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The University of Southampton

MANG3073 Analytics in Action

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.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • 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.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • use basic heuristics to set booking limits;
  • implement optimal pricing models;
  • work with relevant software packages to develop credit scoring solutions;
  • 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:

  • self-manage the development of learning and study skills;
  • plan and control effectively for successful completion of a personal workload;
  • communicate effectively.


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

Completion of assessment task46
Supervised time in studio/workshop4
Preparation for scheduled sessions20
Follow-up work40
Total study time150

Resources & Reading list

Goodfellow, I., Bengio, Y. and Courville, A. (2017). Deep Learning. 

Talluri, K.T. and van Ryzin, G.J. (2005). The Theory and Practice of Revenue Management. 

Thomas, L.C., Crook J.N. and Edelman. (2017). Credit Scoring and Its Applications. 

Gaurav, V. (2013). Getting started with NoSQL: Your guide to the world and technology of NoSQL. 

Chollet, F. (2017). Deep Learning with Python. 

Hastie, T., Tibshirani, R. and Friedman, J. (2013). The Elements of Statistical Learning. 



In-class activities


MethodPercentage contribution
Report  (2000 words) 60%
Report  (1500 words) 40%


MethodPercentage contribution
Report  (2500 words) 100%


MethodPercentage contribution
Report  (2500 words) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: MANG3056


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the Mandatory/Additional Reading as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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