The cause of their failure was a complete mystery.Five months later, a seemingly unrelated event occurred. A computer security firm in Belarus was called in to troubleshoot some computers in Iran that were caught in a reboot loop—crashing and rebooting repeatedly. At first, technicians with the firm believed the malicious code they found on the machines was a simple, routine piece of malware. But as they and other experts around the world investigated, they discovered a virus of unparalleled complexity and mysterious provenance and intent. They had, they soon learned, stumbled upon the world’s first digital weapon.Stuxnet, as it came to be known, was unlike any other virus or worm built before: It was the first attack that reached beyond the computers it targeted to physically destroy the equipment those computers controlled. It was an ingenious attack, jointly engineered by the United States and Israel, that worked exactly as planned, until the rebooting machines gave it all away. And the discovery of Stuxnet was just the beginning: Once the digital weapon was uncovered and deciphered, it provided clues to other tools lurking in the wild. Soon, security experts found and exposed not one but three highly sophisticated digital spy tools that came from the same labs that created Stuxnet. The discoveries gave the world its first look at the scope and sophistication of nation-state surveillance and warfare in the digital age.Kim Zetter, a senior reporter at Wired, has covered hackers and computer security since 1999 and is one of the top journalists in the world on this beat. She was among the first reporters to cover Stuxnet after its discovery and has authored many of the most comprehensive articles about it. In COUNTDOWN TO ZERO DAY: Stuxnet and the Launch of the World’s First Digital Weapon, Zetter expands on this work to show how the code was designed and unleashed and how its use opened a Pandora’s Box, ushering in an age of digital warfare in which any country’s infrastructure—power grids, nuclear plants, oil pipelines, dams—is vulnerable to the same kind of attack with potentially devastating results. A sophisticated digital strike on portions of the power grid, for example, could plunge half the U.S. into darkness for weeks or longer, having a domino effect on all other critical infrastructures dependent on electricity.

    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 book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    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

    Legendary testing expert James Whittaker, until recently a Google testing leader, and two top Google experts reveal exactly how Google tests software, offering brand-new best practices you can use even if you're not quite Google's size...yet! Breakthrough Techniques You Can Actually Use Discover 100% practical, amazingly scalable techniques for analyzing risk and planning tests...thinking like real users...implementing exploratory, black box, white box, and acceptance testing...getting usable feedback...tracking issues...choosing and creating tools...testing "Docs & Mocks," interfaces, classes, modules, libraries, binaries, services, and infrastructure...reviewing code and refactoring...using test hooks, presubmit scripts, queues, continuous builds, and more. With these techniques, you can transform testing from a bottleneck into an accelerator-and make your whole organization more productive!

    Authors Katherine Daniels and Jennifer Davis provide with actionable strategies you can use to engineer sustainable changes in your environment regardless of your level within your organization.

    The problem is that all of those lines of JavaScript code can slow down your apps. This book reveals techniques and strategies to help you eliminate performance bottlenecks during development. You'll learn how to improve execution time, downloading, interaction with the DOM, page life cycle, and more. Yahoo! frontend engineer Nicholas C. Zakas and five other JavaScript experts -- Ross Harmes, Julien Lecomte, Steven Levithan, Stoyan Stefanov, and Matt Sweeney -- demonstrate optimal ways to load code onto a page, and offer programming tips to help your JavaScript run as efficiently and quickly as possible. You'll learn the best practices to build and deploy your files to a production environment, and tools that can help you find problems once your site goes live. Identify problem code and use faster alternatives to accomplish the same task Improve scripts by learning how JavaScript stores and accesses data Implement JavaScript code so that it doesn't slow down interaction with the DOM Use optimization techniques to improve runtime performance Learn ways to ensure the UI is responsive at all times Achieve faster client-server communication Use a build system to minify files, and HTTP compression to deliver them to the browser

    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.

    In Web Form Design, Luke Wroblewski draws on original research, his considerable experience at Yahoo! and eBay, and the perspectives of many of the field's leading designers to show you everything you need to know about designing effective and engaging Web forms.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    At under 200 pages it's quick reading too. Makes a great airplane book.

    A pocket-sized overview of roles, artifacts and the sprint cycle, adapted from the bestseller The Elements of Scrum by Chris Sims & Hillary Louise Johnson

    A no-nonsense tutorial and reliable reference, this book features thoroughly tested real-world examples. The most important language and library features are demonstrated with deliberately simple sample programs, but they aren't fake and they don't cut corners. More importantly, all of the programs have been updated for J2SE 5.0 and should make good starting points for your own code. You won't find any toy examples here. This is a book for programmers who want to write real code to solve real problems. Cay S. Horstmann is a professor of computer science at San Jose State University. Previously he was vice president and chief technology officer of Preview Systems Inc. and a consultant on C++, Java, and Internet programming for major corporations, universities, and organizations. Gary Cornell has written or cowritten more than twenty popular computer books. He has a Ph.D. from Brown University and has been a visiting scientist at IBM Watson Laboratories, as well as a professor at the University of Connecticut.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    This book claimed that through a piece of software called a "browser," which accesses "web sites," the world economy and our daily lives would change forever. Would you have believed even 10 percent of that book? Did you take advantage of the first Internet wave and get ahead of the curve? "Pull" is the blueprint to the next disruptive wave. Some call it Web 3.0; others call it the semantic web. It's a fundamental transition from pushing information to pulling, using a new way of thinking and collaborating online. Using the principles of this book, you will slash 5-20 percent off your bottom line, make your customers happier, accelerate your industry, and prepare your company for the twenty-first century. It isn't going to be easy, and you don't have any choice. By 2015, your company will be more agile and your processes more flexible than you ever thought possible. The semantic web leads to possibilities straight from science fiction, such as buildings that can order their own supplies, eliminating the IRS, and lawyers finally making sense. But it also leads to major changes in every field, from shipping and retail distribution to health care and financial reporting. Through clear examples, case studies, principles, and scenarios, business strategist David Siegel takes you on a tour of this new world. You'll learn: -Which industries are already ahead. -Which industries are already dead. -How to make the power shift from pushing to pulling information. -How software, hardware, media, and marketing will all change. -How to plan your own strategy for embracing the semantic web. We are at the beginning of a new technology curve that will affect all areas of business. Right now, you have a choice. You can decide to start preparing for the exciting opportunities that lay ahead or you can leave this book on the shelf and get left in the dust like last time.