The University of Southampton
Courses

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

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.
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.
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.

Syllabus

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

TypeHours
Teaching24
Independent Study126
Total study time150

Resources & Reading list

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

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

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

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

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

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

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

Computer labs equipped with R/SAS software. Software

DeGroot and Schervish (2013). Probability and Statistics. 

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

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

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

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

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

Access to journal articles to supplement readings. Other Library Support required

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

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

Assessment

Formative

Peer Group Feedback

Summative

MethodPercentage contribution
Group Coursework  (2500 words) 50%
Written exam  (2 hours) 50%

Repeat

MethodPercentage contribution
Individual Coursework  (3000 words) 100%

Referral

MethodPercentage contribution
Individual Coursework  (3000 words) 100%

Repeat Information

Repeat type: Internal & External

Costs

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:

Textbooks

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 www.calendar.soton.ac.uk.

Share this module Share this on Facebook Share this on Google+ Share this on Twitter Share this on Weibo

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×