Business Analytics Course

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.

Teaching methods

Teaching will take place on campus, in our multi-mode-enabled lecture theatres. The course will be taught in line with government COVID-19 guidance and restrictions for teaching. This may include social distancing and reduced capacity in lecture theatres. Should government guidelines be in place at the time limit or prevent on-campus teaching, we reserve the right to deliver some or all of the course fully online.

Assessment

• 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.

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 optimization problems and solve them in Excel and AMPL.
• Construct linear regression models for statistical analysis, and estimate them in R.

Week one

You will review some basic probability (distributions, conditional probability, Bayes Theorem, Central Limit Theorem). You’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, the class will discuss some problems from statistics and some fun puzzles/biases from probability and statistics that often appear in the real world.

Week two

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 by various applications in supply chain management, revenue management and finance.

Week three

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.