This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets.
The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.
In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix.
See What’s New in the Second Edition:
Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical.
The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text.
The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.
Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.
New to the Second Edition
Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates Extensive use of the ggplot2 graphics package in addition to base graphics
Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.
Drawing on the author’s first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code.
With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.
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 analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, generate hypotheses, and quickly test themModel—provide a low-dimensional summary that captures true "signals" in your datasetCommunicate—learn R Markdown for integrating prose, code, and results
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.
Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your data
"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
This book is ideal for anyone who likes puzzles, brainteasers, games, gambling, magic tricks, and those who want to apply math and science to everyday circumstances. Several hacks in the first chapter alone-such as the "central limit theorem,", which allows you to know everything by knowing just a little-serve as sound approaches for marketing and other business objectives. Using the tools of inferential statistics, you can understand the way probability works, discover relationships, predict events with uncanny accuracy, and even make a little money with a well-placed wager here and there.
Statistics Hacks presents useful techniques from statistics, educational and psychological measurement, and experimental research to help you solve a variety of problems in business, games, and life. You'll learn how to:Play smart when you play Texas Hold 'Em, blackjack, roulette, dice games, or even the lotteryDesign your own winnable bar bets to make money and amaze your friendsPredict the outcomes of baseball games, know when to "go for two" in football, and anticipate the winners of other sporting events with surprising accuracyDemystify amazing coincidences and distinguish the truly random from the only seemingly random--even keep your iPod's "random" shuffle honestSpot fraudulent data, detect plagiarism, and break codesHow to isolate the effects of observation on the thing observed
Whether you're a statistics enthusiast who does calculations in your sleep or a civilian who is entertained by clever solutions to interesting problems, Statistics Hacks has tools to give you an edge over the world's slim odds.
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
This book will help you:Become a contributor on a data science teamDeploy a structured lifecycle approach to data analytics problemsApply appropriate analytic techniques and tools to analyzing big dataLearn how to tell a compelling story with data to drive business actionPrepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at www.wiley.com/go/9781118876138.
Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.
Extensively updated to reflect the evolution of statistics and computing, the second edition of the bestselling R Graphics comes complete with new packages and new examples. Paul Murrell, widely known as the leading expert on R graphics, has developed an in-depth resource that helps both neophyte and seasoned users master the intricacies of R graphics.
New in the Second Edition
Updated information on the core graphics engine, the traditional graphics system, the grid graphics system, and the lattice package A new chapter on the ggplot2 package New chapters on applications and extensions of R Graphics, including geographic maps, dynamic and interactive graphics, and node-and-edge graphs
Organized into five parts, R Graphics covers both "traditional" and newer, R-specific graphics systems. The book reviews the graphics facilities of the R language and describes R’s powerful grid graphics system. It then covers the graphics engine, which represents a common set of fundamental graphics facilities, and provides a series of brief overviews of the major areas of application for R graphics and the major extensions of R graphics.
RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer’s memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R’s package system and debugging toolsPractice and apply R programming concepts as you learn them
The important stuff you need to know:Go from novice to ace. Learn how to analyze your data, from writing your first formula to charting your results.Illustrate trends. Discover the clearest way to present your data using Excel’s new Quick Analysis feature.Broaden your analysis. Use pivot tables, slicers, and timelines to examine your data from different perspectives.Import data. Pull data from a variety of sources, including website data feeds and corporate databases.Work from the Web. Launch and manage your workbooks on the road, using the new Excel Web App.Share your worksheets. Store Excel files on SkyDrive and collaborate with colleagues on Facebook, Twitter, and LinkedIn.Master the new data model. Use PowerPivot to work with millions of rows of data.Make calculations. Review financial data, use math and scientific formulas, and perform statistical analyses.
First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP .
Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format:
an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison
With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis.
This book is part of the SAS Press program.
