Gain business analytics skills and understand how to apply them to real-world situations
The goal of this course is to introduce students to three different areas in analytics, with a focus on prescriptive analytics. This course will focus on a brief introduction to probability/statistics, decision trees and optimisation and networks with applications in logistics and organisations.
By the end of the course you will:
Understand some basic probability concepts such as distributions, conditional probability etc. and apply them in real life situations
Build and solve decision trees for modelling strategic real-world problems under uncertainty
Understand how to formulate various kinds of optimisation problems and solve them in Excel and AMPL
Model business phenomenon using networks and obtain insights
We review some basic probability (distributions, conditional probability, Bayes Theorem, Central Limit Theorem, etc.). We’ll also learn how to formulate real-world strategic problems under uncertainty as decision trees and how to solve these trees using an Excel Addin. Finally, if time permits we’ll discuss some problems from statistics and some fun puzzles / biases from probability and statistics that often appear in the real world
We will learn how to formulate managerial decision problems as linear and discrete optimization problems, what the properties of these optimization problems are, and how these optimization problems can be solved in Excel and AMPL. The methodology will be accompanied with various applications in supply chain management, revenue management and finance
We first introduce the language and tools of graph theory and their use in modelling many business phenomena. We specifically study their usage in identifying importance in a collection of entities based on relationships. The second application we study is in logistics and supply chain management. This module will also introduce algorithmic concepts.
Entry requirements: Applicants for this programme will be expected to have some prior exposure or previous learning in calculus, linear algebra and probability.
For more details view our entry requirements.
Assignment (25% of final mark)
Assignment on linear and integer programming (25% of final mark)
Final examination (100% MCQ) - (50% of final mark)
Imperial College London will issue an official transcript with a final overall numerical mark – a breakdown of results will not be provided.
Imperial College London reserves the right to change or alter the courses offered without notice.