Modeling with Data by Ben Klemens
Modeling with Data - Table of Contents
PART I: COMPUTATIONAL TOOLS & COMPUTING
- 1. Principles of Statistics in the Modern Day
- 2. Scientific Computing with the C Language
- 3. Relational Databases and SQL Integration
- 4. Matrices and Models Computation
- 5. Data Visualization and Graphics
- 6. More Coding Tools for Statistical Analysis
PART II: STATISTICAL MODELS & APPENDICES
- 7. Distributions for Description and Fit
- 8. Linear Regression and Linear Projections
- 9. Hypothesis Testing with the CLT
- 10. Maximum Likelihood Estimation (MLE)
- 11. Monte Carlo Methods and Simulation
- A. Appendix: Environments and Makefiles
- B. Appendix: Text Processing and Parsing
What You Will Learn in Modeling with Data
Modeling with Data: Tools and Techniques for Scientific Computing by Ben Klemens is an open-access, mathematically grounded textbook that teaches statistical analysis through the power and speed of modern C and SQL database systems. Published by Princeton University Press, this comprehensive volume guides readers through modern computational statistics, SQL database processing, matrix algebra, data graphics, descriptive distributions, linear projections, hypothesis testing under the Central Limit Theorem, maximum likelihood estimation (MLE), and Monte Carlo simulations with exceptional clarity and structural logic.
Designed for graduate students, computational statisticians, quantitative social scientists, data engineers, and C programmers, this foundational text bridges abstract statistical theory and high-performance software implementation. Klemens details step-by-step algorithms using GNU Scientific Library (GSL) and SQLite, showing how to process large datasets without relying on slow interpreted languages. Whether you are building custom likelihood functions, executing matrix regressions, or scripting GNU Make environments, this book offers an indispensable roadmap.
Recognized globally for its unique combination of statistical rigor and C systems programming, it stands as one of the best computational statistics books for self-study. By connecting empirical modeling directly to C software development, it serves as an essential tool for mastering data modeling and scientific computing with confidence.
Book Details & Specifications
Title:
Modeling with Data by Ben Klemens
Publisher:
Princeton University Press
Year:
2009
Pages:
470
Type:
PDF
Language:
English
ISBN-10 #:
069113314X
ISBN-13 #:
978-0691133140
License:
External Educational Resource
Amazon:
Amazon
About the Author: Ben Klemens
The author Ben Klemens
is a statistician, computational scientist, and quantitative policy consultant who has worked extensively with the U.S. Census Bureau, the World Bank, and various academic institutions. He is an internationally recognized expert in scientific C programming, statistical software design, and open-source computational tools.
Published open-access alongside Princeton University Press, Modeling with Data reflects Klemens's dedicated effort to equip researchers with efficient, production-grade tools for statistical computing. His practical code examples, clear algorithmic explanations, and focus on fundamental computational mechanics make this text a classic reference for computational statistics in C trusted by quantitative researchers worldwide.
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