Written by an author team who is directly involved with SAS,this easy-to-follow guide is fully updated for the latest releaseof SAS and covers just what you need to put this popular softwareto work in your business. SAS allows any business or enterprise toimprove data delivery, analysis, reporting, movement across acompany, data mining, forecasting, statistical analysis, and more.SAS For Dummies, 2nd Edition gives you thenecessary background on what SAS can do for you and explains how touse the Enterprise Guide.SAS provides statistical and data analysis tools to help youdeal with all kinds of data: operational, financial, performance,and morePlaces special emphasis on Enterprise Guide and otheranalytical tools, covering all commonly used featuresCovers all commonly used features and shows you the practicalapplications you can put to work in your businessExplores how to get various types of data into the software andhow to work with databasesCovers producing reports and Web reporting tools, analytics,macros, and working with your data
In the easy-to-follow, no-nonsense For Dummiesformat, SAS For Dummies gives you the knowledge and theconfidence to get SAS working for your organization.
Note: CD-ROM/DVD and other supplementary materials arenot included as part of eBook file.
addresses tasks that nearly every SAS programmer needs to do - that is, make
sure that data errors are located and corrected. This book develops and
demonstrates data cleaning programs and macros that you can use as written or
modify for your own special data cleaning needs.
R is fast becoming the de facto standard for statisticalcomputing and analysis in science, business, engineering, andrelated fields. This book examines this complex language usingsimple statistical examples, showing how R operates in auser-friendly context. Both students and workers in fields thatrequire extensive statistical analysis will find this book helpfulas they learn to use R for simple summary statistics, hypothesistesting, creating graphs, regression, and much more. It coversformula notation, complex statistics, manipulating data andextracting components, and rudimentary programming.R, the open source statistical language increasingly used tohandle statistics and produces publication-quality graphs, isnotoriously complexThis book makes R easier to understand through the use ofsimple statistical examples, teaching the necessary elements in thecontext in which R is actually usedCovers getting started with R and using it for simple summarystatistics, hypothesis testing, and graphsShows how to use R for formula notation, complex statistics,manipulating data, extracting components, and regressionProvides beginning programming instruction for those who wantto write their own scripts
Beginning R offers anyone who needs to performstatistical analysis the information necessary to use R withconfidence.
Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.Get started quickly with an R tutorial and hundreds of examplesExplore R syntax, objects, and other language detailsFind thousands of user-contributed R packages online, including BioconductorLearn how to use R to prepare data for analysisVisualize your data with R’s graphics, lattice, and ggplot2 packagesUse R to calculate statistical fests, fit models, and compute probability distributionsSpeed up intensive computations by writing parallel R programs for HadoopGet a complete desktop reference to R
If you are familiar with online banking and want to expand your finances into a resilient and transparent currency, this book is ideal for you. A basic understanding of online wallets and financial systems will be highly beneficial to unravel the mysteries of Bitcoin.What You Will LearnSet up your wallet and buy a Bitcoin in a flash while understanding the basics of addresses and transactionsAcquire the knack of buying, selling, and trading Bitcoins with online marketplacesSecure and protect your Bitcoins from online theft using Brainwallets and cold storageUnderstand how Bitcoin's underlying technology, the Blockchain, works with simple illustrations and explanationsConfigure your own Bitcoin node and execute common operations on the networkDiscover various aspects of mining Bitcoin and how to set up your own mining rigDive deeper into Bitcoin and write scripts and multi-signature transactions on the networkExplore the various alt-coins and get to know how to compare them and their valueIn Detail
The financial crisis of 2008 raised attention to the need for transparency and accountability in the financial world. As banks and governments were scrambling to stay solvent while seeking a sustainable plan, a powerfully new and resilient technology emerged.
Bitcoin, built on a fundamentally new technology called “The Blockchain,” offered the promise of a new financial system where transactions are sent directly between two parties without the need for central control.
Bitcoin exists as an open and transparent financial system without banks, governments, or corporate support. Simply put, Bitcoin is “programmable money” that has the potential to change the world on the same scale as the Internet itself.
This book arms you with immense knowledge of Bitcoin and helps you implement the technology in your money matters, enabling secure transactions.
We first walk through the fundamentals of Bitcoin, illustrate how the technology works, and exemplify how to interact with this powerful and new financial technology. You will learn how to set up your online Bitcoin wallet, indulge in buying and selling of bitcoins, and manage their storage. We then get to grips with the most powerful algorithm of all times: the Blockchain, and learn how crypto-currencies can reduce the risk of fraud for e-commerce merchants and consumers.
