Modeling with Data: Tools and Techniques for Scientific Computing

by Ben Klemens

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.


Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.



Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

472 pages, Hardcover

First published January 1, 2008

Reviews

[Matt Jans · Rating 5 out of 5]

Great book, with a style reflecting a philosophy and purpose of the text, as well as technical details of "modeling with data." A bridge between quantitative scientists and computer programmers.

I picked up the book partly because it's by a Census Bureau colleague but also because of the title. I've always been interested in the link between computer science and statistics, and I think I learn statistics better in a programming context than a mathematical one.

I had to return the book, so didn't finish it, but I was happy with the sections I read. Ben's writing style is enjoyable and I think he does a good job of hitting interesting programming and statistical points at the same time. I didn't work through any of the exercises, but it seems like that could easily be done, which might make the book a good text for a statistical programming course.

[Senne · Rating 5 out of 5]

PDF version can be downloaded from auther's website. I'm thought by a lot people as nerd, using Linux vim r not standard win excel spss matlab sas etc, this book is 1000 times that, not only using c to do statistic, he wrote his own library/framework,that's a nerd's dream! I must say building foundation firmly feels good and right, this book teach me a lot.

[B · Rating 5 out of 5]

Given that I've forgotten much of this stuff, I really think this book is smarter than I am.