A different perspective on scientific programming [review of "annotated algorithms in python; with applications in physics, biology, and finance" (di pierro, m.; 2013)]

This is an unconventional computer science book, as it’s not a textbook, tutorial, or reference. It’s an attempt to categorize the algorithms of quantitative programming and then make further remarks using a number of examples from each category. The objective, I would presume, is to strengthen the ability of the reader to recognize that their own programming challenges can be reduced to a combination of approximate and statistical approaches that must be pragmatic (so that the time length of execution is reasonable). The coding illustrations demonstrate common techniques for the ordering of algorithms. At the same time, it introduces the reader to Python, a language which is growing in popularity among engineers and scientists. The book excels as an introduction to the Python language for experienced programmers. It has an excellent chapter on parallel processing, an impressive random number generator discussion, and some fine biology and finance examples.