Now, Google engineers who worked on that bestseller introduce The Site Reliability Workbook, a hands-on companion that uses concrete examples to show you how to put SRE principles and practices to work in your environment.This new workbook not only combines practical examples from Google's experiences, but also provides case studies from Google's Cloud Platform customers who underwent this journey. Evernote, The Home Depot, The New York Times, and other companies outline hard-won experiences of what worked for them and what didn't.Dive into this workbook and learn how to flesh out your own SRE practice, no matter what size your company is.You'll learn:How to run reliable services in environments you don't completely control--like cloudPractical applications of how to create, monitor, and run your services via Service Level ObjectivesHow to convert existing ops teams to SRE--including how to dig out of operational overloadMethods for starting SRE from either greenfield or brownfield

    

    The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. 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 InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics

    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

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    As a professional C++ developer and former Harvard teaching fellow, I know what you need to know to be a great C++ programmer, and I know how to teach it, one step at a time. I know where people struggle, and why, and how to make it clear. I cover every step of the programming process, including:Getting the tools you need to program and how to use them*Basic language feature like variables, loops and functions*How to go from an idea to code*A clear, understandable explanation of pointers*Strings, file IO, arrays, references*Classes and advanced class design*C++-specific programming patterns*Object oriented programming*Data structures and the standard template library (STL)Key concepts are reinforced with quizzes and over 75 practice problems.

    Alexander Klimburg was a member of the generation for whom it was a utopian ideal turned reality: a place where ideas, information, and knowledge could be shared and new freedoms found and enjoyed. Two decades later, the future isn't so bright any more: increasingly, the Internet is used as a weapon and a means of domination by states eager to exploit or curtail global connectivity in order to further their national interests. Klimburg is a leading voice in the conversation on the implications of this dangerous shift, and in The Darkening Web, he explains why we underestimate the consequences of states' ambitions to project power in cyberspace at our peril: Not only have hacking and cyber operations fundamentally changed the nature of political conflict--ensnaring states in a struggle to maintain a precarious peace that could rapidly collapse into all-out war--but the rise of covert influencing and information warfare has enabled these same global powers to create and disseminate their own distorted versions of reality in which anything is possible. At stake are not only our personal data or the electrical grid, but the Internet as we know it today--and with it the very existence of open and democratic societies. Blending anecdote with argument, Klimburg brings us face-to-face with the range of threats the struggle for cyberspace presents, from an apocalyptic scenario of debilitated civilian infrastructure to a 1984-like erosion of privacy and freedom of expression. Focusing on different approaches to cyber-conflict in the US, Russia and China, he reveals the extent to which the battle for control of the Internet is as complex and perilous as the one surrounding nuclear weapons during the Cold War--and quite possibly as dangerous for humanity as a whole. Authoritative, thought-provoking, and compellingly argued, The Darkening Web makes clear that the debate about the different aspirations for cyberspace is nothing short of a war over our global values.

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than ever before. There is no doubt that we're performing at higher scales and going faster than ever, but what if we're headed in the wrong direction?The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of platforms can in fact lead to wasted efforts, missed opportunities, and above all an inability to break out of established patterns. Edward Tenner offers a smarter way to think about efficiency, showing how we can combine artificial intelligence and our own intuition, leaving ourselves and our institutions open to learning from the random and unexpected.

    But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

    R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.Understand the basics of the language, including the nature of R objectsLearn how to write R functions and build your own packagesWork with data through visualization, statistical analysis, and other methodsExplore the wealth of packages contributed by the R communityBecome familiar with the lattice graphics package for high-level data visualizationLearn about bioinformatics packages provided by Bioconductor"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians."

    It comprehensively presents the material necessary for understanding the internal architecture as well as system design aspects of Intel’s legendary 8085 and 8086 microprocessors and Intel’s 8051 and 8096 microcontrollers.The book throughout maintains an appropriate balance between the basic concepts and the skill sets needed for system design. Besides, the book lucidly explains the hardware architecture, the instruction set and programming, support chips, peripheral interfacing, and cites several relevant examples to help the readers develop a complete understanding of industrial application projects. Several system design case studies are included to reinforce the concepts discussed.With exhaustive coverage and practical approach, the book would be indispensable to undergraduate students of Electrical and Electronics, Electronics and Communication, and Electronics and Instrumentation Engineering. It can be used for a variety of courses in Microprocessors, Microcontrollers, and Embedded System Design.The second edition of the book introduces additional topics like I/O interfacing and programming, serial interface programming, delay programming using 8086 and 8051. Besides, many more examples and case studies have been added.Contents:Preface • Preface to the First EditionAcknowledgements1. System Design Using Microprocessor2. What a Microprocessor Is3. Intel 8085 Microprocessor—Hardware Architecture4. Intel 8085 Microprocessor—Instruction Set and Programming5. Intel 8086—Hardware Architecture6. Intel 8086 Microprocessor—Instruction Set and Programming7. Microprocessor—Peripheral Interfacing8. System Design Using Intel 8085 and Intel 8086 Microprocessors—Case Studies9. Intel 8051 Microcontroller—Hardware Architecture10. Intel 8051 Microcontroller—Instruction Set and Programming11. The 8051 Microcontroller-Based System Design—Case Studies12. Intel 8096 Microcontroller—Hardware Architecture13. Intel 8096 Microcontroller—Instruction Set and Programming14. The 8096 Microcontroller-Based System Design—Case StudiesAppendices • Index

    “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com

    Now, for the first time, leading agile methodologist Scott Ambler and renowned consultantPramodkumar Sadalage introduce powerful refactoring techniquesspecifically designed for database systems. Ambler and Sadalagedemonstrate how small changes to table structures, data, storedprocedures, and triggers can significantly enhance virtually anydatabase design - without changing semantic

    But, if you type in "white girls," the results are radically different. The suggested porn sites and un-moderated discussions about "why black women are so sassy" or "why black women are so angry" presents a disturbing portrait of black womanhood in modern society.In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color.Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance - operating as a source for email, a major vehicle for primary and secondary school learning, and beyond - understanding and reversing these disquieting trends and discriminatory practices is of utmost importance.An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century.