In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems.From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it.Among the many topics covered, you’ll discover how to:Test drive your data to see if it’s ready for analysisWork spreadsheet data into a usable formHandle encoding problems that lurk in text dataDevelop a successful web-scraping effortUse NLP tools to reveal the real sentiment of online reviewsAddress cloud computing issues that can impact your analysis effortAvoid policies that create data analysis roadblocksTake a systematic approach to data quality analysis

    By presenting the emerging best practices and designs from the Scala community, it guides you through dozens of powerful techniques example by example.About the BookScala is a powerful JVM language that blends the functional and OO programming models. You'll have no trouble getting introductions to Scala in books or online, but it's hard to find great examples and insights from experienced practitioners. You'll find them in Scala in Depth.There's little heavy-handed theory here—just dozens of crisp, practical techniques for coding in Scala. Written for readers who know Java, Scala, or another OO language.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.What's InsideConcise, expressive, and readable code style How to integrate Scala into your existing Java projects Scala's 2.8.0 collections API How to use actors for concurrent programming Mastering the Scala type system Scala's OO features—type member inheritance, multiple inheritance, and composition Functional concepts and patterns—immutability, applicative functors, and monads========================================​==========Table of ContentsScala—a blended language The core rules Modicum of style—coding conventions Utilizing object orientation Using implicits to write expressive code The type system Using implicits and types together Using the right collection Actors Integrating Scala with Java Patterns in functional programming

    They aren't conscious of all the choices they make, like how they format their source, the names they use for variables, or the kinds of loops they use. They're focused entirely on problems they're solving, solutions they're creating, and algorithms they're implementing. So they write code in the way that seems natural, that happens intuitively, and that feels good.But if you're serious about your profession, intuition isn't enough. Perl Best Practices author Damian Conway explains that rules, conventions, standards, and practices not only help programmers communicate and coordinate with one another, they also provide a reliable framework for thinking about problems, and a common language for expressing solutions. This is especially critical in Perl, because the language is designed to offer many ways to accomplish the same task, and consequently it supports many incompatible dialects.With a good dose of Aussie humor, Dr. Conway (familiar to many in the Perl community) offers 256 guidelines on the art of coding to help you write better Perl code--in fact, the best Perl code you possibly can. The guidelines cover code layout, naming conventions, choice of data and control structures, program decomposition, interface design and implementation, modularity, object orientation, error handling, testing, and debugging.They're designed to work together to produce code that is clear, robust, efficient, maintainable, and concise, but Dr. Conway doesn't pretend that this is the one true universal and unequivocal set of best practices. Instead, Perl Best Practices offers coherent and widely applicable suggestions based on real-world experience of how code is actually written, rather than on someone's ivory-tower theories on how software ought to be created.Most of all, Perl Best Practices offers guidelines that actually work, and that many developers around the world are already using. Much like Perl itself, these guidelines are about helping you to get your job done, without getting in the way.Praise for Perl Best Practices from Perl community members:"As a manager of a large Perl project, I'd ensure that every member of my team has a copy of Perl Best Practices on their desk, and use it as the basis for an in-house style guide." -- Randal Schwartz"There are no more excuses for writing bad Perl programs. All levels of Perl programmer will be more productive after reading this book." -- Peter Scott"Perl Best Practices will be the next big important book in the evolution of Perl. The ideas and practices Damian lays down will help bring Perl out from under the embarrassing heading of "scripting languages". Many of us have known Perl is a real programming language, worthy of all the tasks normally delegated to Java and C++. With Perl Best Practices, Damian shows specifically how and why, so everyone else can see, too." -- Andy Lester"Damian's done what many thought impossible: show how to build large, maintainable Perl applications, while still letting Perl be the powerful, expressive language that programmers have loved for years." -- Bill Odom"Finally, a means to bring lasting order to the process and product of real Perl development teams." -- Andrew Sundstrom"Perl Best Practices provides a valuable education in how to write robust, maintainable P

    You've no doubt read your fair share of media hype either proclaiming doom and gloom where robots seize our jobs or prophesying a new utopia where AI cures all our human problems. But what does it actually mean for your role as a business leader? Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities. If you want to drive innovation by combining data, technology, design, and people to solve real problems at an enterprise scale, this is your playbook. This book does not overload you with details on debugging TensorFlow code nor bore you with generalizations about the future of humanity. Instead, we teach you how to lead successful AI initiatives by prioritizing the right opportunities, building a diverse team of experts, conducting strategic experiments, and consciously designing your solutions to benefit both your organization and society as a whole. This book is focused on helping you drive concrete business decisions through applications of artificial intelligence and machine learning. Written with the combined knowledge of three experts in the field, Applied Artificial Intelligence is the best practical guide for business leaders looking to get true value from the adoption of machine learning technology. If you have questions such as... *What is artificial intelligence (AI)? *How do I distinguish true value from AI hype? *What are the best business use cases for AI established so far? *How do I identify the best business case for AI adoption and evaluate opportunities? *Should I build or buy an AI platform? *How do I find and recruit top AI talent for my enterprise? *How will incorporating AI into my business increase revenue or decrease costs? *How can I facilitate AI adoption within my company? ... then this handbook provides you with answers. Who is this book for? * Managers and business professionals * Marketers, product managers and business strategists * Entrepreneurs, founders and startups team members * Consultants, advisors and educators * Engineers and data scientists who want to work with business units And everyone else who is interested in using artificial intelligence and machine learning to improve business processes.

