The Scrum Guide documents the Scrum framework and is maintained by Scrum’s creators, Ken Schwaber and Jeff Sutherland.

    If you're asking this question, it's probably too late: you didn’t see this important new development coming, or didn’t understand it well enough to take it seriously, and now you must play the most dreaded game of all—catch-up.In How Did We Miss That?: How to Forecast the Next Big Thing, Amy Webb shows you how to avoid having to ask that frightening question. Successful business leaders seem to have a sixth sense about what's next; an uncanny ability to predict the next big trend or market development. But it's not magic. Webb teaches you how to spot today the signs of tomorrow's trends; specifically, the trends affecting your world—your market, your products, and your competitors. She demonstrates how the future doesn’t arrive fully formed, but rather emerges step-by-step, appearing first around the fringes of society. But that future is easy to miss unless you know where and how to look.Books about the near-future, not to mention countless blogs, postings, seminars, and conferences, simply tell you what the author thinks is coming, leaving you to pick the prognostication you prefer. Amy Webb shows you where and how to look in the present for harbingers of the future. Her book is an invaluable resource for leaders of all kinds.

    More and more frequently, ScrumMasters and project managers are being asked to coach agile teams. But it's a challenging role. It requires new skills--as well as a subtle understanding of when to step in and when to step back. Migrating from "command and control" to agile coaching requires a whole new mind-set. In Coaching Agile Teams, Lyssa Adkins gives agile coaches the insights they need to adopt this new mind-set and to guide teams to extraordinary performance in a re-energized work environment. You'll gain a deep view into the role of the agile coach, discover what works and what doesn't, and learn how to adapt powerful skills from many allied disciplines, including the fields of professional coaching and mentoring. Coverage includes Understanding what it takes to be a great agile coach Mastering all of the agile coach's roles: teacher, mentor, problem solver, conflict navigator, and performance coach Creating an environment where self-organized, high-performance teams can emerge Coaching teams past cooperation and into full collaboration Evolving your leadership style as your team grows and changes Staying actively engaged without dominating your team and stunting its growth Recognizing failure, recovery, and success modes in your coaching Getting the most out of your own personal agile coaching journey Whether you're an agile coach, leader, trainer, mentor, facilitator, ScrumMaster, project manager, product owner, or team member, this book will help you become skilled at helping others become truly great. What could possibly be more rewarding?

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    This authoritative book scrapes away these bad features to reveal a subset of JavaScript that's more reliable, readable, and maintainable than the language as a whole--a subset you can use to create truly extensible and efficient code.Considered the JavaScript expert by many people in the development community, author Douglas Crockford identifies the abundance of good ideas that make JavaScript an outstanding object-oriented programming language-ideas such as functions, loose typing, dynamic objects, and an expressive object literal notation. Unfortunately, these good ideas are mixed in with bad and downright awful ideas, like a programming model based on global variables.When Java applets failed, JavaScript became the language of the Web by default, making its popularity almost completely independent of its qualities as a programming language. In JavaScript: The Good Parts, Crockford finally digs through the steaming pile of good intentions and blunders to give you a detailed look at all the genuinely elegant parts of JavaScript, including:SyntaxObjectsFunctionsInheritanceArraysRegular expressionsMethodsStyleBeautiful featuresThe real beauty? As you move ahead with the subset of JavaScript that this book presents, you'll also sidestep the need to unlearn all the bad parts. Of course, if you want to find out more about the bad parts and how to use them badly, simply consult any other JavaScript book.With JavaScript: The Good Parts, you'll discover a beautiful, elegant, lightweight and highly expressive language that lets you create effective code, whether you're managing object libraries or just trying to get Ajax to run fast. If you develop sites or applications for the Web, this book is an absolute must.

    With this practical book, you’ll learn how to design effective microinteractions: the small details that exist inside and around features. How can users change a setting? How do they turn on mute, or know they have a new email message?Through vivid, real-world examples from today’s devices and applications, author Dan Saffer walks you through a microinteraction’s essential parts, then shows you how to use them in a mobile app, a web widget, and an appliance. You’ll quickly discover how microinteractions can change a product from one that’s tolerated into one that’s treasured.Explore a microinteraction’s structure: triggers, rules, feedback, modes, and loopsLearn the types of triggers that initiate a microinteractionCreate simple rules that define how your microinteraction can be usedHelp users understand the rules with feedback, using graphics, sounds, and vibrationsUse modes to let users set preferences or modify a microinteractionExtend a microinteraction’s life with loops, such as “Get data every 30 seconds”

    You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.To get you started--whether you're on Windows, OS X, or Linux--author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.Discover why the command line is an agile, scalable, and extensible technology. Even if you're already comfortable processing data with, say, Python or R, you'll greatly improve your data science workflow by also leveraging the power of the command line.Obtain data from websites, APIs, databases, and spreadsheetsPerform scrub operations on plain text, CSV, HTML/XML, and JSONExplore data, compute descriptive statistics, and create visualizationsManage your data science workflow using DrakeCreate reusable tools from one-liners and existing Python or R codeParallelize and distribute data-intensive pipelines using GNU ParallelModel data with dimensionality reduction, clustering, regression, and classification algorithms

    Through four years of groundbreaking research, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance—and what drives it—using rigorous statistical methods. This book presents both the findings and the science behind that research. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance.

    Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas: Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself. The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research. Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer's memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R's package system and debugging toolsPractice and apply R programming concepts as you learn them

    Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.New features in the second edition include:an interactive CD with PowerPoint presentations and problems for each chapter including an overview of the problem's solution; new chapters on basic research concepts including sampling, definitions of different types of variables, and basic research designs and one on nonparametric statistics; more graphs and more precise descriptions of each statistic; and a discussion of confidence intervals.This brief paperback is an ideal supplement for statistics, research methods, courses that use statistics, or as a reference tool to refresh one's memory about key concepts. The actual research examples are from psychology, education, and other social and behavioral sciences.Materials formerly available with this book on CD-ROM are now available for download from our website www.psypress.com. Go to the book's page and look for the 'Download' link in the right-hand column.

    A Problem2. Peter Pigeonhole Prepared A Petition3. What's Your Problem?Part 2: What is The Problem?4. Billy Brighteyes Bests The Bidders5. Billy Bites His Tongue6. Billy Back To The BiddersPart 3: What is The Problem Really?7. The Endless Chain8. Missing The Misfit9. Landing On The Level10. Mind Your MeaningPart 4: Whose Problem Is It?11. Smoke Gets In Your Eyes12. The Campus That Was All Spaced Out13. The Lights At The End Of The TunnelPart 5: Where Does It Come From?14. Janet Jaworski Joggles A Jerk15. Mister Matczyszyn Mends The Matter16. Make-Works And Take-Credits17. Examinations And Other PuzzlesPart 6: Do We Really Want To Solve It?18. Tom Tireless Tinkers With Toys19. Patience Plays Politics20. A Priority Assignment

    Previously undocumented, these 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves.The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently. Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.