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

    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.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

    Significant numbers of poorly designed programs have been created by less-experienced developers, resulting in applications that are inefficient and hard to maintain and extend. Increasingly, software system professionals are discovering just how difficult it is to work with these inherited, non-optimal applications. For several years, expert-level object programmers have employed a growing collection of techniques to improve the structural integrity and performance of such existing software programs. Referred to as refactoring, these practices have remained in the domain of experts because no attempt has been made to transcribe the lore into a form that all developers could use... until now. In Refactoring: Improving the Design of Existing Software, renowned object technology mentor Martin Fowler breaks new ground, demystifying these master practices and demonstrating how software practitioners can realize the significant benefits of this new process.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    The book offers sound, practical advice, no matter what your age or net worth. Bottomline, become a Boglehead and prosper! Originally just the chat-line ruminations of Boglehead founder Taylor Larimore, and Morningstar forum leading cohorts Mel Lindauer and Michael LeBoeuf, their trusted advice has been brewed and distilled into an easy-to-use, need-to-know, no frills guide to building up your own financial well-being - so you can worry less and profit more from the investments you make. Invest like a Boglehead, and let their grassroots investment wisdom guide you down the path of long-term wealth creation and happiness, without all the worries and fuss of stock pickers and day traders. If you face a financial crisis or problem, or simply want to know what is prudent to do with the money you save, the Bogleheads will have the answers you need to help you gain your financial footing and keep it.

    Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills you’ll need alongside your technical know-how in order to succeed in the field. Following their clear and simple instructions you’ll craft a resume that hiring managers will love, learn how to ace your interview, and ensure you hit the ground running in your first months at your new job. Once you’ve gotten your foot in the door, learn to thrive as a data scientist by handling high expectations, dealing with stakeholders, and managing failures. Finally, you’ll look towards the future and learn about how to join the broader data science community, leaving a job gracefully, and plotting your career path. With this book by your side you’ll have everything you need to ensure a rewarding and productive role in data science.

    You'll never find a wiser advisor with a bigger heart.” -- Malachi Rempen: Filmmaker, cartoonist, author and self-described ruffian This book grew out of a series of letters to my daughter concerning various things—mostly about money and investing—she was not yet quite ready to hear. Since money is the single most powerful tool we have for navigating this complex world we’ve created, understanding it is critical. “But Dad,” she once said, “I know money is important. I just don’t want to spend my life thinking about it.” This was eye-opening. I love this stuff. But most people have better things to do with their precious time. Bridges to build, diseases to cure, treaties to negotiate, mountains to climb, technologies to create, children to teach, businesses to run. Unfortunately, benign neglect of things financial leaves you open to the charlatans of the financial world. The people who make investing endlessly complex, because if it can be made complex it becomes more profitable for them, more expensive for us, and we are forced into their waiting arms. Here’s an important truth: Complex investments exist only to profit those who create and sell them. Not only are they more costly to the investor, they are less effective. The simple approach I created for her and present now to you, is not only easy to understand and implement, it is more powerful than any other. Together we’ll explore: Debt: Why you must avoid it and what to do if you have it. The importance of having F-you Money. How to think about money, and the unique way understanding this is key to building your wealth. Where traditional investing advice goes wrong and what actually works. What the stock market really is and how it really works. Why the stock market always goes up and why most people still lose money investing in it. How to invest in a raging bull, or bear, market. Specific investments to implement these strategies. The Wealth Building and Wealth Preservation phases of your investing life and why they are not always tied to your age. How your asset allocation is tied to those phases and how to choose it. How to simplify the sometimes confusing world of 401(k), 403(b), TSP, IRA and Roth accounts. TRFs (Target Retirement Funds), HSAs (Health Savings Accounts) and RMDs (Required Minimum Distributions). What investment firm to use and why the one I recommend is so far superior to the competition. Why you should be very cautious when engaging an investment advisor and whether you need to at all. Why and how you can be conned, and how to avoid becoming prey. Why I don’t recommend dollar cost averaging. What financial independence looks like and how to have your money support you. What the 4% rule is and how to use it to safely spend your wealth. The truth behind Social Security.

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

    Your online and in-store purchasing patterns are recorded, and reveal if you're unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you’re thinking because it saves your private searches. Facebook can determine your sexual orientation without you ever mentioning it.The powers that surveil us do more than simply store this information. Corporations use surveillance to manipulate not only the news articles and advertisements we each see, but also the prices we’re offered. Governments use surveillance to discriminate, censor, chill free speech, and put people in danger worldwide. And both sides share this information with each other or, even worse, lose it to cybercriminals in huge data breaches.Much of this is voluntary: we cooperate with corporate surveillance because it promises us convenience, and we submit to government surveillance because it promises us protection. The result is a mass surveillance society of our own making. But have we given up more than we’ve gained? In Data and Goliath, security expert Bruce Schneier offers another path, one that values both security and privacy. He brings his bestseller up-to-date with a new preface covering the latest developments, and then shows us exactly what we can do to reform government surveillance programs, shake up surveillance-based business models, and protect our individual privacy. You'll never look at your phone, your computer, your credit cards, or even your car in the same way again.

    A simple web search picks out a handful of relevant needles from the world's biggest haystack: the billions of pages on the World Wide Web. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers; and we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease? This is the first book to answer that question in language anyone can understand, revealing the extraordinary ideas that power our PCs, laptops, and smartphones. Using vivid examples, John MacCormick explains the fundamental "tricks" behind nine types of computer algorithms, including artificial intelligence (where we learn about the "nearest neighbor trick" and "twenty questions trick"), Google's famous PageRank algorithm (which uses the "random surfer trick"), data compression, error correction, and much more. These revolutionary algorithms have changed our world: this book unlocks their secrets, and lays bare the incredible ideas that our computers use every day.

    In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.This deeply researched and passionate book could not be more timely.Naomi Klein: "This book is downright scary."Ethan Zuckerman, MIT: "Should be required reading."Dorothy Roberts, author of Killing the Black Body: "A must-read for everyone concerned about modern tools of inequality in America."Astra Taylor, author of The People's Platform: "This is the single most important book about technology you will read this year."

    Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required. 0201157675B07092001

    Berkin and Larry E. Swedroe, co-authors of The Incredible Shrinking Alpha, bring you a thorough yet still jargon-free and accessible guide to applying one of today's most valuable quantitative, evidence-based approaches to outperforming the market: factor investing. Designed for savvy investors and professional advisors alike, Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today takes you on a journey through the land of academic research and an extensive review of its 50-year quest to uncover the secret of successful investing.Along the way, Berkin and Swedroe cite and distill more than 100 academic papers on finance and introduce five unique criteria that a factor (at its most basic, a characteristic or set of characteristics common among a broad set of securities) must meet to be considered worthy of your investment. In addition to providing explanatory power to portfolio returns and delivering a premium, Swedroe and Berkin argue a factor should be persistent, pervasive, robust, investable and intuitive.By the end, you'll have learned that, within the entire "factor zoo," only certain exhibits are worth visiting and only a handful of factors are required to invest in the same manner that made Warren Buffett a legend.Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today offers an in-depth look at the evidence practitioners use to build portfolios and how you as an investor can benefit from that knowledge, rendering it an essential resource for making the informed and prudent investment decisions necessary to help secure your financial future.