{"title":"Computer Science \u0026 Technology","description":"\u003cp\u003eBrowse 850+ \u003cstrong\u003ecomputer science and technology\u003c\/strong\u003e textbooks — programming languages, AI and machine learning, cybersecurity, networking, software engineering, and IT certifications.\u003c\/p\u003e\u003ch2\u003eWhat's inside\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eProgramming (Python, Java, C++, JavaScript) and algorithms\u003c\/li\u003e\n\u003cli\u003eAI, machine learning, and data science\u003c\/li\u003e\n\u003cli\u003eCybersecurity, network security, and CompTIA prep\u003c\/li\u003e\n\u003cli\u003eSoftware engineering and DevOps\u003c\/li\u003e\n\u003cli\u003eRobotics, embedded systems, and hardware\u003c\/li\u003e\n\u003cli\u003eComputer graphics, game development, and animation\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eWho it's for\u003c\/h2\u003e\u003cp\u003eComputer science and software engineering students, IT professionals, developers, security analysts, data scientists, and self-taught programmers.\u003c\/p\u003e\u003ch2\u003eWhy Book Shop Now\u003c\/h2\u003e\u003cp\u003eAuthentic editions from CRC Press, Springer, Cengage, Wiley, and O'Reilly. Verified ISBNs and fast US shipping.\u003c\/p\u003e","products":[{"product_id":"latent-variable-modeling-using-r-a-step-by-step-guide","title":"Latent Variable Modeling Using R A Step by Step Guide","description":"\u003cp\u003eThis step-by-step guide is written for \u003cb\u003eR\u003c\/b\u003e and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the \u003ci\u003elavaan\u003c\/i\u003e package, this book is designed to help readers quickly understand LVMs and their analysis in \u003cb\u003eR\u003c\/b\u003e. The author reviews the reasoning behind the syntax selected and provides examples that demonstrate how to analyze data for a variety of LVMs.　 Featuring examples applicable to psychology, education, business, and other social and health sciences, minimal text is devoted to theoretical underpinnings. The material is presented without　the use of matrix algebra. As a whole the book prepares readers to write about and interpret LVM results they obtain in \u003cb\u003eR\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eEach chapter features background information, boldfaced key　 terms defined in the glossary, detailed interpretations of \u003cb\u003eR\u003c\/b\u003e output, descriptions of how to write the analysis of results for publication, a summary, \u003cb\u003eR\u003c\/b\u003e based practice exercises (with solutions included in the back of the book), and references and related readings. Margin notes help readers better understand LVMs and write their own \u003cb\u003eR\u003c\/b\u003e syntax. Examples using data from published work across a variety of disciplines demonstrate how to use \u003cb\u003eR\u003c\/b\u003e syntax for analyzing and interpreting results. \u003cb\u003eR\u003c\/b\u003e functions, syntax, and the corresponding results appear in gray boxes to help readers quickly locate this material. A unique index helps readers quickly locate R\u003cb\u003e　\u003c\/b\u003efunctions, packages, and datasets. The book and accompanying website at http:\/\/blogs.baylor.edu\/rlatentvariable\/ provides all of the data for the book's examples and exercises as well as \u003cb\u003eR\u003c\/b\u003e syntax so readers can replicate the analyses. The book reviews how to enter the data into \u003cb\u003eR\u003c\/b\u003e, specify the LVMs, and obtain and interpret the estimated parameter values.\u003c\/p\u003e \u003cp\u003eThe book opens with the fundamentals of using \u003cb\u003eR\u003c\/b\u003e including how to download the program, use functions, and enter and manipulate data. Chapters 2 and 3 introduce and then extend path models to include latent variables. Chapter 4 shows readers how to analyze a latent variable model with data from more than one group, while Chapter 5 shows how to analyze a latent variable model with data from more than one time period. Chapter 6 demonstrates the analysis of dichotomous variables, while Chapter 7 demonstrates how to analyze LVMs with missing data. Chapter 8 focuses on sample size determination using Monte Carlo methods, which can be used with a wide range of statistical models and account for missing data. The final chapter examines hierarchical LVMs, demonstrating both higher-order and bi-factor approaches. 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The text fully maps to the new CompTIA Security+ SY0-701 Certification Exam, providing thorough coverage of all domain objectives. In addition to its comprehensive coverage of the fundamental essentials of network and computer security, the 8th edition includes expanded coverage of information security management, artificial intelligence, compliance, cryptography and cloud and virtualization security. 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It is almost impossible to write a mathematics book that can be read and enjoyed by people of all levels and backgrounds, yet Aigner and Ziegler accomplish this feat of exposition with virtuoso style. [...] This book does an invaluable service to mathematics, by illustrating for non-mathematicians what it is that mathematicians mean when they speak about beauty.”\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Reviews\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"... Inside PFTB (Proofs from The Book) is indeed a glimpse of mathematical heaven, where clever insights and beautiful ideas combine in astonishing and glorious ways. There is vast wealth within its pages, one gem after another. ... 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The book also covers basic data structures and sorting algorithms, which is a nice bonus. ... every 1 – pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types.\" (Steve Yegge, Get that Job at Google)\u003c\/p\u003e\u003cp\u003e\"Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. ... Every programmer should read this book, and anyone working in the field should keep it close to hand. ... This is the best investment ... a programmer or aspiring programmer can make.\" (Harold Thimbleby, Times Higher Education)\u003c\/p\u003e\u003cp\u003e\"It is wonderful to open to a random spot and discover aninteresting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... 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