Module overview
Having learnt the basic techniques and principles of business analytics in previous semester 1 modules, this module will introduce you to a number of advanced machine learning methods and their applications in practice. These include machine learning methods, 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: MANGXXX Analytics in Action I
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- work with current software packages to create models using complex data sources.
- identify the analytical models;
- use basic heuristics to set booking limits;
- appropriate for analysing the work with relevant software packages to develop credit scoring solutions;
- handle various decisions with complex/big data;
- assess the relevance of software packages outputs to the decisions being addressed;
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;
- underlying theory of credit scoring;
- solutions and technologies specifically designed for handling and extracting patterns from big data;
- interpret the output of advanced analytics techniques used for complex data analytics applications.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- self-manage the development of learning and study skills;
- plan and control effectively for successful completion of a personal workload;
- communicate effectively.
Syllabus
The topics covered in this module will include:
- Introduction and overview of various business analytics techniques;
- Sources of different types of data (e.g. unstructured, network, etc.)
- Using advanced analytics model (e.g. advanced generalized linear models, random forests, deep learning, etc.) to conduct analysis with various types of data to solve different business problem settings
- 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 |
---|---|
Revision | 10 |
Lecture | 24 |
Completion of assessment task | 46 |
Supervised time in studio/workshop | 10 |
Follow-up work | 40 |
Preparation for scheduled sessions | 20 |
Tutorial | 8 |
Total study time | 158 |
Resources & Reading list
Textbooks
Goodfellow, I., Bengio, Y. and Courville, A. (2017). Deep Learning. Available freely online at http://www.deeplearningbook.org/: MIT Press.
Chollet, F. (2017). Deep Learning with Python. Manning Publications.
Thomas, L.C., Crook J.N. and Edelman. (2017). Credit Scoring and Its Applications. Philadelphia, PA, USA: SIAM Press.
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.
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.
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