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
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- communicate effectively.
- self-manage the development of learning and study skills;
- plan and control effectively for successful completion of a personal workload;
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- identify the analytical models;
- assess the relevance of software packages outputs to the decisions being addressed;
- handle various decisions with complex/big data;
- use basic heuristics to set booking limits;
- appropriate for analysing the work with relevant software packages to develop credit scoring solutions;
- work with current software packages to create models using complex data sources.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- underlying theory of credit scoring;
- solutions and technologies specifically designed for handling and extracting patterns from big data;
- solutions basic principles of pricing and revenue management;
- interpret the output of advanced analytics techniques used for complex data analytics applications.
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 |
Preparation for scheduled sessions | 20 |
Supervised time in studio/workshop | 10 |
Lecture | 24 |
Completion of assessment task | 46 |
Tutorial | 8 |
Follow-up work | 40 |
Total study time | 158 |
Resources & Reading list
Textbooks
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.
Thomas, L.C., Crook J.N. and Edelman. (2017). Credit Scoring and Its Applications. Philadelphia, PA, USA: SIAM Press.
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.
Chollet, F. (2017). Deep Learning with Python. Manning Publications.
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