You'll find the latest methodologies for the design of effective test cases, including information on psychological and economic principles, managerial aspects, test tools, high-order testing, code inspections, and debugging. Accessible, comprehensive, and always practical, this edition provides the key information you need to test successfully, whether a novice or a working programmer. Buy your copy today and end up with fewer bugs tomorrow.

    Kevin Mitnick's last book sold 40,000 units in North America.As the clich� goes, information is power. In this age of technology, an increasing majority of the world's information is stored electronically. It makes sense then that we rely on high-tech electronic protection systems to guard that information. As professional hackers, Johnny Long and Kevin Mitnick get paid to uncover weaknesses in those systems and exploit them. Whether breaking into buildings or slipping past industrial-grade firewalls, their goal has always been the same: extract the information using any means necessary. After hundreds of jobs, they have discovered the secrets to bypassing every conceivable high-tech security system. This book reveals those secrets; as the title suggests, it has nothing to do with high technology.

    If you're a business or IT professional, this short hands-on guide teaches you how to pull and transform data with SQL in significant ways. You will quickly master the fundamentals of SQL and learn how to create your own databases.Author Thomas Nield provides exercises throughout the book to help you practice your newfound SQL skills at home, without having to use a database server environment. Not only will you learn how to use key SQL statements to find and manipulate your data, but you'll also discover how to efficiently design and manage databases to meet your needs.You'll also learn how to:Explore relational databases, including lightweight and centralized modelsUse SQLite and SQLiteStudio to create lightweight databases in minutesQuery and transform data in meaningful ways by using SELECT, WHERE, GROUP BY, and ORDER BYJoin tables to get a more complete view of your business dataBuild your own tables and centralized databases by using normalized design principlesManage data by learning how to INSERT, DELETE, and UPDATE records

    Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a “Feynmanesque” overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers.

    Nageswara Rao, Wiley India, 9789351199427

    Topics are broken down into three major conceptual pieces: Virtualization, Concurrency, and Persistence. Includes all major components of modern systems including scheduling, virtual memory management, disk subsystems and I/O, file systems, and even a short introduction to distributed systems.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    Salient features: Expanded coverage on software tools including user interfaces; enhanced treatment of language processors with addition of three new chapters on the topic; includes detailed discussions on assemblers, macroprocessors, compilers, and interpreters, and linkers, security in a distributed environment; complementary new chapter devoted to protection; process management and information management; numerous examples from contemporary systems like UNIX and IBM PC illustrating concepts and techniques; indispensible text for undergraduate and postgraduate students of computer science and engineering; an invaluable reference tools for system analysis and computer professionals.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    He is a good lover; he adores Adah and can forego any comfort of the world for her sake. But even then, he is not happy. Because his true calling is not in the corporate; it’s in writing. After much deliberation, he takes the plunge and leaves his job to write full-time. While he struggles for balance, Adah also starts keeping herself away. Was she really in love with him, or was it just a facade?Amidst all this confusion, Pihu Sharma enters his life – his first ever fan, who seems to be head over heels in love with him. While Ranbir’s equation with Adah deteriorates, Pihu leaves behind her luxurious life to shift in with him. Is this the true love Ranbir had been waiting for?Join Ranbir as he makes his way through a world that kills for money and dies for love.

    Although dashboards are potentially powerful, this potential is rarely realized. The greatest display technology in the world won't solve this if you fail to use effective visual design. And if a dashboard fails to tell you precisely what you need to know in an instant, you'll never use it, even if it's filled with cute gauges, meters, and traffic lights. Don't let your investment in dashboard technology go to waste.This book will teach you the visual design skills you need to create dashboards that communicate clearly, rapidly, and compellingly. Information Dashboard Design will explain how to:Avoid the thirteen mistakes common to dashboard design Provide viewers with the information they need quickly and clearly Apply what we now know about visual perception to the visual presentation of information Minimize distractions, cliches, and unnecessary embellishments that create confusion Organize business information to support meaning and usability Create an aesthetically pleasing viewing experience Maintain consistency of design to provide accurate interpretation Optimize the power of dashboard technology by pairing it with visual effectiveness Stephen Few has over 20 years of experience as an IT innovator, consultant, and educator. As Principal of the consultancy Perceptual Edge, Stephen focuses on data visualization for analyzing and communicating quantitative business information. He provides consulting and training services, speaks frequently at conferences, and teaches in the MBA program at the University of California in Berkeley. He is also the author of Show Me the Numbers: Designing Tables and Graphs to Enlighten. Visit his website at www.perceptualedge.com.

    The best-selling reference focuses on the basic materials and methods used in building construction. Emphasizing common construction systems such as light wood frames, masonry bearing walls, steel frames, and reinforced concrete, the new edition includes new coverage of green design and energy-efficient construction energies, and is based on the International Building Code(r).

    

    Long before the advent of computers, human beings were collecting, storing, and organizing information: from Ice Age taxonomies to Sumerian archives, Greek libraries to Dark Age monasteries.Today, we stand at a precipice, as our old systems struggle to cope with what designer Richard Saul Wurman called a "tsunami of data." With some historical perspective, however, we can begin to understand our predicament not just as the result of technological change, but as the latest chapter in an ancient story that we are only beginning to understand.Spanning disciplines from evolutionary theory and cultural anthropology to the history of books, libraries, and computer science, writer and information architect Alex Wright weaves an intriguing narrative that connects such seemingly far-flung topics as insect colonies, Stone Age jewelry, medieval monasteries, Renaissance encyclopedias, early computer networks, and the World Wide Web. Finally, he pulls these threads together to reach a surprising conclusion, suggesting that the future of the information age may lie deep in our cultural past.

    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.