With a solid base of Blockchain, you will write and execute your own custom transactions. Most importantly, you will be able to protect and secure your Bitcoin with the help of effective solutions provided in the book. Packed with plenty of screenshots, Learning Bitcoin is a simple and painless guide to working with Bitcoin.Style and approach
This is an easy-to-follow guide to working with Bitcoin and the Blockchain technology. This book is ideal for anyone who wants to learn the basics of Bitcoin and explore how to set up their own transactions.
Among the topics included are how to combine plot statements to create custom graphs; customizing graph axes, legends, and insets; advanced features, such as annotation and attribute maps; tips and tricks for creating the optimal graph for the intended usage; real-world examples from the health and life sciences domain; and ODS styles.
The procedures in "Statistical Graphics Procedures by Example" are specifically designed for the creation of analytical graphs. That makes this book a must-read for analysts and statisticians in the health care, clinical trials, financial, and insurance industries. However, you will find that the examples here apply to all fields.
Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming.
People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results.Gets you up to speed on the #1 analytics/data science software toolDemonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modelingShows you how R offers intel from leading researchers in data science, free of chargeProvides information on using R Studio to work with R
Get ready to use R to crunch and analyze your data—the fast and easy way!
Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysis
"Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla
"An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora
The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code.Write a simple R program, and discover what the language can doUse data types such as vectors, arrays, lists, data frames, and stringsExecute code conditionally or repeatedly with branches and loopsApply R add-on packages, and package your own work for othersLearn how to clean data you import from a variety of sourcesUnderstand data through visualization and summary statisticsUse statistical models to pass quantitative judgments about data and make predictionsLearn what to do when things go wrong while writing data analysis code
The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations. It has been conceived as a self contained piece. Therefore, it is addressed not only to Six Sigma practitioners, but also to professionals trying to initiate themselves in this management methodology. The book may be used as a text book as well.
Thoroughly updated, Cody's Data Cleaning Techniques Using SAS, Third Edition, addresses tasks that nearly every data analyst needs to do - that is, make sure that data errors are located and corrected. Written in Ron Cody's signature informal, tutorial style, this book develops and demonstrates data cleaning programs and macros that you can use as written or modify which will make your job of data cleaning easier, faster, and more efficient.
Building on both the author’s experience gained from teaching a data cleaning course for over 10 years, and advances in SAS, this third edition includes four new chapters, covering topics such as the use of Perl regular expressions for checking the format of character values (such as zip codes or email addresses) and how to standardize company names and addresses.
With this book, you will learn how to:find and correct errors in character and numeric values develop programming techniques related to dates and missing values deal with highly skewed data develop techniques for correcting your data errors use integrity constraints and audit trails to prevent errors from being added to a clean data set
"Seamless R and C++ integration with Rcpp" is simply a wonderful book. For anyone who uses C/C++ and R, it is an indispensable resource. The writing is outstanding. A huge bonus is the section on applications. This section covers the matrix packages Armadillo and Eigen and the GNU Scientific Library as well as RInside which enables you to use R inside C++. These applications are what most of us need to know to really do scientific programming with R and C++. I love this book. -- Robert McCulloch, University of Chicago Booth School of Business
Rcpp is now considered an essential package for anybody doing serious computational research using R. Dirk's book is an excellent companion and takes the reader from a gentle introduction to more advanced applications via numerous examples and efficiency enhancing gems. The book is packed with all you might have ever wanted to know about Rcpp, its cousins (RcppArmadillo, RcppEigen .etc.), modules, package development and sugar. Overall, this book is a must-have on your shelf. -- Sanjog Misra, UCLA Anderson School of Management
The Rcpp package represents a major leap forward for scientific computations with R. With very few lines of C++ code, one has R's data structures readily at hand for further computations in C++. Hence, high-level numerical programming can be made in C++ almost as easily as in R, but often with a substantial speed gain. Dirk is a crucial person in these developments, and his book takes the reader from the first fragile steps on to using the full Rcpp machinery. A very recommended book! -- Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark
"Seamless R and C ++ Integration with Rcpp" provides the first comprehensive introduction to Rcpp. Rcpp has become the most widely-used language extension for R, and is deployed by over one-hundred different CRAN and BioConductor packages. Rcpp permits users to pass scalars, vectors, matrices, list or entire R objects back and forth between R and C++ with ease. This brings the depth of the R analysis framework together with the power, speed, and efficiency of C++.
