The authors' enthusiasm for their subject is compelling as they present abstract concepts in a humorous and easy-to-grasp fashion. Together, these books will open new doors of thought to anyone who wants to find out what computing is really about. The Little Schemer introduces computing as an extension of arithmetic and algebra; things that everyone studies in grade school and high school. It introduces programs as recursive functions and briefly discusses the limits of what computers can do. The authors use the programming language Scheme, and interesting foods to illustrate these abstract ideas. The Seasoned Schemer informs the reader about additional dimensions of computing: functions as values, change of state, and exceptional cases. The Little LISPer has been a popular introduction to LISP for many years. It had appeared in French and Japanese. The Little Schemer and The Seasoned Schemer are worthy successors and will prove equally popular as textbooks for Scheme courses as well as companion texts for any complete introductory course in Computer Science.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.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 dataWonderfully 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

    "Monitoring Monitoring" explains what makes your monitoring less than stellar, and provides a practical approach to designing and implementing a monitoring strategy, from the application down to the hardware in the datacenter and everything in between.In the world of technical operations, monitoring is core to everything you do. In today s changing landscape of microservices, cloud infrastructure, and more, monitoring is experiencing a new surge of growth, bringing along new methodologies, new ways of thinking, and new tools.Complete with a primer on statistics and a monitoring vocabulary, this book helps you identify the main areas you need to monitor and shows you how to approach them. It s ideal for operations engineers, system administrators, system and software engineers, site reliability engineers, network engineers, and other operations professionals."

    Whether you are a beginner or an experienced administrator, you'll quickly be able to apply its principles and advice to your everyday problems.The book approaches Unix system administration from the perspective of your job -- the routine tasks and troubleshooting that make up your day. Whether you're dealing with frustrated users, convincing an uncomprehending management that you need new hardware, rebuilding the kernel, or simply adding new users, you'll find help in this book. You'll also learn about back up and restore and how to set up printers, secure your system, and perform many other system administration tasks. But the book is not for full-time system administrators alone. Linux users and others who administer their own systems will benefit from its practical, hands-on approach.This second edition has been updated for all major Unix platforms, including SunOS 4.1, Solaris 2.4, AIX 4.1, Linux 1.1, Digital Unix, OSF/1, SCO Unix Version 3, HP/UX Versions 9 and 10, and IRIX Version 6. The entire book has been thoroughly reviewed and tested on all of the platforms covered. In addition, networking, electronic mail, security, and kernel configuration topics have been expanded substantially.Topics covered include:Starting up and shutting down your system Adding new users Managing processes System security Organizing and planning file systems Planning and performing backups Setting up pointers TCP/IP networking Setting up email Adding terminals and disk drives Setting up and using the accounting system

    Throughout the book, the authors share their insights into Swift to ensure that you understand the hows and whys of Swift and can put that understanding to use in different contexts. After working through the book, you will have the knowledge and confidence to develop your own solutions to a wide range of programming challenges using Swift.

    You'll also receive updates when significant changes are made, as well as the final ebook version. Embedded Android is for Developers wanting to create embedded systems based on Android and for those wanting to port Android to new hardware, or creating a custom development environment. Hackers and moders will also find this an indispensible guide to how Android works.

    It is a great book, not just for beginning programmers, but also for computer users who would want to have an idea what is happening behind the scenes as they work with various computer programs. In this book, you are going to learn what the C programming language entails, how to write conditions, expressions, statements and even commands, for the language to perform its functions efficiently. You will learn too how to organize relevant expressions so that after compilation and execution, the computer returns useful results and not error messages. Additionally, this book details the data types that you need for the C language and how to present it as well. Simply put, this is a book for programmers, learners taking other computer courses, and other computer users who would like to be versed with the workings of the most popular computer language, C. In this book You'll learn: What Is The C Language? Setting Up Your Local Environment The C Structure and Data Type C Constants and Literals C Storage Classes Making Decisions In C The Role Of Loops In C Programming Functions in C Programming Structures and Union in C Bit Fields and Typedef Within C. C Header Files and Type Casting Benefits Of Using The C Language ...and much more!! Download your copy today! click the BUY button and download it right now!

    You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.Get a straightforward synopsis of the social web landscapeUse adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, LinkedIn, and Google+Learn how to employ easy-to-use Python tools to slice and dice the data you collectExplore social connections in microformats with the XHTML Friends NetworkApply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detectionBuild interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Rust's modern, flexible types ensure your program is free of null pointer dereferences, double frees, dangling pointers, and similar bugs, all at compile time, without runtime overhead. In multi-threaded code, Rust catches data races at compile time, making concurrency much easier to use.Written by two experienced systems programmers, this book explains how Rust manages to bridge the gap between performance and safety, and how you can take advantage of it. Topics include:How Rust represents values in memory (with diagrams)Complete explanations of ownership, moves, borrows, and lifetimesCargo, rustdoc, unit tests, and how to publish your code on crates.io, Rust's public package repositoryHigh-level features like generic code, closures, collections, and iterators that make Rust productive and flexibleConcurrency in Rust: threads, mutexes, channels, and atomics, all much safer to use than in C or C++Unsafe code, and how to preserve the integrity of ordinary code that uses itExtended examples illustrating how pieces of the language fit together

    Displaying extraordinary creativity and programming virtuosity, Alexandrescu offers a cutting-edge approach to design that unites design patterns, generic programming, and C++, enabling programmers to achieve expressive, flexible, and highly reusable code. This book introduces the concept of generic components--reusable design templates that produce boilerplate code for compiler consumption--all within C++. Generic components enable an easier and more seamless transition from design to application code, generate code that better expresses the original design intention, and support the reuse of design structures with minimal recoding. The author describes the specific C++ techniques and features that are used in building generic components and goes on to implement industrial strength generic components for real-world applications. Recurring issues that C++ developers face in their day-to-day activity are discussed in depth and implemented in a generic way. These include: Policy-based design for flexibility Partial template specialization Typelists--powerful type manipulation structures Patterns such as Visitor, Singleton, Command, and Factories Multi-method engines For each generic component, the book presents the fundamental problems and design options, and finally implements a generic solution.

    With the many upgraded features, C# is more expressive than ever. However, an in depth understanding is required to get the most out of the language.C# in Depth, Second Edition is a thoroughly revised, up-to-date book that covers the new features of C# 4 as well as Code Contracts. In it, you'll see the subtleties of C# programming in action, learning how to work with high-value features that you'll be glad to have in your toolkit. The book helps readers avoid hidden pitfalls of C# programming by understanding "behind the scenes" issues.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    Weinberg adds new insights and highlights the similarities and differences between now and then. Using a conversational style that invites the reader to join him, Weinberg reunites with some of his most insightful writings on the human side of software engineering.Topics include egoless programming, intelligence, psychological measurement, personality factors, motivation, training, social problems on large projects, problem-solving ability, programming language design, team formation, the programming environment, and much more.Dorset House Publishing is proud to make this important text available to new generations of programmers -- and to encourage readers of the first edition to return to its valuable lessons.

    Together, they ruled big business. They transformed popular culture. And they provoked a national controversy. More than anything, they lived a unique and rollicking American Dream, escaping the broken homes of their youth to produce the most notoriously successful game franchises in history—Doom and Quake— until the games they made tore them apart. This is a story of friendship and betrayal, commerce and artistry—a powerful and compassionate account of what it's like to be young, driven, and wildly creative.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms