• C++ Programming for Financial Engineering
    Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering.
    Python for Finance with Intro to Data Science
    Gain practical understanding of Python to read, understand, and write professional Python code for your first day on the job. Coming soon.
    An Intuition-Based Options Primer for FE
    Ideal for entry level positions interviews and graduate studies, specializing in options trading arbitrage and options valuation models.

Advanced Calculus or Numerical Linear Algebra

MichaelLewis

Well-Known Member
C++
Thinking ahead for prep courses, if you only had time to take one, which would you think is more valuable for MFE prep?
 

Michsund

Well-Known Member
C++
Just wanted to add that I’ve taken the advanced calculus course, it’s good but I feel like mfe programs go over that in detail. Linear is not in my experience.
 

MichaelLewis

Well-Known Member
C++
Just wanted to add that I’ve taken the advanced calculus course, it’s good but I feel like mfe programs go over that in detail. Linear is not in my experience.
Are there any good books or source material so I can get comfortable with the topic beforehand? I was a little surprised as well because it seemed PDEs we’re super important.
 

Michsund

Well-Known Member
C++
Are there any good books or source material so I can get comfortable with the topic beforehand? I was a little surprised as well because it seemed PDEs we’re super important.
I only saw pde for stoc Calc 2 even then it wasn’t much. I assume you need pde’s more for pricing but I’m not really an expert there. I would saw coming in to any mfe know how to use pandas and numpy well and be good at basic maths.ie) linear, Calc,etc
 

Michsund

Well-Known Member
C++
C++ is a good way to learn to code because you learn the fundamentals for sure, but in terms of mfe course work at least at cmu knowing how to use python is very important.
 

Andy Nguyen

Member
C++ is a good way to learn to code because you learn the fundamentals for sure, but in terms of mfe course work at least at cmu knowing how to use python is very important.
Quantnet now offers an online Python course as well :)
Would love to hear about the kind of Python projects you do at CMU and the level of knowledge expected there.
 

Michsund

Well-Known Member
C++
Quantnet now offers an online Python course as well :)
Would love to hear about the kind of Python projects you do at CMU and the level of knowledge expected there.
We do have python notebooks where we do a lot of classes, but it’s not structured well in terms of setting up a work style project. I mean some people probably do, but the structure of our code wasn’t something that I would write when working for a job. In that sense it might be interesting to see how to write python code “professionally”. Maybe @Onegin can give his opinion on this
 

Daniel Duffy

C++ author, trainer
Traditionally, "linear algebra" tends to be taught in academia without dealing with "numerical" part. A better name is Vector (or Linear) spaces. To this stuff into a computer we have "Numerical Linear Algebra" and has algorithms for a wide range of problems and applications all over the place, e.g. PDE, ML. Finally, these algos are in Fortran, C++ and Python. Each part of the jigsaw is needed.

Some topics

 

Onegin

Well-Known Member
C++
We do have python notebooks where we do a lot of classes, but it’s not structured well in terms of setting up a work style project. I mean some people probably do, but the structure of our code wasn’t something that I would write when working for a job. In that sense it might be interesting to see how to write python code “professionally”. Maybe @Onegin can give his opinion on this
Agree; we code a lot in python, but it’s typically subordinate to the topic under study. For example, write this simulation, ML Algo, Data science study, in python. The pro is the curriculum goes beyond the “recipe book” approach to ML / DS into the underlying theory, assumptions, and evolution.

That’s different from developing production quality code. I heard once (like on a podcast last week) that software development progressed from an individual caffeine fueled cowboy approach to a firm-wide cross team synchronized activity. So you have to think about versioning, inter-operability, standards, duplication of efforts, documentation, to name a few. That’s not covered, and is very important in a professional setting where you’re working on stuff that has a lot of financial risk. But, It’s not obvious to me that it could / should be covered in such a program. There are other ways to obtain that knowledge (C++ course here being one of them), but it’s harder to learn ML / StoCal at a high resolution without the focus of a masters program in my opinion. Of course, I’m probably one of the dimmer bulbs in the program, so take this w a grain of salt.
 

Daniel Duffy

C++ author, trainer
Agree; we code a lot in python, but it’s typically subordinate to the topic under study. For example, write this simulation, ML Algo, Data science study, in python. The pro is the curriculum goes beyond the “recipe book” approach to ML / DS into the underlying theory, assumptions, and evolution.

That’s different from developing production quality code. I heard once (like on a podcast last week) that software development progressed from an individual caffeine fueled cowboy approach to a firm-wide cross team synchronized activity. So you have to think about versioning, inter-operability, standards, duplication of efforts, documentation, to name a few. That’s not covered, and is very important in a professional setting where you’re working on stuff that has a lot of financial risk. But, It’s not obvious to me that it could / should be covered in such a program. There are other ways to obtain that knowledge (C++ course here being one of them), but it’s harder to learn ML / StoCal at a high resolution without the focus of a masters program in my opinion. Of course, I’m probably one of the dimmer bulbs in the program, so take this w a grain of salt.
This is 100% correct.
After all these years Computer Science still does not understand that software development is more of an engineering+ design discipline.
Anyone can write a program that prints '42'. And unfortunately, the situation is exacerbated in ML. The real risk in the future is armies of Python maintenance programmers if we are not careful.

For some reason, many students have not been made aware of how software development works outside. Read my analysis of the disastrous COVID-19 software. It proves what I was saying all along.


I have worked in industry for some time and best practices have been woven into the Quantnet/Baruch C++ courses. e.g. decomposition, SRP, design patterns and all those good stuff.
 
Last edited:

Daniel Duffy

C++ author, trainer
Anonymous message

Professor Duffy, just one question do you any chance offer possibility of buying some book concerning Python with code as for Financial Instrument Pricing. I am in desperate need of professional python code [..]

@APalley
@Andy Nguyen
 
Last edited:

Cuong Ly

Active Member
C++
@Michsund
FYI, Baruch College also offers Python for Finance at $750. Quantnet is at $1,250 with additional 3 levels focusing on Data Science/Machine Learning.
 
Top