Decision Science for Business module (BU31017)

​​Develop quantitative and analytical skills for business decision-making, using spreadsheet modelling and other advanced tools to analyse and solve complex real-world problems

Credits
15
Module code
BU31017
Level
3
Semester
Semester 1
School
School of Business
Discipline
Economics

​​Good decisions in business often need more than intuition. They need clear frameworks and analytical tools. That is what this module provides. 

​You'll work across a range of problem types: optimisation, resource allocation, scheduling, and decision-making under uncertainty. For each, you'll learn how to represent the problem, apply the right technique, and interpret the results. 

​Topics include linear programming, transportation and assignment problems, network analysis, decision analysis, and project management. Throughout, you'll build hands-on experience with spreadsheet modelling in Excel and more advanced programming tools. 

​A key goal is communication. You'll learn to explain technical results clearly to both specialist and non-specialist audiences. Lectures introduce new methods each week. Tutorials let you practice with real-world examples. 

​What you will learn 

​In this module, you will: 

  • ​Understand quantitative models and their role in business decision-making 
  • ​Apply analytical concepts to real-world business problems 
  • ​Build spreadsheet models to analyse and solve business challenges 
  • ​Work with linear programming, network analysis, and scheduling methods 
  • ​Communicate technical results clearly to specialist and non-specialist audiences 

​By the end of this module, you will be able to: 

  • ​Explain the key quantitative models used in decision science and their importance in business 
  • ​Apply these models to a range of problems, including optimisation, scheduling, and decision analysis 
  • ​Build and use spreadsheet models to develop solutions for real-world analytics problems 
  • ​Interpret and present the results of quantitative analysis 
  • ​Communicate technical outcomes clearly to varied audiences​

Assignments / assessments

​​Coursework (40%) 

  • ​Assesses proficiency in using spreadsheets and advanced modelling tools to solve analytics problems 

​Written exam (60%) 

  • ​Two-hour exam 
  • ​Assesses understanding of quantitative modelling techniques and ability to interpret results​

Teaching methods / timetable

  • ​​Weekly lectures covering quantitative methods and real-world business problems 
  • ​Tutorials with problem-solving exercises and discussion 
  • ​Practical training in spreadsheet modelling and advanced programming tools 

​Regular office hours are available with lecturers and tutors for additional support and guidance on assessments.​ 

Courses

This module is available on the following courses: