The reading list for scientific programmer [closed]

Solution 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

Solution 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.

Solution 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.