The University of Southampton

MANG6260 Using Big Data for Consultancy

Module Overview

This module focuses on in-depth and advanced statistical tools for analysing data. It incorporates techniques such as advanced regression, semiparametric regression, generalised linear regression and structural equation modelling. The module uses real raw data (as well as big data) that require knowledge of data cleaning, data organisation, basic data management prior to systematic data analysis. This includes introduction to R graphics for data visualisation. The module also covers the importance of statistical assumptions and their implications for choice of techniques and data interpretation.

Aims and Objectives

Module Aims

To provide impart advanced marketing analytics tools and techniques common within the discipline. You will develop hands on experience with these tools using R statistical programming software to ensure that they understand these tools conceptually and can apply them in realistic situations.

Learning Outcomes

Knowledge and Understanding

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

  • The different types of marketing analytics activities involved advanced analytical techniques in contemporary organisations;
  • The complexities of collecting, integrating, processing and managing text and network data from a wide range of internal and external sources;
  • How various advanced analytical techniques can be used to uncover the potential of various types of data to gain actionable insights and support marketing decisions.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Select and apply suitable methods to collect data, and then integrate, prepare and manage these data;
  • Critically analyse, interpret, organise and use visual tools to present quantitative data;
  • Evaluate and apply advanced analytical techniques to solve Marketing Analytics problems, and then reflect upon the selected approach;
  • Derive actionable insights through the results of analyses and communicate them to a non-technical audience.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Communicate ideas and arguments fluently and effectively in a variety of written formats;
  • Communicate ideas and arguments orally and through formal presentations;
  • Work effectively in a team and recognise problems associated with team working;
  • Manage yourself, time and resources effectively;
  • Use computing and IT resources effectively;
  • Demonstrate confidence in your own ability to learn new concepts.


The topics covered include: • How this module integrates with previous analytics modules; Revision of necessary fundamental content from previous semester; Overview of expected goals; • The management problems associated with, data necessary for, data processing needed for, concepts underlying, practical implementation of, and interpretation and communication of results from: • Conjoint analysis • Multi-dimensional scaling • Cluster analysis • Factor analysis • Reliability analysis • Other relevant statistical techniques • The precise statistical tools covered may change slightly in response to what is determined to be the most relevant advanced statistical techniques based on academic and industry practice. Only small modifications from the above list are anticipated over the coming years.

Special Features

• Use of R statistical programming language (free software) • R-studio • Eclipse platform • R graphics

Learning and Teaching

Teaching and learning methods

Teaching methods include: • Lectures explaining the problems and concepts • Laboratory sessions where the tools can be practiced and applied • Guided independent study Through all delivery methods the content and presentation of the module will be accessible and inclusive. Learning activities include: • An assignment • Laboratory work • Case study / problem solving activities • Private study

Independent Study126
Total study time150

Resources & Reading list

Keele, L. J (2008). Semiparametric Regression for the Social Sciences. 

Business and management journals. Journal

Software. This module is completely taught in computer laboratory (either Windows/Apple) with pre-installed latest version of R statistical programming and R-Studio.

Kline, R.B (2011). Principles and Practice of Structural Equation Modeling. 

Robert I. Kabacoff (2011). R in Action: Data Analysis and Graphics with R. 

Access to journal articles to supplement readings. Journal



Class practicals


MethodPercentage contribution
Group presentation 30%
Individual Coursework  (2500 words) 70%


MethodPercentage contribution
Individual Coursework  (3000 words) 100%


MethodPercentage contribution
Individual Coursework  (2500 words) 100%

Repeat Information

Repeat type: Internal & External


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:


Students would need access to computers and use R. software for the module.


Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the core/recommended text 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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