. . [with] new information, photographs and first-hand accounts." --FlypastDuring the 1950s, plans were being drawn at North American Aviation in Southern California for an incredible Mach-3 strategic bomber. The concept was born as a result of General Curtis LeMay's desire for a heavy bomber with the weapon load and range of the subsonic B-52 and a top speed in excess of the supersonic medium bomber, the B-58 Hustler. However, in April 1961, Defense Secretary McNamara stopped the production go-ahead for the B-70 because of rapid cost escalation and the USSR's newfound ability to destroy aircraft at extremely high altitude using either missiles or the new Mig-25 fighter. Nevertheless, in 1963 plans for the production of three high-speed research aircraft were approved and construction proceeded. In September 1964 the first Valkyrie, now re-coded A/V-1, took to the air for the first time and in October went supersonic.This book is the most detailed description of the design, engineering and research that went into this astounding aircraft. It is full of unpublished details, photographs and firsthand accounts from those closely associated with the project. Although never put into full production, this giant six-engined aircraft became famous for its breakthrough technology, and the spectacular images captured on a fatal air-to-air photo shoot when an observing Starfighter collided with Valkyrie A/V-2 which crashed into the Mojave Desert."Well-illustrated with numerous diagrams and black and white photographs, the book provides an interesting insight into one of the so-called 'white elephant' projects of the 1960s." --Jets Monthly

    But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that.This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to:- How to measure your own level of uncertainty in a conclusion or belief- Calculate Bayes theorem and understand what it's useful for- Find the posterior, likelihood, and prior to check the accuracy of your conclusions- Calculate distributions to see the range of your data- Compare hypotheses and draw reliable conclusions from themNext time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

    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.

    In order to enable students to apply the basics of data modeling to real models, the book addresses the realities of developing systems in real-world situations by assessing the merits of a variety of possible solutions as well as using language and diagramming methods that represent industry practice.This revised edition has been given significantly expanded coverage and reorganized for greater reader comprehension even as it retains its distinctive hallmarks of readability and usefulness. Beginning with the basics, the book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management. It includes an entirely new section discussing the development of logical and physical modeling, along with new material describing a powerful technique for model verification. It also provides an excellent resource for additional lectures and exercises.This text is the ideal reference for data modelers, data architects, database designers, DBAs, and systems analysts, as well as undergraduate and graduate-level students looking for a real-world perspective.

    It also covers MySQL, and PostgreSQL. It contains examples which have been tested against each SQL platform, with incompatibilities or platform distinctives called out and explained.

    It is offered for sale in print format as a convenience.Get a head start evaluating Windows 8.1 - with early technical insights from award-winning journalist and Windows expert Ed Bott. Based on the Windows 8.1 Preview release, this guide introduces new features and capabilities, with scenario-based advice on how Windows 8.1 can meet the needs of your business. Get the high-level overview you need to begin preparing your deployment now.Preview new features and enhancements, including:How features compare to Windows 7 and Windows XP The Windows 8.1 user experience Deployment Security features Internet Explorer 11 Delivering Windows apps Recovery options Networking and remote access Managing mobile devices Virtualization Windows RT 8.1

    Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas: Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself. The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research. Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.

    In this one-of-a-kind text, author V. Anton Spraul breaks down the ways that programmers solve problems and teaches you what other introductory books often ignore: how to Think Like a Programmer. Each chapter tackles a single programming concept, like classes, pointers, and recursion, and open-ended exercises throughout challenge you to apply your knowledge. You'll also learn how to:Split problems into discrete components to make them easier to solve Make the most of code reuse with functions, classes, and libraries Pick the perfect data structure for a particular job Master more advanced programming tools like recursion and dynamic memory Organize your thoughts and develop strategies to tackle particular types of problems Although the book's examples are written in C++, the creative problem-solving concepts they illustrate go beyond any particular language; in fact, they often reach outside the realm of computer science. As the most skillful programmers know, writing great code is a creative art—and the first step in creating your masterpiece is learning to Think Like a Programmer.

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    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

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    Never again will you order the Poisson Distribution in a French restaurant!This updated version features all new material.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Emphasis is placed upon the theoretical basis of each type of instrument, its optimal area of application, its sensitivity, its precision, and its limitations. The text also introduces students to elementary integrated circuitry, microprocessors and computers, and treatment of analytical data.

    Since that time, this text has been revised, tweaked, and enhanced to account for the changes found within each release of the .NET platform (1.1, 2.0, 3.0 and now 3.5)..NET 3.0 was more of an augmentative release, essentially providing three new APIs: Windows Presentation Foundation (WPF), Windows Communication Foundation (WCF) and Windows Workflow Foundation (WF). As you would expect, coverage of the "W's" has been expanded a great deal in this version of the book from the previous Special Edition text.Unlike .NET 3.0, .NET 3.5 provides dozens of C# language features and .NET APIs. This edition of the book will walk you through all of this material using the same readable approach as was found in previous editions. Rest assured, you'll find detailed coverage of Language Integrated Query (LINQ), the C# 2008 language changes (automatic properties, extension methods, anonymous types, etc.) and the numerous bells and whistles of Visual Studio 2008. What you'll learn Everything you need to knowget up to speed with C# 2008 quickly and efficiently. Discover all the new .NET 3.5 featuresLanguage Integrated Query, anonymous types, extension methods, automatic properties, and more. Get a professional footholdtargeted to appeal to experienced software professionals, this book gives you the facts you need the way you need to see them. A rock-solid foundationfocuses on everything you need to be a successful .NET 3.5 programmer, not just the new features. Get comfortable with all the core aspects of the platform including assemblies, remoting, Windows Forms, Web Forms, ADO.NET, XML web services, and much more. Who this book is forIf you're checking out this book for the first time, understand that it targets experienced software professionals and/or students of computer science (so please don't expect three chapters devoted to "for" loops). The mission of this text is to provide you with a rock-solid foundation to the C# 2008 programming language and the core aspects of the .NET platform (object-oriented programming, assemblies, file IO, Windows Forms/WPF, ASP.NET, ADO.NET, WCF, WF, etc.). Once you digest the information presented in these 33 chapters, you'll be in a perfect position to apply this knowledge to your specific programming assignments, and you'll be well equipped to explore the .NET universe on your own terms. "