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

    The author explains the foundations of project management - project integration, scope, time, cost, quality, human resources, communications, risk, and procurement - using the experiences of real-life businesses. This new fourth edition includes a running case, new PMBOK Guide, and coverage of Microsoft Project 2003. Accompanying the book is a revised companion Web site, www.course.com/mis/schwalbe4e.

    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

    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

    The authors guide you step by step, using construction of a CD database application to give you hands-on experience as you progress from the basic to the complex. You'll start with fundamental concepts like writing Unix programs in C. You'll learn basic system calls, file I/O, interprocess communication, and shell programming. You'll become skilled with the toolkits and libraries for working with user interfaces.The book starts from the basics, explaining how to compile and run your first program. New to this edition are chapters on MySQL(R) access and administration; programming GNOME and KDE; and Linux standards for portable applications. Coverage of kernel programming, device drivers, CVS, grep, and GUI development environments has expanded. This book gives you practical knowledge for real wor ld application.What does this book cover?In this book, you will learn how toDevelop programs to access files and the Linux environment Use the GNU compiler, debugger and other development tools Program data storage aapplications for MySQL and DBM database systems Write programs that take advantage of signals, processes and threads Build graphical user interfaces using both the GTK (for GNOME) and Qt (for KDE) libraries Write device drivers that can be loaded into the Linux kernel Access the network using TCP/IP sockets Write scripts that use grep, regular expressions and other Linux facilities Who is this book for?This book is for programmers with some C or C++ experience, who want to take advantage of the Linux development environment. You should have enough Linux familiarity to have installed and configured users on Linux.

    Written by Peter Morville, author of the groundbreaking Information Architecture for the World Wide Web, the book defines our current age as a state of unlimited findability. In other words, anyone can find anything at any time. Complete navigability.Morville discusses the Internet, GIS, and other network technologies that are coming together to make unlimited findability possible. He explores how the melding of these innovations impacts society, since Web access is now a standard requirement for successful people and businesses. But before he does that, Morville looks back at the history of wayfinding and human evolution, suggesting that our fear of being lost has driven us to create maps, charts, and now, the mobile Internet.The book's central thesis is that information literacy, information architecture, and usability are all critical components of this new world order. Hand in hand with that is the contention that only by planning and designing the best possible software, devices, and Internet, will we be able to maintain this connectivity in the future. Morville's book is highlighted with full color illustrations and rich examples that bring his prose to life.Ambient Findability doesn't preach or pretend to know all the answers. Instead, it presents research, stories, and examples in support of its novel ideas. Are we truly at a critical point in our evolution where the quality of our digital networks will dictate how we behave as a species? Is findability indeed the primary key to a successful global marketplace in the 21st century and beyond. Peter Morville takes you on a thought-provoking tour of these memes and more -- ideas that will not only fascinate but will stir your creativity in practical ways that you can apply to your work immediately.

    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.

    But what is it physically? And where is it really? Our mental map of the network is as blank as the map of the ocean that Columbus carried on his first Atlantic voyage. The Internet, its material nuts and bolts, is an unexplored territory. Until now.In Tubes, journalist Andrew Blum goes inside the Internet's physical infrastructure and flips on the lights, revealing an utterly fresh look at the online world we think we know. It is a shockingly tactile realm of unmarked compounds, populated by a special caste of engineer who pieces together our networks by hand; where glass fibers pulse with light and creaky telegraph buildings, tortuously rewired, become communication hubs once again. From the room in Los Angeles where the Internet first flickered to life to the caverns beneath Manhattan where new fiber-optic cable is buried; from the coast of Portugal, where a ten-thousand-mile undersea cable just two thumbs wide connects Europe and Africa, to the wilds of the Pacific Northwest, where Google, Microsoft, and Facebook have built monumental data centers—Blum chronicles the dramatic story of the Internet's development, explains how it all works, and takes the first-ever in-depth look inside its hidden monuments.This is a book about real places on the map: their sounds and smells, their storied pasts, their physical details, and the people who live there. For all the talk of the "placelessness" of our digital age, the Internet is as fixed in real, physical spaces as the railroad or telephone. You can map it and touch it, and you can visit it. Is the Internet in fact "a series of tubes" as Ted Stevens, the late senator from Alaska, once famously described it? How can we know the Internet's possibilities if we don't know its parts?Like Tracy Kidder's classic The Soul of a New Machine or Tom Vanderbilt's recent bestseller Traffic, Tubes combines on-the-ground reporting and lucid explanation into an engaging, mind-bending narrative to help us understand the physical world that underlies our digital lives.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    You'd like to learn how to do more work with SQL inside the database before pushing data across the network to your applications. You'd like to take your SQL skills to the next level.Let's face it, SQL is a deceptively simple language to learn, and many database developers never go far beyond the simple statement: SELECT columns FROM table WHERE conditions. But there is so much more you can do with the language. In the SQL Cookbook, experienced SQL developer Anthony Molinaro shares his favorite SQL techniques and features. You'll learn about:Window functions, arguably the most significant enhancement to SQL in the past decade. If you're not using these, you're missing outPowerful, database-specific features such as SQL Server's PIVOT and UNPIVOT operators, Oracle's MODEL clause, and PostgreSQL's very useful GENERATE_SERIES functionPivoting rows into columns, reverse-pivoting columns into rows, using pivoting to facilitate inter-row calculations, and double-pivoting a result setBucketization, and why you should never use that term in Brooklyn.How to create histograms, summarize data into buckets, perform aggregations over a moving range of values, generate running-totals and subtotals, and other advanced, data warehousing techniquesThe technique of walking a string, which allows you to use SQL to parse through the characters, words, or delimited elements of a stringWritten in O'Reilly's popular Problem/Solution/Discussion style, the SQL Cookbook is sure to please. Anthony's credo is: When it comes down to it, we all go to work, we all have bills to pay, and we all want to go home at a reasonable time and enjoy what's still available of our days. The SQL Cookbook moves quickly from problem to solution, saving you time each step of the way.

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    This book is a tutorial and exposition of the principles of Web site design. It aids users in building web sites that stand out from the noise of the web and bring them to the content in an effective and efficient way. This four-color book gives substantial critiques of existing Web site designs.

    Healso discusses the social aspects of software and how free softwarecan create community and social justice.Given the current turmoil in copyright and patent laws, includingthe DMCA and proposed CBDTPA, these essays are more relevant thanever. Stallman tackles head-on the essential issues driving thecurrent changes in copyright law. He argues that for creativity toflourish, software must be free of inappropriate and overly-broadlegal constraints. Over the past twenty years his arguments andactions have changed the course of software history; this new book issure to impact the future of software and legal policies in the yearsto come.Lawrence Lessig, the author of two well-known books on similar topics,writes the introduction. He is a noted legal expert on copyright lawand a Stanford Law School professor.