    If you're a reasonably proficient programmer who can think logically, you have all the background you'll need. Stepanov and Rose introduce the relevant abstract algebra and number theory with exceptional clarity. They carefully explain the problems mathematicians first needed to solve, and then show how these mathematical solutions translate to generic programming and the creation of more effective and elegant code. To demonstrate the crucial role these mathematical principles play in many modern applications, the authors show how to use these results and generalized algorithms to implement a real-world public-key cryptosystem. As you read this book, you'll master the thought processes necessary for effective programming and learn how to generalize narrowly conceived algorithms to widen their usefulness without losing efficiency. You'll also gain deep insight into the value of mathematics to programming--insight that will prove invaluable no matter what programming languages and paradigms you use. You will learn aboutHow to generalize a four thousand-year-old algorithm, demonstrating indispensable lessons about clarity and efficiencyAncient paradoxes, beautiful theorems, and the productive tension between continuous and discreteA simple algorithm for finding greatest common divisor (GCD) and modern abstractions that build on itPowerful mathematical approaches to abstractionHow abstract algebra provides the idea at the heart of generic programmingAxioms, proofs, theories, and models: using mathematical techniques to organize knowledge about your algorithms and data structuresSurprising subtleties of simple programming tasks and what you can learn from themHow practical implementations can exploit theoretical knowledge

    It is the first C++ book that actually demonstrates how to design large systems, and one of the few books on object-oriented design specifically geared to practical aspects of the C++ programming language. In this book, Lakos explains the process of decomposing large systems into physical (not inheritance) hierarchies of smaller, more manageable components. Such systems with their acyclic physical dependencies are fundamentally easier and more economical to maintain, test, and reuse than tightly interdependent systems. In addition to explaining the motivation for following good physical as well as logical design practices, Lakos provides you with a catalog of specific techniques designed to eliminate cyclic, compile-time, and link-time (physical) dependencies. He then extends these concepts from large to very large systems. The book concludes with a comprehensive top-down approach to the logical design of individual components. Appendices include a valuable design pattern Protocol Hierarchy designed to avoid fat inte

    Aiming to tell the dark secrets of computer arithmetic, this title is suitable for library developers, compiler writers, and lovers of elegant hacks.

    Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work.Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

    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

    Today, the utopia they sought to create is looking more dystopian than ever: from digital surveillance and the loss of privacy to the spreading of misinformation and hate speech to predatory algorithms targeting the weak and vulnerable to products that have been engineered to manipulate our desires. How did we get here? How did these once-scrappy and idealistic enterprises become rapacious monopolies with the power to corrupt our elections, co-opt all our data, and control the largest single chunk of corporate wealth—while evading all semblance of regulation and taxes?  In Don’t Be Evil, Financial Times global business columnist Rana Foroohar tells the story of how Big Tech lost its soul—and ate our lunch. Through her skilled reporting and unparalleled access—won through nearly thirty years covering business and technology—she shows the true extent to which behemoths like Google, Facebook, Apple, and Amazon are monetizing both our data and our attention, without us seeing a penny of those exorbitant profits. Finally, Foroohar lays out a plan for how we can resist, by creating a framework that fosters innovation while also protecting us from the dark side of digital technology.Praise for Don’t Be Evil “At first sight, Don’t Be Evil looks like it’s doing for Google what muckraking journalist Ida Tarbell did for Standard Oil over a century ago. But this whip-smart, highly readable book’s scope turns out to be much broader. Worried about the monopolistic tendencies of big tech? The addictive apps on your iPhone? The role Facebook played in Donald Trump’s election? Foroohar will leave you even more worried, but a lot better informed.”—Niall Ferguson, Milbank Family Senior Fellow at the Hoover Institution, Stanford, and author of The Square and the Tower

    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

    It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected. In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge. The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans. The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What hap­pens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others?  Who knows—maybe there’s a bot learning to do your job this minute.

    This urgent report is required reading, from defense expert P.W. Singer and Council on Foreign Relations fellow Emerson Brooking.

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information