Gain business analytics skills and understand how to apply them to real-world situations
The goal of the 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 basic probability concepts such as distributions, conditional probability etc. and be familiar with applications to real life domains such as revenue management. You will also understand what a stochastic process is and how to run a Monte-Carlo simulation
Understand how to formulate various kinds of optimization problems and solve them in Excel and AMPL
Construct linear regression models for statistical analysis and estimate them in R
You will review some basic probability (distributions, conditional probability, Bayes Theorem, Central Limit Theorem, etc.). You'll also learn what a stochastic process is and how to perform a Monte-Carlo simulation. Finally, if time permits, the class will discuss some problems from statistics and some fun puzzles/biases from probability and statistics that often appear in the real world.
You 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.
You will learn about the specification and estimation of the linear regression model, from model assumptions, coefficient estimation to model inference and predictions. Using empirical applications drawn from economics and related fields, you will learn how these approaches can be successfully applied in practice.
Academic level: Equivalent to an undergraduate course
Suggested credit level: 3 – 4 US / 7.5 ECTS credits. Your home institution will determine how much credit is awarded
Entry requirements: Applicants for this business analytics short course 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.