Module overview
This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year.
Having learnt the basic techniques and principles of business analytics in previous modules, this module will introduce you to a number of advanced applications of business analytics in practice. These include pricing and revenue management, credit scoring, big data solutions and technologies, and advanced models to extract complex non-linear patterns from large amounts of diverse data. The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.
Linked modules
Pre-requisite: MANG3056
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- solutions and technologies specifically designed for handling and extracting patterns from big data.
- underlying theory of credit scoring;
- basic principles of pricing and revenue management;
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- use basic heuristics to set booking limits;
- work with relevant software packages to develop credit scoring solutions;
- implement optimal pricing models;
- handle various types of queries with big data sets;
- work with current software packages to create models using complex data sources.
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;
- communicate effectively.
- self-manage the development of learning and study skills;
Syllabus
The topics covered in this module will include:
- Introduction and overview of various business analytics techniques;
- Revenue management models: basic principles, booking limits, optimal pricing model;
- Credit Scoring models: basic concepts, working with software, dealing with difficulties;
- Big data solutions and technologies: the main challenges that drive the need to NoSQL, differences with relational databases, principles of cloud computing.
- Deep learning and non-linear models: basic principles, feature extraction, ensembles, modelling.
Learning and Teaching
Teaching and learning methods
Teaching methods include:
- Lectures
- Interactive case studies
- Problem-solving activities
- Computer labs
- Directed reading
- Private/guided study
Learning activities include:
- Introductory lectures
- Two assignments (individual written reports)
- Case study / problem solving activities
- In class debate and discussion
- Private study
- Use of video and online materials
| Type | Hours |
|---|---|
| Tutorial | 8 |
| Preparation for scheduled sessions | 20 |
| Completion of assessment task | 46 |
| Lecture | 24 |
| Supervised time in studio/workshop | 4 |
| Revision | 8 |
| Follow-up work | 40 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Chollet, F. (2017). Deep Learning with Python. Manning Publications.
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.
Talluri, K.T. and van Ryzin, G.J. (2005). The Theory and Practice of Revenue Management. Springer.
Thomas, L.C., Crook J.N. and Edelman. (2017). Credit Scoring and Its Applications. Philadelphia, PA, USA: SIAM Press.
Gaurav, V. (2013). Getting started with NoSQL: Your guide to the world and technology of NoSQL. Packet Publishing Ltd.
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