Skip to main navigationSkip to main content
The University of Southampton

MANG1032 Foundations of Business Analytics (UOSM)

Module Overview

Business analytics is closely related to management science and operational research. It refers to the use of statistical methods and models as well as empirical data to support the process of making business decisions. This module provides general knowledge about business analytics, illustrated with case studies and examples from various industries. In order to use the above mentioned methods and models effectively, one needs to understand the underlying probability theory and statistics. Thus, the module also provides a basic knowledge of statistics and probability. It introduces such concepts as random variables and probability distributions, and it covers the basics of statistical analysis and inference.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

  • the role of business analytics in generating value from data;
  • the scope and nature of different types of business analytics techniques;
  • the role of probability theory in modelling uncertainty;
  • basic concepts of statistical analysis and inference models.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • apply basic business analytics techniques to business problems;
  • use probability distributions to model uncertainty in real life problems;
  • apply basic statistical analysis and inference models to business problems.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • learn the basics of mathematical arguments;
  • communicate mathematical ideas effectively both in oral and written form;
  • use a variety of visual models for representing the results of your analysis.


The topics covered in this module will include: • The role of business analytics in generating value from data based on case studies from industry; • Various types of business analytics techniques, i.e. descriptive, predictive, and prescriptive, along with relevant examples and case studies; • Introduction to the concept of modelling; • Important concepts of probability theory, including random variables, expectation, and probability distributions; • Statistical inference and relevant models; • An introduction to clustering; • Applications of selected modelling approaches.

Learning and Teaching

Teaching and learning methods

Teaching methods include: • Lectures • Interactive case studies • Problem-solving activities • Directed reading • Private/guided study Learning activities include: • Introductory lectures • Case study/problem solving activities • In class debate and discussion • Private study • Use of video and online materials

Preparation for scheduled sessions10
Follow-up work30
Total study time150

Resources & Reading list

Moore, D.S., McCabe, G.P. and Craig, B. (2014). Introduction to the Practice of Statistics. 

Evans, J.R (2013). Business Analytics: Methods, Models and Decisions. 



Weekly online exercises


MethodPercentage contribution
Examination 20%
Examination  (2 hours) 80%


MethodPercentage contribution
Examination  (2 hours) 100%


MethodPercentage contribution
Examination  (2 hours) 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:


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

Share this module Share this on Facebook Share this on Twitter Share this on Weibo
Privacy Settings