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Distance Learning for Financial Engineers

Datasim offers several distance learning courses which can be followed at your own location at your own pace. With the distance learning courses you have access to a special section on our website/forum that contains various resources:

  • Screen capture videos of the presentations/demonstrations
  • Exercises
  • Source code
  • Discussion forum where you can ask questions and interact with other students

Further you will receive a binder with the presentation slides and exercises and depending on the distance learning course one or more reference books. 'Lifelong acces to the resources'.

Click here for some example videos and exercises


Computational and Quantitative Finance in C++

The hands-on distance-learning course is a thorough introduction to the C++ object-oriented language and how to apply it to designing and developing applications in Quantitative Finance. Included is also C++ 11.It is intended for busy quants, quant developers and financial engineers who are interested in learning in their own time and who wish to avail of the relevant resources after course completion, for example, source code, articles and communication with other course participants.

For those who already have knowledge and experience with basic C++, there is the possibility to follow only the 2nd half of the course.

More information about these Distance Learning courses:


Advanced Finite Difference Method for Quantitative Finance: Theory, Applications and Computation

This distance learning course shows how to use the Finite Difference Method (FDM) to price a range of one-factor and many-factor option pricing models for equity and interest rate problems that we specify as partial differential equations (PDEs). We introduce and elaborate modern and robust finite difference methods that solve pricing problems and that remain stable and accurate for various combinations of input parameters, payoff functions and boundary conditions.

This course discusses all aspects of option pricing, starting from the PDE specification of the model through to defining robust and appropriate FD schemes tha we then use to price multi-factor PDE to ensure good accuracy and stability. The contents of the course have been updated and revised to reflect new results and developments in the field.

More information about this Distance Learning course:

Mathematics Foundations course

The goal of this distance learning course is to introduce and elaborate the mathematical concepts, methods and algorithms that lay the foundations for quantitative finance applications. The course contents are applicable to a wide range of real-world domains, including derivatives pricing and risk management applications. It lays the mathematical foundations for careers in a number of areas in the financial world.

The course is intended for novice quantitative analysts and developers who are working in quantitative finance. The level of this course is similar to what a second year mathematics course would offer. It is also suitable for anyone wishing to learn mathematics up to secon-year university level.

More information about this Distance Learning course:


Advanced C++ - Programming Models, Libraries and Parallel Computation

The goal of this hands-on distance learning course is to learn the most advanced features of object-oriented and generic programming in C++, the STL and boost libraries and modern software design methods. We have developed this course for those professionals working in business, engineering and other areas who are involved in software development.

More information about this Distance Learning course:

Advanced C# for Computational Finance and Derivatives' Pricing

The goal of this hands-on distance learning course is to apply the C# object-oriented language and the libraries in the .NET framework to the design and implementation of flexible and robust applications. The focus of the course is on using object-oriented and generic programming models in combination with useful libraries to help the quant developer produce running code for a range of pricing applications for equities and interest rate products. We also discuss how to implement the Gamma (GOF) design patterns in C# and we have seen a many-fold productivity improvement because the .NET libraries support them or can be easily adapted to support them.

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C++ and its Application in Finance - For Universities and Banking

This is one of the few distance learning C++ courses that takes you all the way from basic C++ through to object-oriented programming and creating applications in combination with the Standard Template Library (STL) and boost library. The course consists of dedicated modules each of which dealing with one particular aspect of the language. The course has been created and is supported by Dr. Daniel J. Duffy who has been working with C++ since 1989 and who has currently written seven books on object-oriented design, C++ and their applications. C++ 11 is also discussed.

More information about this Distance Learning course:

Creating Add-in Excel Applications using C++ and C# - Interoperability Software Tools and Applications

The goal of this distance learning course is to apply C++ and C# to help in the creation of Automation and COM add-ins for Excel and to show how to develop worksheet functions and Excel-based applications in computational finance. We introduce and elaborate the tools, libraries and language features that are needed to create efficient and robust add-ins. In addition, we achieve a high level of interoperability between C++, C# and VBA.

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FDE/FDM Methods in Computational Finance: Theory, Algorithms and Applications

The goal of this introducory distance learning course is to introduce the finite difference method and its applications to computational finance. We bring together in one place all the methods and techniques that are used to price and hedge derivatives that are modelled using partial differential equations (PDE) in finance and that we approximate using the Finite Difference Method (FDM). We discuss all aspects of the problem, from PDE definition to numerical schemes and system assembly.

This course should appeal to a wide audience in finance. Having followed this course you will be in a position to understand the literature on PDE/FDM methods. It also lays the foundation for more advanced methods for multi-factor and nonlinear models that are used in computational finance.

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Numerical Methods for Computational Finance, Engineering and Science

Many advanced and complex problems in engineering, finance and science cannot be solved analytically and for this reason numerical methods have been developed to approximate the solution of these problems and which can then be implemented in a digital computer. There are many kinds of numerical methods that are used to solve a range of problems and in this course we discuss a number of these techniques. The contents correspond to what is taught in university courses in Numerical Analysis in combination with the implementation of numerical algorithms in C++ using the standard STL and Boost libraries. Finally, we apply all these techniques in numerous examples and applications. To our knowledge, this is the only course that offers this integrated set of features. The course discusses topics corresponding to second and third year university level.

More information about this Distance Learning course: