This work covers the project lifecycle, addressing every key issue participants face: politics, people, process, project management, and tools.

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

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    When the supply train doesn't arrive, Almanzo Wilder and his brother realize something must be done. They begin an impossible journey in search of provisions, before it's too late.

    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

    However, successful applications have a habit of growing. Eventually the development team ends up in what is known as monolithic hell. All aspects of software development and deployment become painfully slow. The solution is to adopt the microservice architecture, which structures an application as a services, organized around business capabilities. This architecture accelerates software development and enables continuous delivery and deployment of complex software applications.Microservice Patterns teaches enterprise developers and architects how to build applications with the microservice architecture. Rather than simply advocating for the use the microservice architecture, this clearly-written guide takes a balanced, pragmatic approach. You'll discover that the microservice architecture is not a silver bullet and has both benefits and drawbacks. Along the way, you'll learn a pattern language that will enable you to solve the issues that arise when using the microservice architecture. This book also teaches you how to refactor a monolithic application to a microservice architecture.

    His revelations demystify the strategy behind the company's recent flurry of bold moves, all driven by the pursuit of a business plan unlike any other: to become the indispensable gatekeeper of all the world's information, the one-stop destination for all our information needs. Will Google succeed? And what are the implications of a single company commanding so much information and knowing so much about us? As ambitious as Google's goal is, with 68 percent of all Web searches (and growing), profits that are the envy of the business world, and a surplus of talent, the company is, Stross shows, well along the way to fulfilling its ambition, becoming as dominant a force on the Web as Microsoft became on the PC. Google isn't just a superior search service anymore. In recent years it has launched a dizzying array of new services and advanced into whole new businesses, from the introductions of its controversial Book Search and the irresistible Google Earth, to bidding for a slice of the wireless-phone spectrum and nonchalantly purchasing YouTube for $1.65 billion. Google has also taken direct aim at Microsoft's core business, offering free e-mail and software from word processing to spreadsheets and calendars, pushing a transformative -- and highly disruptive -- concept known as "cloud computing." According to this plan, users will increasingly store all of their data on Google's massive servers -- a network of a million computers that amounts to the world's largest supercomputer, with unlimited capacity to house all the information Google seeks. The more offerings Google adds, and the more ubiquitous a presence it becomes, the more dependent its users become on its services and the more information they contribute to its uni

    Written by Chris Sims, a top scrum trainer and pioneer of experiential learning, and Hillary Louise Johnson, a novelist and business journalist, it demonstrates the principles, practices and pitfalls of the scrum framework through lively storytelling and vividly told example.The Elements of Scrum opens with a blow-by-blow description of a week in the life of a scrum team, then briefly details the history and origins of scrum, comparing it to traditional methodologies and providing context for how scrum applies to the cultural history of the software industry. Next, the principles and practices set forth in the Agile Manifesto are broken down and illustrated with real-world examples, putting the reader inside the heads of the founders of scrum and agile for a thorough grounding in theory.The meat of the book explains every aspect of the scrum process, including team composition, scheduling and work flow management, in crisp, clear, example-laden prose designed to provide insight to novices and experienced practitioners alike.The book concludes with a section on supporting technical practices like Test Driven Development and Pair Programming, to help the reader apply scrum at the practical level.The Elements of Scrum is taught at colleges and universities across the country, including UCLA, George Mason University, Arizona State, SUNY Potsdam, Wofford College, and Becker College. It has been translated into Mandarin, and is soon to appear in other international editions.

    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.