Education advice needed for experience professional new to field

[Francis Bruno]

I'm a hardware engineer who has developed high speed/ low latency devices for about 25 years.

I left SpaceX a few months ago to pursue a career in developing FPGA hardware for the finance industry and I'm currently working at a trading firm in Chicago where in my short time, I've already made a big difference in their technology. The job and people are great and I want to learn more about the algorithm side so I can better understand the needs of the traders and make suggestions as to what can be accelerated better on the FPGA platform than in software.

I'm excellent at FPGA/ digital logic design, but I lack high level programming experience (I can hack in Python and C. I'm currently taking a C# coursera course for my personal benefit.) Also, I'm weak on math and need to refresh.

I am planning to take the Financial Engineering & Risk Management on Coursera to get a taste. I see the online C++ course advertised here and I'm considering it. I'm also reading lots of books recommended by the software folks at my company on the history of the industry.

I would like recommendation on whether something like an MS in Financial Engineering or Finanacial Mathematics is worthwhile? I would have to find something local or online and also part time as I love my job.

I have been out of school for 25 years, however, I do possess a BSEE (not a very good GPA < 3), an MSEE (3.6 or 3.8, I don't recall exactly) and an MBA 3.8 I believe.

A bit long winded, but I would appreciate any advice from the people here.

Thanks,
Frank

 

[Daniel Duffy]

I would recommend the QN course I developed (I might be biased :D). I have given this course to real-time and embedded developers (among many others) here in the Netherland and EU since 1992 based on the contents of QN.

C# is really good to know but is easy to learn *after* C++.

The QN price is good value as well. And GREAT support from TAs (even I try to help now and again )

Maths: difficult one, but calculus and then numerical methods? a kind of reverse engineering way is play with Python libraries and move 'backwards' to the maths behind it.