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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. The module uses real raw data (as well as big data) that require knowledge of data pre-processing prior to systematic data analysis. This includes using suitable analytical statistical tools (e.g. R, SAS, etc.). The module will focus on how different advanced statistical techniques could be used in the marketing analytics area.

Aims and Objectives

Module Aims

to provide impart advanced marketing analytics tools and techniques common within the discipline. You will develop knowledge on how different statistical techniques can be applied 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 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 relevant statistical techniques

Special Features

• Use statistical programming

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

Business and management journals. Journal

Access to journal articles to supplement readings. Journal

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



Class practicals


MethodPercentage contribution
Group presentation 30%
Report  (2500 words) 70%


MethodPercentage contribution
Individual report  (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 software packages 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 mandatory/additional reading 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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