The reading list for scientific programmer [closed]

Question

I am working to become a scientific programmer. I have enough background in Math and Stat but rather lacking on programming background. I found it very hard to learn how to use a language for scientific programming because most of the reference for SP are close to trivial.

My work involves statistical/financial modelling and none with physics model. Currently, I use Python extensively with numpy and scipy. Done R/Mathematica. I know enough C/C++ to read code. No experience in Fortran.

I dont know if this is a good list of language for a scientific programmer. If this is, what is a good reading list for learning the syntax and design pattern of these languages in scientific settings.

Answer 1

At some stage you're going to need floating point arithmetic. It's hard to do it well, less hard to do it competently, and easy to do it badly. This paper is a must read:

What Every Computer Scientist Should Know About Floating-Point Arithmetic

Answer 2

I thoroughly recommend

Scientific and Engineering C++: An Introduction with Advanced Techniques and Examples by Barton and Nackman

Don't be put off by its age, it's excellent. Numerical Recipes in your favourite language (so long as it is C,C++ or Fortran) is compendious, and excellent for learning from, not always the best algorithms for each problem.

I also like

Parallel Scientific Computing in C++ and MPI: A Seamless Approach to Parallel Algorithms and their Implementation by Karniadakis

The sooner you start parallel computing the better.

Answer 3

My first suggestion is that you look at the top 5 universities for your specific field, look at what they're teaching and what the professors are using for research. That's how you can discover the relevant language/approach.

Also have a look at this stackoverflow question ("practices-for-programming-in-a-scientific-environment").

You're doing statistical/finance modeling? I use R in that field myself, and it is quickly becoming the standard for statistical analysis, especially in the social sciences, but in finance as well (see, for instance, http://rinfinance.com). Matlab is probably still more widely used in industry, but I have the sense that this may be changing. I would only fall back to C++ as a last resort if performance is a major factor.

Look at these related questions for help finding reading materials related to R:

• suitable-functional-language-for-scientific-statistical-computing
• books-for-learning-the-r-language
• what-can-be-done-in-r-that-cant-be-done-with-python-numpy-scipy
• r-for-finance-tutorials-resources

In terms of book recommendations related to statistics and finance, I still think that the best general option is David Ruppert's "Statistics and Finance" (you can find most of the R code here and the author's website has matlab code).

Lastly, if your scientific computing isn't statistical, then I actually think that Mathematica is the best tool. It seems to get very little mention amongst programmers, but it is the best tool for pure scientific research in my view. It has much better support for things like integration and partial differential equations that matlab. They have a nice list of books on the wolfram website.

Answer 4

In terms of languages, I think you have a good coverage. Python is great for experimentation and prototyping, Mathematica is good for helping with the theoretical stuff, and C/C++ are there if you need to do serious number crunching.

I might also suggest you develop an appreciation of an assembly language and also a functional language (such as Haskell), not really to use, but rather because of the effect they have on your programming skills and style, and of the concepts they bring home to you. They might also come in handy one day.

I would also consider it vital to learn about parallel programming (concurrent/distributed) as this is the only way to access the sort of computing power that sometimes is necessary for scientific problems. Exposure to functional programming would be quite helpful in this regard, whether or not you actually use a functional language to solve the problem.

Unfortunately I don't have much to suggest in the way of reading, but you may find The Scientist and Engineer's Guide to Digital Signal Processing helpful.