Module overview
Linked modules
Pre-requisite: MANG3108
Aims and Objectives
Learning Outcomes
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- plan and control effectively for successful completion of a personal workload;
- self-manage the development of learning and study skills;
- communicate effectively.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- appropriate for analysing the work with relevant software packages to develop credit scoring solutions;
- use basic heuristics to set booking limits;
- work with current software packages to create models using complex data sources.
- assess the relevance of software packages outputs to the decisions being addressed;
- identify the analytical models;
- handle various decisions with complex/big data;
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- solutions basic principles of pricing and revenue management;
- solutions and technologies specifically designed for handling and extracting patterns from big data;
- underlying theory of credit scoring;
- interpret the output of advanced analytics techniques used for complex data analytics applications.
Syllabus
Learning and Teaching
Teaching and learning methods
Type | Hours |
---|---|
Supervised time in studio/workshop | 10 |
Completion of assessment task | 46 |
Tutorial | 8 |
Follow-up work | 40 |
Preparation for scheduled sessions | 20 |
Lecture | 24 |
Revision | 10 |
Total study time | 158 |
Resources & Reading list
Textbooks
Thomas, L.C., Crook J.N. and Edelman. (2017). Credit Scoring and Its Applications. Philadelphia, PA, USA: SIAM Press.
Chollet, F. (2017). Deep Learning with Python. Manning Publications.
Talluri, K.T. and van Ryzin, G.J. (2005). The Theory and Practice of Revenue Management. Springer.
Gaurav, V. (2013). Getting started with NoSQL: Your guide to the world and technology of NoSQL. Packet Publishing Ltd.
Hastie, T., Tibshirani, R. and Friedman, J. (2013). The Elements of Statistical Learning. Available freely online at https://statweb.stanford.edu/~tibs/ElemStatLearn/. NI, USA: Springer.
Goodfellow, I., Bengio, Y. and Courville, A. (2017). Deep Learning. Available freely online at http://www.deeplearningbook.org/: MIT Press.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
In-class activities
- Assessment Type: Formative
- Feedback: Feedback will arise from in-class activities such as problem-solving activities and discussions, and also from computer labs
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Report | 60% |
Report | 40% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Report | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Report | 100% |
Repeat Information
Repeat type: Internal & External