Dirk Eddelbuettel has been a contributor to CRAN for over a decade and maintains around twenty packages. He is the Debian/Ubuntu maintainer for R and other quantitative software, edits the CRAN Task Views for Finance and High-Performance Computing, is a co-founder of the annual R/Finance conference, and an editor of the Journal of Statistical Software. He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.
A short chapter, Mission Impossible, introduces LaTeX documents and presentations. Read these 30 pages; you then should be able to compose your own work in LaTeX. The remainder of the book delves deeper into the topics outlined in Mission Impossible while avoiding technical subjects. Chapters on presentations and illustrations are a highlight, as is the introduction of LaTeX on an iPad.
Students, faculty, and professionals in the worlds of mathematics and technology will benefit greatly from this new, practical introduction to LaTeX. George Grätzer, author of More Math into LaTeX (now in its 4th edition) and First Steps in LaTeX, has been a LaTeX guru for over a quarter of century.
From the reviews of More Math into LaTeX:
``There are several LaTeX guides, but this one wins hands down for the elegance of its approach and breadth of coverage.''
—Amazon.com, Best of 2000, Editors Choice
``A very helpful and useful tool for all scientists and engineers.''
—Review of Astronomical Tools
``A novice reader will be able to learn the most essential features of LaTeX sufficient to begin typesetting papers within a few hours of time...An experienced TeX user, on the other hand, will find a systematic and detailed discussion of all LaTeX features, supporting software, and many other advanced technical issues.''
The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other.
The book is organized into 14 chapters, starting with programming concepts such as variables, assignments, input/output, and selection statements; moves onto loops; and then solves problems using both the ‘programming concept’ and the ‘power of MATLAB’ side-by-side. In-depth coverage is given to input/output, a topic that is fundamental to many engineering applications. Vectorized Code has been made into its own chapter, in order to emphasize the importance of using MATLAB efficiently. There are also expanded examples on low-level file input functions, Graphical User Interfaces, and use of MATLAB Version R2012b; modified and new end-of-chapter exercises; improved labeling of plots; and improved standards for variable names and documentation.
This book will be a valuable resource for engineers learning to program and model in MATLAB, as well as for undergraduates in engineering and science taking a course that uses (or recommends) MATLAB.Presents programming concepts and MATLAB built-in functions side-by-sideSystematic, step-by-step approach, building on concepts throughout the book, facilitating easier learningSections on common pitfalls and programming guidelines direct students towards best practice
If you have never mined before, this book will ensure that you know what mining is all about. If you are familiar with Bitcoin mining, then it will help you to optimize your mining operations at a deeper level. A basic understanding of computers and operating systems is assumed, and some familiarity with cryptocurrency basics would be an added advantage.What You Will LearnGet introduced to Bitcoin mining from the ground upFind out about mining software and the different types of mining hardwareMaster setup techniques to enable efficient miningExamine the pros and cons of the different types of mining hardwareDeduce the differences between solo and pool miningTake a peek into professional mining farmsExplore the future of mining and blockchain-based applicationsIn Detail
Blockchain is being billed as the technology of the future. Bitcoin is the first application of that technology. Mining is what makes it all possible. Exploring mining from a practical perspective will help you make informed decisions about your mining setup. Understanding what the future may hold for blockchains, and therefore for mining, will help you position yourself to take advantage of the impending changes.
This practical guide starts with an introduction to Bitcoin wallets, as well as mining hardware and software. You will move on to learn about different mining techniques using the CPU, GPU, FPGA, and ultimately the ASIC as an example. After this, you will gain an insight into solo mining and pool mining, and see the differences between the two. The book will then walk you through large-scale mining and the challenges faced during such operations. Finally, you will take a look into the future to see a world where blockchain-based applications are commonplace and mining is ubiquitous.Style and approach
This is a practical guide that includes detailed step-by-step instructions and examples on each essential concept of Bitcoin mining.