It is also the most powerful, and in some ways, the most confusing. What do accessible, content-focused standards-based web designers and front-end developers need to know? And how can we harness the power of HTML5 in today’s browsers?In this brilliant and entertaining user’s guide, Jeremy Keith cuts to the chase, with crisp, clear, practical examples, and his patented twinkle and charm.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    Hardcover published by Quadragle/The New York Times Book Co., third printing, August 1977, copyright 1975.

    The authors present updated coverage of compilers based on research and techniques that have been developed in the field over the past few years. The book provides a thorough introduction to compiler design and covers topics such as context-free grammars, fine state machines, and syntax-directed translation.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

    However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.

    So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems?In this collection of essays and articles, key members of Google's Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You'll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient--lessons directly applicable to your organization.This book is divided into four sections: Introduction--Learn what site reliability engineering is and why it differs from conventional IT industry practicesPrinciples--Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE)Practices--Understand the theory and practice of an SRE's day-to-day work: building and operating large distributed computing systemsManagement--Explore Google's best practices for training, communication, and meetings that your organization can use

    Some can generate coherent user experiences, others produce confusing patchwork designs. Some inspire teams to contribute to them, while others are neglected. Some get better with time, more cohesive and better functioning; others get worse, becoming bloated and cumbersome.Throughout this book, Alla Kholmatova, previously a lead designer at FutureLearn, will share an approach and the key qualities of effective, enduring design systems. It’s based on Alla’s experiences, case-studies from AirBnB, Atlassian, Eurostar, TED, and Sipgate, plus 18 months of research and interviews — all attempting to figure out what works and what doesn’t work in real-life products. It may not answer every question, but it will help you figure out just the right strategy for establishing and evolving a design system in your organization.

    Unfortunately, reliable guidance on Agile management has been scarce indeed. Now, leading Agile manager Jurgen Appelo fills that gap, introducing a realistic approach to leading, managing, and growing your Agile team or organization. Writing for current managers and developers moving into management, Appelo shares insights that are grounded in modern complex systems theory, reflecting the intense complexity of modern software development. Appelo's Management 3.0 model recognizes that today's organizations are living, networked systems; and that management is primarily about people and relationships. Management 3.0 doesn't offer mere checklists or prescriptions to follow slavishly; rather, it deepens your understanding of how organizations and Agile teams work and gives you tools to solve your own problems. Drawing on his extensive experience as an Agile manager, the author identifies the most important practices of Agile management and helps you improve each of them. Coverage includes - Getting beyond "Management 1.0" control and "Management 2.0" fads - Understanding how complexity affects your organization - Keeping your people active, creative, innovative, and motivated - Giving teams the care and authority they need to grow on their own - Defining boundaries so teams can succeed in alignment with business goals - Sowing the seeds for a culture of software craftsmanship - Crafting an organizational network that promotes success - Implementing continuous improvement that actually works Thoroughly pragmatic-and never trendy-Jurgen Appelo's Management 3.0 helps you bring greater agility to any software organization, team, or project.

    Offering indispensle lessons, practical advice, facts, anecdotes and inspiration, this book is a timeless creative bible for all those looking to succeed in life, business and creativity. These are key lessons derived from the incomparle life of 'Master Communicator' George Lois, the original Mad Man of Madison Avenue. Written and compiled by the man The Wall Street Journal called "prodigy, enfant terrible, founder of agencies, creator of legends," each step is borne from a passion to succeed and a disdain for the status quo.Organised into inspirational, bite-sized pointers, each page offers fresh insight into the sources of success, from identifying your heroes to identifying yourself. The ideas, images and illustrations presented in this book are fresh, witty and in-your-face. Whether it's communicating your point in nanosecond, creating an explosive portfolio or making your presence felt, no one is better placed than George Lois to teach you the process of creativity.Poignant, punchy and to-the-point, Damn Good Advice (For People With Talent!) is a must have for anyone on a quest for success.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    Who are these people, what motivates them, and why should you care?Consider these facts: Everything around us is turning into computers. Your typewriter is gone, replaced by a computer. Your phone has turned into a computer. So has your camera. Soon your TV will. Your car was not only designed on computers, but has more processing power in it than a room-sized mainframe did in 1970. Letters, encyclopedias, newspapers, and even your local store are being replaced by the Internet.Hackers & Painters: Big Ideas from the Computer Age, by Paul Graham, explains this world and the motivations of the people who occupy it. In clear, thoughtful prose that draws on illuminating historical examples, Graham takes readers on an unflinching exploration into what he calls “an intellectual Wild West.”The ideas discussed in this book will have a powerful and lasting impact on how we think, how we work, how we develop technology, and how we live. Topics include the importance of beauty in software design, how to make wealth, heresy and free speech, the programming language renaissance, the open-source movement, digital design, internet startups, and more.

    To create a successful web product that's as large as Etsy, Facebook, Twitter, or Pinterest-or even as small as a tiny app-you need to know more than just HTML and CSS. You need to understand how to create meaningful online experiences so that users want to come back again and again.In other words, you have to stop thinking like a web designer or a visual designer or a UX designer or an interaction designer and start thinking like a product designer.In this breakthrough introduction to modern product design, Etsy Creative Director Randy Hunt explains the skills, processes, types of tools, and recommended workflows for creating world-class web products. After reading this book, you'll have a complete understanding of what product design really is and you'll be equipped with the best practices necessary for building your own successful online products.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    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.