The University of Southampton

MANG6329 Data Analytics

Module Overview

The aim of this module is to give you a practical grounding in the skills and techniques necessary to conduct data and marketing analytics. Some basic statistical models are introduced in this module. Also, this module is designed to get you over the basic hurdles you will face when beginning to learn the data analytics and management techniques using an advanced analytical software package (e.g. R, SAS). It will cover some of the basic tasks that you face as a marketing and data analyst and will put you in a position to extend your knowledge of applying your analytical techniques.

Aims and Objectives

Module Aims

This module aims to introduce the basic knowledge and techniques used in marketing analytics. It will focus on providing an understanding of some basic statistical models and the skills that underpin more advanced marketing analytic techniques covered later.

Learning Outcomes

Knowledge and Understanding

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

  • How various analytical techniques can be used to uncover the potential of data to gain actionable insights and support marketing decisions
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Communicate ideas and arguments fluently and effectively using a written report;
  • 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.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Integrate, prepare, and manage quantitative data
  • Critically analyse, interpret, organise and present quantitative data


The topics covered include: • Basic statistical approaches: basic descriptive statistics, standard distributions, hypothesis testing, correlation analysis, regression analysis • Sources of quantitative data • Evaluation of data (input): Reading data, exploring data • Use of data (process): Transforming data, Manipulating Data, managing data • Presentation of data (output): Graphics and tables • The use of analytical software packages (e.g. R or SAS) to conduct statistical analysis

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 Learning activities include • Coursework • Laboratory work • Case study / problem solving activities • Private study

Independent Study126
Total study time150

Resources & Reading list

DeGroot and Schervish (2013). Probability and Statistics. 

Alan Agresti and Barbara Finlay (2008). Statistical Methods for the Social Sciences. 

Computer labs equipped with R/SAS software. Software

Diez, D. M., C. D. Barr, M. Cetinkaya-Rundel (2015). OpenIntro Statistics. 

Gailmard, S (2014). Statistical Modeling and Inference for Social Science. 

Braun, W. J., D. J. Murdoch (2007). A First Course in Statistical Programming with R. 

Gelman, A., J. Hill (2007). Data Analysis using Regression and Multilevel/Hierarchical Models. 

Chang, W. (2013). R Graphics Cookbook. 

Faraway, J. J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. 

Faraway, J. J. (2014). Linear Models with R. 

Laura M. Chihara, Tim C. Hesterberg (2011). Mathematical Statistics with Resampling and R. 

Fox, J., S. Weisberg (2011). An R Companion to Applied Regression. 

Fox, J. (2016). Applied Regression Analysis and Generalized Linear Models. 

Newbold, P., W.L. Carlson, B.M. Thorne (2013). Statistics for Business and Economics. 

Chapman, C.N., Feit, E.M. (2015). R for Marketing Research and Analytics. 

Winston, W. L. (2014). Marketing Analytics: Data-Driven Techniques with Microsoft Excel. 



Lab work


MethodPercentage contribution
Examination  (2 hours) 50%
Group Assignment  (2500 words) 50%


MethodPercentage contribution
Individual assignment  (3000 words) 100%


MethodPercentage contribution
Individual assignment  (3000 words) 100%
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