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 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.
You will review some basic probability (distributions, conditional probability, Bayes Theorem, Central Limit Theorem, etc.). 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.
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.
Covid-19 safety and multi-mode delivery
The course will be taught in line with government COVID-19 guidance and restrictions for teaching, including social distancing and reduced capacity in lecture theatres. Students can choose to study fully online or through our multi-mode delivery format, which enables you to learn safely via a mixture of on-campus and remote online teaching. If you choose to study through multi-mode delivery, a minimum of 50% of the lectures will be delivered on campus. Should government guidelines in place at the time prevent on-campus teaching, we reserve the right to deliver the course fully online.
Lecture content and class material
Lecture content and class material will be made available through an interactive online teaching and learning hub – The Summer School Hub.
Workshops will use case studies, structured discussions and in-class exercises to demonstrate the application of concepts as you learn. You will also be expected to complete significant private study, exam preparation and group assignment work outside of your scheduled classes.
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.