The main online course providers that you want to be looking at are:
This was a very rigorous, highly instructive course and was taught in Matlab. Thoroughly recommend.
This was very easy; and is basically Yale’s Finance 101 class. Would recommend for anyone with no prior knowledge in finance, otherwise, I would stay clear, as it is lacking any kind of rigour.
I can’t recommend this course enough, Harvard have thrown a lot of resources into putting this together, and it is certainly full of cool stuff, amazing problem sets, lectures and there is also a final project. If you want to learn Computer Science then it doesn’t get any better than this.
This was a really good course, very instructive and aimed at an intermediate level. No programming was required, but excel templates were, which were needed in order to solve the weekly quizzes. Main focus of the course was the use of binomial trees (i.e. lattices) for pricing securities. The course covered arbitrage pricing theory and general finial engineering theory for options, futures, swaps, interest rates and a few more exotic derivatives. Would definitely recommend.
Following on from Andrew Ng’s Machine Learning Course from Stanford, Prof Ng puts out a 5-part specialisation in Deep learning. This time they are using Python and the Tensor-Flow Library. Andrew Ng is as awesome as ever, and this course is very good. He has designed an excellent notation for his exposition of teaching deep learning to his audience. Would highly recommend.
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization – Coursera Course
After I completed the first ML course above in Matlab, I rebuilt a lot of the algos into my trading infrastructure for testing and analysis. However, at the time I was doing a lot of work with Neural-Nets, and had an abundance of hyperparamater questions relating to the practical application of tuning. This course is incredibly useful in this dimension and I wish I did this course 2 years ago. Would highly recommend.
This was useful, and interesting, but very easy and short. So yeah, would definitely recommend doing it, but I think Andrew Ng needs to bulk it out more with more awesome tips and trips of implementing your own Machine Learning Projects.
- Financial Engineering, Part 2, Columbia University – Coursera Course
- Pricing Options with Mathematical Models, Caltech – edX Course
- John Cochrane – Asset Pricing, University of Chicago – Canvas Course
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- Data Science, John Hopkins University – Coursera
- Big Data, UC San Diego – Coursera
- Timothy Geithner – Global Financial Crisis, Yale University – Coursera Course
- Machine Learning for Trading in Python, Georgia Tech – Udacity Course
- Machine Learning, Columbia University – edX Course
- Artificial Intelligence, Columbia – edX Course
- Computational Probability and Inference, MIT – edX Course
Other Learning Resources:
- John Cochrane – Teaching Materials
- HFT and Algorithmic Trading Course, University of Toronto
- Kevin Sheppard – Python Course
- Natural Language Processing Course, Stanford University
- Python for Finance Course
- Machine Learning Courses – Information
- Kevin Sheppard – Advanced Financial Econometrics, Oxford University
- Joel Hasbrouck – Principles of Securities Trading