Modeling with Data: Tools and Techniques for Scientific Computing

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

**Tag(s):**
Mathematics
Statistics

**Publication date**: 24 Mar 2009

**ISBN-10**:
069113314X

**ISBN-13**:
9780691133140

**Paperback**:
472 pages

**Views**: 6,926

Modeling with Data: Tools and Techniques for Scientific Computing

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

From the Introduction:

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.

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.

Tweet

About The Author(s)

Ben Klemens has worked on computational modeling projects in a surprising range of contexts, including work on cross-border migration at the World Bank, terrorist networks and voter turnout with the Brookings Institution, the genetic roots of mental disorders with the National Institute of Mental Health, network externalities as a grad student at Caltech, and even flogging in the 19th-century U.S. Navy at NYU.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors