Data Modelling and Analysis
| Code | School | Level | Credits | Semesters |
| COMP4131 | School of Computer Science | 4 | 20 | Spring China |
- Code
- COMP4131
- School
- School of Computer Science
- Level
- 4
- Credits
- 20
- Semesters
- Spring China
Summary
This module will enable you to appreciate the range of data analysis problems that can be modelled computationally and a range of techniques that are suitable to analyse and solve those problems. Topics covered include: basic statistics; types of data; data visualisation techniques; data modelling; data pre-processing methods including data imputation; forecasting methods; clustering and classification methods (decision trees, naīve bayes classifiers, k-nearest neighbours); data simulation and model interpretation techniques to aid decision support.
Spending around 4 hours each week in lectures and computer classes, appropriate software (eg. R, Weka) will be used to illustrate the topics you'll cover.
Target Students
Level 3 and Level 4 students in the School of Computer Science. This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Classes
- One 2-hour lecture each week for 11 weeks
- One 2-hour computing each week for 11 weeks
Assessment
- 25% Coursework 1: Lab submission
- 75% Coursework 2: Data analysis study (code and 4000 words)
Assessed by end of spring semester
Educational Aims
• To introduce the principles, techniques and applications of a range of data analysis and modelling techniques.• To enable the students to appreciate some of the most widely used data analysis and modelling techniques and to know which one to choose for their applications.• To enable the students to understand and be able to put into practice computer-based data analysis and modelling techniques.Learning Outcomes
Knowledge and Understanding:
• Understanding the capabilities, strengths and limitations of data analysis and modelling methods (A3)
• An appreciation of different data analysis and modelling techniques (A4)
Intellectual Skills
• The ability to understand complex ideas and relate them to specific situations (B4)
Professional Skills
• The ability to implement selected data analysis and modelling methods for real world applications (C1)
• The ability to evaluate data analysis and modelling techniques and select those appropriate to a given task (C3)
Transferable Skills:
• The ability to address real problems and assess the value of their proposed solutions (D1)
• The ability to retrieve and analyse information from a variety of sources and produced detailed written reports on the result (D4)