DBMS)
- Use SQL to create, maintain, and manipulate data in a relational database
- Demonstrate understanding of non-relational models
- Understand data mining principles and techniques
- Apply practical methodology and toolkit for data mining projects.
Syllabus
- System thinking
- Soft System methodology
- Design for Integration – architectures
- Architectures framework (such as TOGAF) and examples of information systems architectures
- Role of information modelling in information systems design
- Introduction to advanced modelling issues
- Relationship between Entity-Relational modelling and UML
- Overview of database technologies and historical development
- Relational algebra, and Codd’s relational data model
- Relational database management systems (DBMS)
- Normal forms and normalisation
- SQL and database management
- Non-relational databases (such as graph database)
- Introduction to Data mining inputs and outputs
- The WEKA toolkit for applied Data Mining
- CRISP-DM: Cross Industry Standard Process for Data Mining
- Supervised Machine Learning of data classifiers
- Unsupervised Machine Learning of data clusters and associations
- Text data mining and Information Extraction
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| In Course Assessment | 1 | 20.00 | 20.00 |
| In Course Assessment | 2 | 4.00 | 8.00 |
| In Course Assessment | 2 | 18.00 | 36.00 |
| Class tests, exams and assessment | 1 | 1.50 | 1.50 |
| Class tests, exams and assessment | 1 | 2.00 | 2.00 |
| Lecture | 45 | 1.00 | 45.00 |
| Independent online learning hours | 87.50 | ||
| Private study hours | 0.00 | ||
| Total Contact hours | 112.50 | ||
| Total hours (100hr per 10 credits) | 200.00 | ||
Private study
87.5 hours, including online learning, bookwork, lab and coursework.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Assignment | Coursework | 5.00 |
| Assignment | Coursework | 5.00 |
| Presentation | Data Mining | 20.00 |
| Assignment | Information System Case Study | 30.00 |
| Total percentage (Assessment Coursework) | 60.00 | |
Resits will be assessed by the same methodology as the first attempt, unless otherwise stated.
Exams
| Exam type | Exam duration | % of formal assessment |
| Standard exam (closed essays, MCQs etc) | 1 hr 30 mins | 40.00 |
| Total percentage (Assessment Exams) | 40.00 | |
Resits will be assessed by the same methodology as the first attempt, unless otherwise stated.
Reading list
The reading list is available from the Library websiteLast updated: 10/11/2015
Browse Other Catalogues
- Undergraduate module catalogue
- Taught Postgraduate module catalogue
- Undergraduate programme catalogue
- Taught Postgraduate programme catalogue
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