If programming intrigues you (for whatever reason), Beginning Programming All-In-One Desk Reference For Dummies is like having a starter programming library all in one handy, if hefty, book.In this practical guide, you'll find out about algorithms, best practices, compiling, debugging your programs, and much more. The concepts are illustrated in several different programming languages, so you'll get a feel for the variety of languages and the needs they fill.Inside you'll discover seven minibooks:Getting Started: From learning methods for writing programs to becoming familiar with types of programming languages, you'll lay the foundation for your programming adventure with this minibook. Programming Basics: Here you'll dive into how programs work, variables, data types, branching, looping, subprograms, objects, and more. Data Structures: From structures, arrays, sets, linked lists, and collections, to stacks, queues, graphs, and trees, you'll dig deeply into the data. Algorithms: This minibook shows you how to sort and search algorithms, how to use string searching, and gets into data compression and encryption. Web Programming: Learn everything you need to know about coding for the web: HyperText. Markup Language (better known simply as HTML), CSS, JavaScript, PHP, and Ruby. Programming Language Syntax: Introduces you to the syntax of various languages - C, C++, Java, C#, Perl, Python, Pascal, Delphi, Visual Basic, REALbasic - so you know when to use which one. Applications: This is the fun part where you put your newly developed programming skills to work in practical ways. Additionally, Beginning Programming All-In-One Desk Reference For Dummies shows you how to decide what you want your program to do, turn your instructions into "machine language" that the computer understands, use programming best practices, explore the "how" and "why" of data structuring, and more. And you'll get a look into various applications like database management, bioinformatics, computer security, and artificial intelligence. After you get this book and start coding, you'll soon realize that -- wow! You're a programmer!

    Why wade through a 600-page book when you can begin working productively in a matter of minutes? It's an ideal primer for Mac and PC users of the Internet who need to know a little bit about UNIX on the systems they visit.This book is the most effective introduction to UNIX in print. The fourth edition covers the highlights of the Linux operating system. It's a handy book for someone just starting with UNIX or Linux, as well as someone who encounters a UNIX system on the Internet. And it now includes a quick-reference card.Topics covered include: Linux operating system highlightsLogging in and logging outWindow systems (especially X/Motif)Managing UNIX files and directoriesSending and receiving mailRedirecting input/outputPipes and filtersBackground processingBasic network commandsv

    Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." — Mariatta Wijaya, Python Core Developer"This book makes you write better Python code!" — Bob Belderbos, Software Developer at Oracle"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." — Ben Felder, Pythonista"It's like having a seasoned tutor explaining, well, tricks!" — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    With metaprogramming, you can produce elegant, clean, and beautiful programs. Once the domain of expert Rubyists, metaprogramming is now accessible to programmers of all levels. This thoroughly revised and updated second edition of the bestselling Metaprogramming Ruby explains metaprogramming in a down-to-earth style and arms you with a practical toolbox that will help you write your best Ruby code ever.Dig under the surface and explore Ruby's most advanced feature: a collection of techniques and tricks known as metaprogramming. In this book, you'll learn metaprogramming as an essential component of Ruby and discover the deep, non-obvious details of the language. Once you understand the tenets of Ruby, including the object model, scopes, and singleton classes, you're on your way to applying metaprogramming both in your daily work assignments and in your fun, after-hours projects.Metaprogramming Ruby, Second Edition makes mastering the language enjoyable. The book is packed with: Pragmatic examples of metaprogramming in action, many of which come straight from real-life gems such as Rails.Programming challenges that let you experiment and play with some of the most out-there metaprogramming concepts.Metaprogramming spells--33 practical recipes and idioms that you can study and apply right now, to write code that is sure to impress.This completely revised new edition covers the new features in Ruby 2.0 and 2.1, and contains code from the latest Ruby libraries, including Rails 4. Most examples are new, from the wild, with more recent libraries. And the book reflects current ideas of when and how much metaprogramming you should use.Whether you're a Ruby apprentice on the path to mastering the language or a Ruby wiz in search of new tips, this book is for you.What You Need: Ruby 2.x, Ruby 1.9, or a recent version of JRuby.

    You want to take your web skills to the next level. And you're finally ready to add "programmer" to the resume. It sounds like you're ready to learn the Web's hottest programming language: JavaScript. Head First JavaScript is your ticket to going beyond copying and pasting the code from someone else's web site, and writing your own interactive web pages. With Head First JavaScript, you learn:The basics of programming, from variables to types to looping How the web browser runs your code, and how you can talk to the browser with your code Why you'll never have to worry about casting, overloading, or polymorphism when you're writing JavaScript code How to use the Document Object Model to change your web pages without making your users click buttons If you've ever read a Head First book, you know what to expect -- a visually rich format designed for the way your brain works. Head First JavaScript is no exception. It starts where HTML and CSS leave off, and takes you through your first program into more complex programming concepts -- like working directly with the web browser's object model and writing code that works on all modern browsers. Don't be intimidated if you've never written a line of code before! In typical Head First style, Head First JavaScript doesn't skip steps, and we're not interested in having you cut and paste code. You'll learn JavaScript, understand it, and have a blast along the way. So get ready... dynamic and exciting web pages are just pages away.

    It includes a complete chapter on C++ and an overall organization designed to appeal to the many programmers who view C as a stepping stone to C++ and the object-oriented paradigm. This edition also features an increased emphasis on modules and ADTs, which are essential concepts for creating reusable code and which show how to use header files to tie together a multi-file program. computer science students.

