Best Reference Books – Computer Oriented Numerical Analysis and Statistical Methods

We have compiled the list of Top 10 Best Reference Books on Computer Oriented Numerical Analysis and Statistical Methods subject. These books are used by students of top universities, institutes and colleges. Here is the full list of top 10 best books on Computer Oriented Numerical Analysis and Statistical Methods along with reviews.

Kindly note that we have put a lot of effort into researching the best books on Computer Oriented Numerical Analysis and Statistical Methods subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Computer Oriented Numerical Analysis and Statistical Methods" as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.

1. “Computer Oriented Numerical Methods” by Rajaraman

“Computer Oriented Numerical Methods” Book Review: This book is a brief and lucid introduction to computer-oriented numerical methods with chosen graphical illustrations that give an insight into the mechanism of various methods suitable for students interested in the field and engineering aspirants. This book develops computational algorithms for solving nonlinear algebraic equations, sets of linear equations, curve-fitting, integration, differentiation, and solving ordinary differential equations.

2. “Fundamentals of Numerical Computation” by G Alefeld and R Grigorieff
3. “Programming in Haskell” by Graham Hutton

“Programming in Haskell” Book Review: The new edition of the book has been extensively updated and expanded to include new and more advanced features of Haskell, new examples and exercises, selected solutions, and freely downloadable lecture slides and example code. While Part I of the book focuses on basic concepts, Part II introduces the reader to more advanced topics. The presentation is clean and simple. All concepts are explained from first principles and no programming experience is required, making this book convenient to a wide spectrum of readers.

4. “Computer Oriented Numerical Methods” by R S Salaria

“Computer Oriented Numerical Methods” Book Review: This book provides thorough coverage of the subject, ensures the conceptual understanding of numerical methods, formulae for different numerical methods have been derived in the simplest manner, algorithms for these methods are developed using pseudo-language, a large number of programming exercises to test your for reference, a large number of multiple-choice questions and review exercises to test the users programming skills collected. In this book, the majority of the algorithms are implemented in C, C++, and FORTRAN languages suitable for students interested in the field.

5. “Computer Oriented Numerical Methods” by N Datta

“Computer Oriented Numerical Methods” Book Review: This book gives the algorithms required for easy implementation of numerical methods in computer programming. The book deals with the important topics of numerical methods, including errors in numerical computation, in a lucid style. The contents of the book go like: Numerical Methods and errors Interpolation, Numerical Differentiation, Numerical Integration, Solution of Algebraic and Transcendental Equations, Numerical Solution of a System of Linear Equations, Numerical Solution of Ordinary differential equations, Curve fitting, Numerical Solution of problems associated with Partial Differential Equations. This book gives the user chapter-end short questions with answers and appendices with theory questions and ‘C’ programs which is a student-friendly feature of the book.

6. “Object-Oriented Analysis and Design with Applications” by Booch

“Object-Oriented Analysis and Design with Applications” Book Review: In this book of “Object-Oriented Analysis and Design with Applications” students can learn to apply object-oriented methods using the Unified Modeling Language (UML) 2.0. This book offers improved methods for object development that tackle the complex problems faced by system and software developers. This book also presents numerous examples, illustrates essential concepts, explains the method, and shows successful applications in a variety of fields, including systems architecture, data acquisition, cryptanalysis, control systems, and Web development. Students will also find pragmatic advice on a host of important issues, including classification, implementation strategies, and cost-effective project management.

7. “Machine Learning (in Python and R) For Dummies” by John Paul Mueller and Luca Massaron

“Machine Learning (in Python and R) For Dummies” Book Review: This book offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get the users familiar with the basic concepts of machine learning, this book helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. This guide makes it easier to understand and implement machine learning seamlessly. In this comprehensive text, students will learn how day-to-day activities are powered by machine learning, learn to ‘speak’ certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis, learn to code in R using RStudio, find out how to code in Python using Anaconda.

8. “Numerical Analysis with Algorithms and Computer Programs in C++” by Wadhwa A

“Numerical Analysis with Algorithms and Computer Programs in C++” Book Review: This book is intended for the students of B. Sc. (Hons.) Physics, B. Sc. (Hons.) Electronics, and B. Sc. (Hons.) Mathematics as well as engineering students of all disciplines. The book gives a brief introduction to Numerical Methods, blended with the traditional algebraic approach and the computer-based approach. The special importance is given to evolving algorithms that have been directly transformed into programs in C++. In this book, each numerical method, used for solving nonlinear algebraic equations, simultaneous linear equations, differentiation, integration, ordinary differential equations, curve fitting, and so on, is accompanied by an algorithm and the corresponding computer program.

9. “Hands–On Programming with R” by Garrett Grolemund

“Hands-On Programming with R” Book Review: In this book, students will learn how to program by diving into the R language and then use their skills to solve practical data science problems. In this book, students will learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write their functions and use all of R’s programming tools through which the user will gain valuable programming skills and support their work as a data scientist at the same time. This book includes hands-on working with three practical data analysis projects based on casino games, Storing, retrieve and change data values in your computer’s memory, writing programs and simulations that outperform those written by typical R users, using R programming tools such as if-else statements, for loops and S3 classes, learning to write lightning-fast vectorized R code, taking advantage of R’s package system and debugging tools, practicing and applying R programming concepts as they learn.

10. “A Student’s Guide to Numerical Methods South Asia Edition” by Ian H Hutchinson

“Hands-On Programming with R” “A Student’s Guide to Numerical Methods South Asia Edition” Book Review: This brief, plain-language guide for senior undergraduates and graduate students intends to develop inspiration, practical skills, and an understanding of the structure of numerical methods for the physical sciences and engineering. This book provides accessible self-contained explanations of mathematical principles, avoiding intimidating formal proofs. Worked examples and targeted exercises in this book enable the student to master the realities of using numerical techniques for basic needs such as a solution of ordinary and partial differential equations, fitting experimental data, and simulation using particle and Monte Carlo methods. This book comes with carefully selected topics and is structured to build understanding, and illustrate key principles such as accuracy, stability, order of convergence, iterative refinement, and computational effort estimation. This book can be used for self-study, or as the basis of an accelerated introductory class, this compact textbook provides a thorough grounding in computational physics and engineering.

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Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn | Youtube | Instagram | Facebook | Twitter