Online pre-study modules
Before the programme begins you will start developing your knowledge and skills by studying a series of online pre-study modules, delivered through The Hub, our purpose-built learning environment. The Hub provides an interactive, engaging, and flexible learning environment, giving you access to all the materials you need and connecting you with your peers and tutors. The modules are designed to give you a basic understanding of areas which will be covered in more detail throughout the programme.
These modules will be available from July onwards to students who have accepted an offer of admission.
Develop your understanding of the basic financial statements including the balance sheet, the income statement and the cash flow statement. You will learn about the basic pro-forma of these statements, how they are prepared, and their limitations. This knowledge will be useful for you to be able to interpret and analyse information to support good decision-making.
Foundations for Career Success
This online module helps you prepare for roles in the finance sector. It will give you an overview of career options within the sector, help define your application strategy and give you the tools to develop your market knowledge and commercial awareness.
Please note: you will not be able to access the services offered by Imperial College Business School Careers until you have completed this module.
Finance Careers Primer
This online module accelerates your career development and ensures you are ready to work with Careers as soon as you set foot on campus. You will examine your key skills, interests, values and motivators and learn how to use that knowledge to build a CV. After uploading your CV using our template, you will be ready to hit the ground running in September.
Introduction to Finance
This online learning module introduces you to basic concepts in finance and financial valuation models.
When you have completed the module you will be able to:
Use the time value of money to value assets
Understand how risky cash flows are valued
Calculate spot and forward rates
Understand how a yield curve is obtained
Understand how portfolio selection problem is solved
Implement the CAPM equation to estimate the rate of return on risky assets
Introduction to Maths
This module provides a refresh of mathematical techniques that you will have encountered in your earlier studies.
The module will enable you to:
- Take derivatives and integrate simple commonly encountered functions
- Employ product and chain rules and integrate by parts
- Understand and manipulate simple equations involving vectors and matrices
- Be familiar with commonly encountered matrix functions such as determinants and eigenvectors
- Understand simple properties of linear ordinary differential equations
How would you define plagiarism? Do you know what plagiarism is? Do you know there are different types of plagiarism? Many students students think plagiarism is when you ‘cut and paste’ or copy other people’s work. However, this is only one half of a definition of plagiarism. This module provides a full understanding of what plagiarism is, and why it is an academic offence.
These optional interactive modules provide practical advice on working in groups, delivering presentations, and time management. They contain videos of recent students describing their experiences of studying at the Business School, some of the challenges they faced, and suggestions on how to overcome these.
An English Language Development section is also available for non-native speakers of English who wish to practice their skills and develop subject-specific vocabulary. You can upload your writing for detailed feedback from a language tutor.
Foundation module - Foundations in Financial Technology
Once you are on campus in September, the programme begins with a foundation module, made up of six components that introduce the tools of modern finance, enhance your programming knowledge and help you to develop your commercial awareness and career development skills.
Applications of R and Databases for Finance
The module is designed for students with little programming experience and provides the foundations of programming in R. Variables, arrays, conditional statements, loops, and functions are explained. Furthermore, the module will focus on modelling, leveraging the skills of R that apply to modern financial markets, from simple linear regression and estimation to volatility modelling, asset pricing and other relevant topics in finance.
As big data problems are more and more prevalent for business, this module also looks at the practical usage of databases with the main emphasis on SQL and how to efficiently process large sets of data.
After a brief overview of the aims of financial intermediation, this component focuses on the principal user of capital; the corporation. You will examine its legal and organisational structure, and discuss how it reports its activities in the annual accounts. Probably a company’s most important decision is which projects to invest in. After being introduced to the time value of money, you will discuss various investment or capital budgeting decision rules.
In the second half of the component, you will focus on techniques to value securities and in particular common shares, both theoretically and through a number of case examples. You will discuss how financial analysts might approach this problem, and techniques that are used to verify or corroborate their estimates of fair value. You will also examine the choice of project financing, how the capital is sourced and how it may an impact on the value of the project to the investors. This is referred to as the capital structure question.
Markets and Securities
This component provides a broad overview of key financial markets; Stocks, Bonds and Derivatives and introduces the concepts of risk and return and how diversification influences risk and return.
This component of the module will serve as an introduction to the Python programming language, with the goal of becoming proficient in organising and writing medium-sized programmes for practical problem-solving cover debugging and good programming practices.
Career and Professional Development
Delivered by a combination of Imperial College Business School Careers, external consultants and professionals working in the sector, this module runs over both the autumn and spring terms, with a focus in the autumn term on skills needed to secure roles and moving on to leadership skills in the spring to prepare you for the recruitment process and help you succeed in securing a role.
Throughout the autumn, spring and summer terms, you will study core modules across key areas to build on previous experience while introducing new and challenging disciplines.
Accounting and Corporate Finance
Corporate finance is at the heart of investment banking and crucial to running a successful business in any sector. In this module you cover all the key elements of company finance, valuation and risk management. You will learn to understand how to assess individual investment projects, value companies, and evaluate the sources of finance available to companies.
Big Data in Finance I
Over the past few years, there has been an explosion of interest in the use of large datasets and new empirical techniques to make financial decisions of all kinds. In this elective we examine how the combination of large datasets, empirical techniques including machine learning, and insights from behavioural finance are helping in making more efficient financial decisions. Two areas in which progress has been especially rapid are credit analytics (predicting default in personal loans, mortgages, and firms), and asset management. This elective focuses on these specific markets, considering them from supply, demand, and regulatory perspectives. You will build empirical models to illustrate important concepts throughout the elective.
Big Data in Finance II
Big Data in Finance II builds on and complements insights from the previous module. The module will focus on three key techniques in Big Data analysis and machine learning, and their applications to finance. First, you will explore unsupervised machine learning models (e.g. clustering algorithms) and their applications to recommendation algorithms in finance. Second, expanding the introductory material on neural networks in Big Data in Finance I, the module will develop this material further to cover Deep Learning techniques, which will then, as in Big Data in Finance I, be applied to credit scoring and/or portfolio choice problems. Third, the module will introduce and discuss reinforcement learning models, with potential applications to portfolio selection and trading strategies.
“The skills students learn on this module are directly applicable for anyone wanting to work in a fintech business that involves credit scoring or asset management.”
Blockchain and Applications
Through this module, you will gain an understanding of the core value proposition of blockchain technology and how its etymology drives the new zeitgeist.
You will also learn the canonical technology (Bitcoin & Ethereum), their challenges along with current thinking about how to overcome them, while also gaining insight on raising capital from and valuing the token-based economy.
Ethics & Professional Standards in Finance (online)
This module will to introduce you to corporate responsibility and professional standards for financial analysts. You will be taken through a review of the key factors and responsibilities for ethical practice in finance. There are eight sections in total for students to complete. Each section will include video commentary, a web-based lecture, suggested readings, practical examples and discussion questions to test the key concepts learned in that section.
Financial Econometrics in R/Python
This module builds on the introductory module and introduces basic programming in R to perform statistical analysis using the R Studio editor. You will apply your skills to empirical finance applications like stock market predictability using different factors from the literature. The module will also build on basic programming skills in Python to perform similar analysis but also as applied to financial modelling like options pricing and financial modelling.
Investments and Portfolio Management
This module provides you with a critical understanding of techniques used for investments and portfolio management. The teaching is accompanied by case studies and realistic practical examples that you will solve each week using programming software such as R. By the end of the module you will be able to implement trading strategies and portfolio construction methods in a wide range of assets.
Imperial College Business School reserves the right to alter modules whenever they need to be amended or improved. Faculty may also change as and when required.