Prescriptive and Predictive Analytics module (BU51039)

Learn about business analytics and be introduced to a range of techniques to help you predict future outcomes by using data to optimise your decision making.

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Module code


Explore the principles and methods of predictive and prescriptive analytics, by using them together you will develop relevant skills in statistical modelling and how to choose relevant forecasting models. Use machine learning techniques and corresponding applications to help solve a variety of business related decision problems.

This module provides you with the tools to transform data into actionable insights, fostering growth and innovation which will help you make informed decisions and boost your problem solving skills.

What you will learn

In this module, you will:

  • explore and prepare data
  • optimisation techniques
  • decision support systems
  • analyse scenarios and create simulations
  • consider the ethical issues and limits of analytics

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

  • describe and use CART (Classification and Regression Trees), KNN (K-Nearest Neighbours) and Random Forests algorithms
  • choose between different kinds of applications to create simulation to solve complex business problems

Assignments / assessment

Class test (40%)

  • 1 hour in length

Assignment (60%)

  • 2,000 words
  • 30 hours of effort expected

This module does not have a final exam.

Teaching methods / timetable

You should expect weekly lectures and in-person tutorials and be expected to have completed the reading and tasks in preparation of contributing to the class.

Week Topics covered
1 Linear optimisation: model building and graphical solution
2 Linear optimisation: minimisation problem and special Linear Programming (LP) problems
3 Linear optimisation: computer solution and sensitivity analysis
4 Applications of linear optimisation
5 Integer optimisation
6 Statistical distributions and linear regression
7 Regression and classification
8 CART, KNN, and Random Forests
9 Support vector machines and Naive Bayes algorithm
10 Simulations



This module is available on following courses: