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The **10 Best Mathematical & Statistical Software Books **list have been recommended not only by normal readers but also by experts.

You’ll also find that these are top-ranking books on the US Amazon Best Sellers book list for the **Mathematical & Statistical Software category of books**.

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Let’s take a look at the list of **10 Best Mathematical & Statistical Software Books**.

## 10 Best Mathematical & Statistical Software Books

Now, let’s dive right into the list of **10 Best Mathematical & Statistical Software Books**, where we’ll provide a quick outline for each book.

### 1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham Review Summary

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R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: * Wrangle –transform your datasets into a form convenient for analysis * Program –learn powerful R tools for solving data problems with greater clarity and ease * Explore –examine your data, generate hypotheses, and quickly test them * Model –provide a low-dimensional summary that captures true “signals” in your dataset * Communicate –learn R Markdown for integrating prose, code, and results.

### 2. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) by Gareth James Review Summary

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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

### 3. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by Peter Bruce Review Summary

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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: * Why exploratory data analysis is a key preliminary step in data science * How random sampling can reduce bias and yield a higher-quality dataset, even with big data * How the principles of experimental design yield definitive answers to questions * How to use regression to estimate outcomes and detect anomalies * Key classification techniques for predicting which categories a record belongs to * Statistical machine learning methods that “learn” from data * Unsupervised learning methods for extracting meaning from unlabeled data

### 4. Monte Carlo Simulation: The Art of Random Process Characterization by D. James Benton Review Summary

Monte Carlo Simulation: The Art of Random Process Characterization
There are many textbooks devoted to the theory behind Monte Carlo methods. More often than not, these are heavy on theory and light on example. Rarely do they include the examples in their entirety, mostly presenting the final results in summary form. The aim of this text is to be light on theory and heavy on example. Each example is included in its entirety: input, output, and source code or spreadsheet. If you work through all the examples, you should be able to simulate whatever process is needed.

### 5. The Model Thinker: What You Need to Know to Make Data Work for You by Scott E. Page Review Summary

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The Model Thinker: What You Need to Know to Make Data Work for You
Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja. From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren’t enough: we need to know how to make those numbers talk. In The Model Thinker , social scientist Scott E. Page shows us the mathematical, statistical, and computational models–from linear regression to random walks and far beyond– that can turn anyone into a genius. At the core of the book is Page’s “many- model paradigm,” which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

### 6. Bitcoin: The Digital Gold behind Cryptocurrencies, Blockchain and Mining by Steve J. Hotwelsy Review Summary

Bitcoin: The Digital Gold behind Cryptocurrencies, Blockchain and Mining
WHAT DO YOU SAY IF I TEACH YOU EVERYTHING THERE IS TO KNOW ABOUT CRYPTOCURRENCIES AND YOU START TO ACCUMULATE A REAL FORTUNE? Are you curious, right? Yet it is the truth, it’s not a coincidence that they are considered the new DIGITAL GOLD! If you want to know all the tricks and advantages of bitcoins, you want to understand how to use them or just know what they are, this book will give you all the answers you need. Don’t worry, with bitcoin you don’t need to have specific skills but just have the right support, adopt the right mental condition to easily understand and be well informed. I will help you throughout the learning process thanks to the easy smoothness in reading! The particularity of these virtual coins, is that they are encrypted behind a code. They are a great investment innovation that is depopulating in the digital world and has a really modern features that you can’t never imagine! Obviously, you will also learn what mining and blockchain are and how they work, because they play a fundamental role in this business. Change your mindset from NOW and start investing first in YOURSELF and in your STUDY. I’m giving you all my knowledge so you can SUCCEED and not end up in bad scams. Trust me, GET THIS BOOK NOW and start learning all the mechanisms behind the most profitable coin ever invented!

### 7. The Book of R: A First Course in Programming and Statistics by Tilman M. Davies Review Summary

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The Book of R: A First Course in Programming and Statistics
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: -The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops -Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R -How to access R’s thousands of functions, libraries, and data sets -How to draw valid and useful conclusions from your data -How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

### 8. R For Dummies by Andrie de Vries Review Summary

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R For Dummies
Mastering R has never been easier Picking up R can be tough, even for seasoned statisticians and data analysts. R For Dummies , 2 nd Edition provides a quick and painless way to master all the R you’ll ever need. Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel. You’ll learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and so much more. R is an open source statistical environment and programming language that has become very popular in varied fields for the management and analysis of data. R provides a wide array of statistical and graphical techniques, and has become the standard among statisticians for software development and data analysis. R For Dummies, 2 nd Edition takes the intimidation out of working with R and arms you with the knowledge and know-how to master the programming language of choice among statisticians and data analysts worldwide. * Covers downloading, installing, and configuring R * Includes tips for getting data in and out of R * Offers advice on fitting regression models and ANOVA * Provides helpful hints for working with graphics R For Dummies, 2 nd Edition is an ideal introduction to R for complete beginners, as well as an excellent technical reference for experienced R programmers.

### 9. Programming Quantum Computers: Essential Algorithms and Code Samples by Eric R. Johnston Review Summary

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Programming Quantum Computers: Essential Algorithms and Code Samples
Quantum computers are poised to kick-start a new computing revolution–and you can join in right away. If you’re in software engineering, computer graphics, data science, or just an intrigued computerphile, this book provides a hands- on programmer’s guide to understanding quantum computing. Rather than labor through math and theory, you’ll work directly with examples that demonstrate this technology’s unique capabilities. Quantum computing specialists Eric Johnston, Nic Harrigan, and Mercedes Gimeno-Segovia show you how to build the skills, tools, and intuition required to write quantum programs at the center of applications. You’ll understand what quantum computers can do and learn how to identify the types of problems they can solve. This book includes three multichapter sections: * Programming for a QPU –Explore core concepts for programming quantum processing units, including how to describe and manipulate qubits and how to perform quantum teleportation. * QPU Primitives –Learn algorithmic primitives and techniques, including amplitude amplification, the Quantum Fourier Transform, and phase estimation. * QPU Applications –Investigate how QPU primitives are used to build existing applications, including quantum search techniques and Shor’s factoring algorithm.

### 10. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos Review Summary

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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm , Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner–the Master Algorithm–and discusses what it will mean for business, science, and society. If data-ism is today’s philosophy, this book is its bible.