This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

    In Dataclysm, Christian Rudder uses it to show us who we truly are.   For centuries, we’ve relied on polling or small-scale lab experiments to study human behavior. Today, a new approach is possible. As we live more of our lives online, researchers can finally observe us directly, in vast numbers, and without filters. Data scientists have become the new demographers.   In this daring and original book, Rudder explains how Facebook "likes" can predict, with surprising accuracy, a person’s sexual orientation and even intelligence; how attractive women receive exponentially more interview requests; and why you must have haters to be hot. He charts the rise and fall of America’s most reviled word through Google Search and examines the new dynamics of collaborative rage on Twitter. He shows how people express themselves, both privately and publicly. What is the least Asian thing you can say? Do people bathe more in Vermont or New Jersey? What do black women think about Simon & Garfunkel? (Hint: they don’t think about Simon & Garfunkel.) Rudder also traces human migration over time, showing how groups of people move from certain small towns to the same big cities across the globe. And he grapples with the challenge of maintaining privacy in a world where these explorations are possible.   Visually arresting and full of wit and insight, Dataclysm is a new way of seeing ourselves—a brilliant alchemy, in which math is made human and numbers become the narrative of our time.

    It should be labelled 'start here'." --Pieter Jan Stappers, ID-StudioLab, Delft University of Technology

    If you're working with a relational database--whether you're writing applications, performing administrative tasks, or generating reports--you need to know how to interact with your data. Even if you are using a tool that generates SQL for you, such as a reporting tool, there may still be cases where you need to bypass the automatic generation feature and write your own SQL statements.To help you attain this fundamental SQL knowledge, look to "Learning SQL," an introductory guide to SQL, designed primarily for developers just cutting their teeth on the language."Learning SQL" moves you quickly through the basics and then on to some of the more commonly used advanced features. Among the topics discussed: The history of the computerized databaseSQL Data Statements--those used to create, manipulate, and retrieve data stored in your database; example statements include select, update, insert, and deleteSQL Schema Statements--those used to create database objects, such as tables, indexes, and constraintsHow data sets can interact with queriesThe importance of subqueriesData conversion and manipulation via SQL's built-in functionsHow conditional logic can be used in Data StatementsBest of all, "Learning SQL" talks to you in a real-world manner, discussing various platform differences that you're likely to encounter and offering a series of chapter exercises that walk you through the learning process. Whenever possible, the book sticks to the features included in the ANSI SQL standards. This means you'll be able to apply what you learn to any of several different databases; the book covers MySQL, Microsoft SQL Server, and Oracle Database, but the features and syntax should apply just as well (perhaps with some tweaking) to IBM DB2, Sybase Adaptive Server, and PostgreSQL.Put the power and flexibility of SQL to work. With "Learning SQL" you can master this important skill and know that the SQL statements you write are indeed correct.

    Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.Learn how to use a problem's "weight" against itself to:Break down seemingly complex data problems into simplified partsUse alternative data analysis techniques to examine themUse human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problemsLearn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

    These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of Web intelligence, as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected.Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

    Whittaker defines both in-the-small techniques for individual testers and in-the-large techniques to supercharge test teams. He also introduces a hybrid strategy for injecting exploratory concepts into traditional scripted testing. You'll learn when to use each, and how to use them all successfully.Concise, entertaining, and actionable, this book introduces robust techniques that have been used extensively by real testers on shipping software, illuminating their actual experiences with these techniques, and the results they've achieved. Writing for testers, QA specialists, developers, program managers, and architects alike, Whittaker answers crucial questions such as:- Why do some bugs remain invisible to automated testing--and how can I uncover them?- What techniques will help me consistently discover and eliminate "show stopper" bugs?- How do I make manual testing more effective--and less boring and unpleasant?- What's the most effective high-level test strategy for each project?- Which inputs should I test when I can't test them all?- Which test cases will provide the best feature coverage?- How can I get better results by combining exploratory testing with traditional script or scenario-based testing?- How do I reflect feedback from the development process, such as code changes?

    This book teaches you how to conduct examinations by explaining what digital forensics is, the methodologies used, key technical concepts and the tools needed to perform examinations. Details on digital forensics for computers, networks, cell phones, GPS, the cloud, and Internet are discussed. Readers will also learn how to collect evidence, document the scene, and recover deleted data. This is the only resource your students need to get a jump-start into digital forensics investigations.This book is organized into 11 chapters. After an introduction to the basics of digital forensics, the book proceeds with a discussion of key technical concepts. Succeeding chapters cover labs and tools; collecting evidence; Windows system artifacts; anti-forensics; Internet and email; network forensics; and mobile device forensics. The book concludes by outlining challenges and concerns associated with digital forensics. PowerPoint lecture slides are also available.This book will be a valuable resource for entry-level digital forensics professionals as well as those in complimentary fields including law enforcement, legal, and general information security.

    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.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

    Written by the lead technical writer at one of Silicon Valley's most exciting companies, Modern Technical Writing is a set of guiding principles and thoughtful recommendations for new and experienced technical writers alike. Not a reference manual, and not comprehensive, it instead serves as an introduction to a sensible writing and publishing process, one that has eluded the profession for too long.

    Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

    Designed as a handy resource when you need quick solutions or techniques, the book offers dozens of short, focused pieces of code and hundreds of worked-out examples for programmers of all levels who don't have the time (or expertise) to solve MySQL problems from scratch.The new edition covers MySQL 5.0 and its powerful new features, as well as the older but still widespread MySQL 4.1. One major emphasis of this book is how to use SQL to formulate queries for particular kinds of questions, using the mysql client program included in MySQL distributions. The other major emphasis is how to write programs that interact with the MySQL server through an API. You'll find plenty of examples using several language APIs in multiple scenarios and situations, including the use of Ruby to retrieve and format data. There are also many new examples for using Perl, PHP, Python, and Java as well.Other recipes in the book teach you to:Access data from multiple tables at the same time Use SQL to select, sort, and summarize rows Find matches or mismatches between rows in two tables Determine intervals between dates or times, including age calculations Store images into MySQL and retrieve them for display in web pages Get LOAD DATA to read your data files properly or find which values in the file are invalid Use strict mode to prevent entry of bad data into your database Copy a table or a database to another server Generate sequence numbers to use as unique row identifiers Create database events that execute according to a schedule And a lot moreMySQL Cookbook doesn't attempt to develop full-fledged, complex applications. Instead, it's intended to assist you in developing applications yourself by helping you get past problems that have you stumped.

    PHP is a server-side, HTML-embedded scripting language. It is an open source technology, rapidly gaining popularity as a scripting language for people running dynamic websites. One of its major attractions over Perl, JavaScript and other scripting languages is that PHP has a built-in database integration layer and seamless IP connectivity, with LDAP and TCP as well as the IMAP mail interface. Features; Real world, practical experience and techniques From installation and configuration of the PHP engine to advanced dynamic application design Definitive coverage of core PHP language and database addressing: MySQL is covered in depth. Practical e-commerce and business scripting including database application development, together with PHP and XML applications. LDAP connectivity addressed.