Distance Learning Applied Numerical Methods with Python and Python Libraries

Foundations for Computational Finance and Machine Learning


Originator and coach: Dr. Daniel J. Duffy

Click here for course contents.

The goal of this hands-on (online) course is to learn the major packages in the Python programming language and to use them in computational finance,  Machine Learning (ML) and other numerically-intensive applications.

The course consists of ten modules:

  1. Object-oriented Python.
  2. Functional programming in Python.
  3. Essential data structures.
  4. Fundamental numerical methods.
  5. Advanced numerical methods.
  6. Numerical solution of ODEs/PDEs.
  7. Python for Computational Finance.
  8. Introduction to Machine Learning (ML).
  9. Auxiliary libraries.
  10. Design Patterns.

In short, the main goal is to gain hands-on programming experience with Python and its libraries and to apply that knowledge to several application areas.


Why this Course?

This course represents the fusion of numerical mathematics, Python coding and libraries and using them to write applications for computational finance, statistics, Machine Learning and more. The goal is to gain insights on how to define a problem, find a suitable algorithm to solve it and then program a solution to that problem in Python.  In particular, we reduce the effort it takes to understand logical concepts and their applicability and to learn how to apply them in practice. Some examples and reasons for taking the course are:

  • Learn numerical analysis and methods through programming in Python.
  • Use Python libraries in your modules and classes.
  • Learn computational finance and Black Scholes option pricing.
  • Using Python’s Machine Learning (ML) libraries.
  • Write prototype Python code before porting it to production C++.
  • Design structured and maintainable Python applications using Design Patterns.
  • This course is the perfect companion to Daniel Duffy’s pure, applied and numerical mathematics courses. See Distance Learning Applied numerical methods

There are of course only some of the possible use cases.


Topics covered

  • Python refresher.
  • Object-oriented and functional programming in Python.
  • Design patterns and maintainable code.
  • Fundamental data structures for numerical methods.
  • Numerical solution of ordinary differential equations (ODEs) and partial differential equations (PDEs).
  • Statistics and Optimisation.
  • Linear algebra and matrix libraries.
  • Python for Computational Finance (PDE, Monte Carlo, lattices).
  • Machine Learning.
  • Advanced statistical functions.


Benefits for the Student

This course encapsulates the full lifecycle on how to solve computationally and numerically-intensive applications in Python. Each module is in principle independent from the other modules.  In this way it is possible to improve skills and understanding of the topics in less time than otherwise.

Finally, a  number of the topics are new in the current context, for example numerical processes in computational finance and designing structured Python code.


Intended Audience

Something for everybody! This course assumes that you have some knowledge of Python. The focus is on using Python in applications and not just on learning syntax. Nonetheless, we devote a section to a refresher on essential language features to help you prepare for the course topics.

The duration varies between two and four months.


What do you receive?

Full access to videos, code, hard copy of video text, small quizzes, exercises and student project.

The course can include an initial Skype session to determine how to set up the course and a project is also possible if the student wishes. Regular interaction via emails and Skype.

The student receives a certificate at course completion.


Click here for course contents.


Related Courses

Online  course “Applied Numerical Methods” that discusses the mathematical and numerical foundations of the Python libraries that we examine in the current course.


You will get a discount to Euro 1.195,00 if you subscribe to this course before 1 August 2019 (early bird)

For existing student the price is Euro 695,00(no VAT if you are outside EU). The courses remain accessible forever and you can plan your own schedule but in general it should be possible to complete a course (including exercises and mini-project) in 2-4 months.  See also our Frequently Asked Questions (FAQ) on the Datasim site. Please contact dduffy@datasim.nl

Course price

Price (excl. VAT) € 1395.00
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Distance Learning Applied Numerical Methods with Python and Python Libraries

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