Online pre-study modules
Before the programme begins you will be expected to complete some online pre-study modules which are delivered through The Hub, Imperial College Business School’s virtual learning environment. These modules will be available to students who have accepted an offer of admission from July onwards, and are designed to give you a basic knowledge of areas which will be covered later in the programme.
Please note: all students complete these modules online (on campus and online students).
This online learning module is delivered as three separate sections.
The Finance portion will introduce you to basic concepts in Finance and Financial Valuation models.
The Accounting portion gives you an introduction to the basic financial statements, namely the balance sheet, the income statement and the cash flow statement. It is a legal requirement for companies and large organisations to report their financial status through these statements.
The Maths portion provides a refresher on calculus and linear algebra.
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.
In this short module, we will explain the key academic issues relating to deliberate and accidental plagiarism including what it is, the different types of plagiarism and the notion of academic integrity, as well as providing advice on how to avoid plagiarism in your work. You are required to complete and pass the online plagiarism awareness test before the programme begins.
Core modules rapidly build the theoretical and programming foundations you need for analytics. They are the backbone of the programme and will provide you with a solid combination of tools and knowledge.
These modules are studied on campus by full-time students, and online for those studying the part-time programme.
This module introduces you to the design of algorithms and data structures for computational problem solving. The design of efficient computational methods for sifting through large data sets lies in the core of modern technological innovation ranging from search engines and social networks to healthcare, energy and finance. The module will familiarise you with key algorithm design paradigms and central concepts of computational complexity and running-time analysis. You will develop a working knowledge of basic algorithms (for problems such as search, sorting, and shortest paths) and data structures, along with the necessary programming constructs. The module will also serve as an introduction to the Python programming language, with the goal of becoming proficient in organizing and writing programs for practical problem-solving.
This module introduces you to practical usage of databases with the main emphasis on SQL and related technologies. As big data problems are more and more prevalent for business, this module introduces basics of their processing with Apache Spark – a versatile, big data processing engine. This module will use PostgreSQL – one of the most popular and powerful object-relational database management systems.
This module will teach you basic analytics skills and methodologies for large-scale data analysis. It will focus on the practical use of these skills and methodologies to solve real world problems. The goal of the module is to enable you to be data-savvy, analytically minded and coding-literate problem solvers.
This rigorous module will teach you concepts of probability, statistics and linear algebra. You will learn to summarise and analyse data using statistical concepts and understand linear algebra to represent and manipulate data. This will ensure that you build a solid foundation of the mathematical concepts necessary to succeed on the programme. This module will also serve as an introduction to R.
Networks arise in many different contexts and a vast amount of networked data is now generated. For instance social networks such as Facebook and Twitter generate immense amount of data that is invaluable to marketers and businesses to obtain product feedback and do targeted marketing. This module covers algorithms, analysis and interpretation of network data and relationships.
Optimisation plays a key role in statistical, machine learning and business operational models. This module trains students on the fundamental optimisation tools available to the modeller and business analyst. You will obtain hands-on experience with the key modelling (AIMMS, GAMS, AMPL) and optimisation (CPLEX, GUROBI) software packages.
This module provides an introduction to multiple regression analysis and related methods for analysing data in business applications.
This module covers a range of state-of-the-art methods and tools to visualise high-dimensional data. You will learn how humans process and perceive images, be introduced to the best practices for visualising patterns in large data sets, and get hands-on experience with state-of-the-art visualisation software packages.
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.