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 and big data solutions and technologies. The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.

Aims and Objectives

Module Aims

To introduce the underlying principles of pricing and revenue management and how they can be implemented in practice, the concept of developing scorecards using advanced data mining techniques, and solutions and technologies designed specifically for handling big data, including NoSQL and cloud computing.

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 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
  • Use NoSQL to handle various types of queries with big data sets
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, in both oral and written form, using and justifying argument within reports; presentations and debates


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, document modelling with NoSQL, principles of cloud computing.

Special Features


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 • An assignment (individual written coursework on NoSQL) • Case study / problem solving activities • In class debate and discussion • Private study • Use of video and online materials

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

Resources & Reading list

Hastie, T., Tibshirani, R. and Friedman, J. (2015). The Principles of Statistical Learning. 

Computer Resource. This module will require the weekly use of a computer lab equipped with the latest version of SAS Enterprise Miner, R and Anaconda Python for four weeks

Thomas, L.C., Crook J.N. and Edelman (2002). Credit Scoring and Its Applications. 

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, Springer. 

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



MethodPercentage contribution
Examination  (2 hours) 70%
Individual assignment  (1500 words) 30%


MethodPercentage contribution
Individual assignment  (2500 words) 100%


MethodPercentage contribution
Examination  (3 hours) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: MANG3056 Data Mining for Marketing 2016-17

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