Advanced Finite Difference Method (FDM) for Computational Finance

The goal of this distance learning course is to approximate the solution of partial differential equations (PDEs) by the Finite Difference Method (FDM) with applications to derivative pricing in computational finance. This course is an in-depth introduction from PDE model specification through efficient and accurate finite difference schemes for a range of one-factor and two-factor option pricing problems. The focus is on understanding the financial, mathematical and numerical skills needed in order to set up the discrete system of equations that we can then implement in C++11, for example.

This course is suitable for front-office and middle-office quant developers who wish to learn the finite difference method for computational finance. The contents of the course is also relevant to other disciplines such as science and engineering.

Who should attend?

This course has been developed so that you can use the theory to solve existing problems as well as applying the knowledge to the pricing of new financial instruments. In particular, the course is for professionals with a strong mathematical background:

  • Financial engineers who design new pricing models
  • Analysts and quants
  • Other professionals who wish to understand and apply advanced numerical methods to derivatives pricing

Tools for Data Science > IBM

What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you’ll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

What is Data Science? > IBM

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.