    "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems."

    Commercial game development expert Robert Nystrom presents an array of general solutions to problems encountered in game development. For example, you'll learn how double-buffering enables a player to perceive smooth and realistic motion, and how the service locator pattern can help you provide access to services such as sound without coupling your code to any particular sound driver or sound hardware. Games have much in common with other software, but also a number of unique constraints. Some of the patterns in this book are well-known in other domains of software development. Other of the patterns are unique to gaming. In either case, Robert Nystrom bridges from the ivory tower world of software architecture to the in-the-trenches reality of hardcore game programming. You'll learn the patterns and the general problems that they solve. You'll come away able to apply powerful and reusable architectural solutions that enable you to produce higher quality games with less effort than before. Applies classic design patterns to game programming. Introduces new patterns specific to game programming. Brings abstract software architecture down to Earth with approachable writing and an emphasis on simple code that shows each pattern in practice. What you'll learn Overcome architectural challenges unique to game programming Apply lessons from the larger software world to games. Tie different parts of a game (graphics, sound, AI) into a cohesive whole. Create elegant and maintainable architecture. Achieve good, low-level performance. Gain insight into professional, game development. Who this book is forGame Programming Patterns is aimed at professional game programmers who, while successful in shipping games, are frustrated at how hard it sometimes is to add and modify features when a game is under development. Game Programming Patterns shows how to apply modern software practices to the problem of game development while still maintaining the blazing-fast performance demanded by hard-core gamers. Game Programming Patterns also appeals to those learning about game programming in their spare time. Hobbyists and aspiring professionals alike will find much to learn in this book about pathfinding, collision detection, and other game-programming problem domains.

    But do you know how to use them to their greatest possible advantage? Only now, with the publication of Linkers & Loaders, is there an authoritative book devoted entirely to these deep-seated compile-time and run-time processes. The book begins with a detailed and comparative account of linking and loading that illustrates the differences among various compilers and operating systems. On top of this foundation, the author presents clear practical advice to help you create faster, cleaner code. You'll learn to avoid the pitfalls associated with Windows DLLs, take advantage of the space-saving, performance-improving techniques supported by many modern linkers, make the best use of the UNIX ELF library scheme, and much more. If you're serious about programming, you'll devour this unique guide to one of the field's least understood topics. Linkers & Loaders is also an ideal supplementary text for compiler and operating systems courses.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.To get you started--whether you're on Windows, OS X, or Linux--author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.Discover why the command line is an agile, scalable, and extensible technology. Even if you're already comfortable processing data with, say, Python or R, you'll greatly improve your data science workflow by also leveraging the power of the command line.Obtain data from websites, APIs, databases, and spreadsheetsPerform scrub operations on plain text, CSV, HTML/XML, and JSONExplore data, compute descriptive statistics, and create visualizationsManage your data science workflow using DrakeCreate reusable tools from one-liners and existing Python or R codeParallelize and distribute data-intensive pipelines using GNU ParallelModel data with dimensionality reduction, clustering, regression, and classification algorithms

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    I've spent the last couple years actively teaching Haskell online and in person. Along the way, I started keeping notes on exercises and methods of teaching specific concepts and techniques in Haskell that eventually turned into my guide for learning haskell. That experience led me to work on this book.If you are new to programming entirely, Haskell is a great first language. You may have noticed the trend of "Functional Programming in [Imperative Language]" books and tutorials and learning Haskell gets right to the heart of what functional programming is. Languages such as Java are gradually adopting functional concepts, but most such languages were not designed to be functional languages, after all. We would not encourage you to learn Haskell as an only language, but because Haskell is a pure functional language, it is a fertile environment for mastering functional programming techniques. That way of thinking and problem solving is useful, no matter what other languages you might know or learn.Haskell is not a difficult language to use. Quite the opposite. I'm now able to tackle problems that I couldn't have tackled when I was primarily a Clojure, Common Lisp, or Python user. Haskell is difficult to teach effectively.

    It has been winning converts from dynamic language enthusiasts as well as users of traditional compiled languages. The former appreciate the robustness and efficiency that Go's lightweight type system brings to their code; the latter find Go's simplicity and fast tools a refreshing change. Thanks to its well-designed standard libraries and its excellent support for concurrent programming, Go is fast becoming the language of choice for distributed systems. The Go Programming Language is the definitive book on Go for the working programmer. It assumes no prior knowledge of Go, nor any other specific programming language, so you'll find it an accessible guide whether you come from JavaScript, Ruby, Python, Java, or C++. The book will quickly get you started using Go effectively from the beginning, and by the end, you will know how to use it well to write clear, idiomatic and efficient programs to solve real-world problems. You'll understand not just how to use its standard libraries, but how they work, and how to apply the same design techniques to your own projects. The earlier chapters will introduce you to the basic concepts of Go programming---numbers, strings, functions---while at the same time presenting important computer science concepts like recursion, and useful examples of graphics, UTF-8, and error handling. The chapters on methods and interfaces will show you a new way to think about object-oriented programming; the chapter on concurrency explains why concurrency is so important in modern programming, and how Go helps you handle it well. You'll also learn about Go's pragmatic but effective approach to testing; how to build, test, and manage projects using the go tool, and the art of metaprogramming using reflection. The book contains hundreds of interesting and practical examples that cover the whole language and a wide range of applications. The code samples from the book are available for download from gopl.io.