Politicians and marketers present shoddy evidence for dubious claims all the time. But smart people make mistakes too, and when it comes to statistics, plenty of otherwise great scientists--yes, even those published in peer-reviewed journals--are doing statistics wrong."Statistics Done Wrong" comes to the rescue with cautionary tales of all-too-common statistical fallacies. It'll help you see where and why researchers often go wrong and teach you the best practices for avoiding their mistakes.In this book, you'll learn: - Why "statistically significant" doesn't necessarily imply practical significance- Ideas behind hypothesis testing and regression analysis, and common misinterpretations of those ideas- How and how not to ask questions, design experiments, and work with data- Why many studies have too little data to detect what they're looking for-and, surprisingly, why this means published results are often overestimates- Why false positives are much more common than "significant at the 5% level" would suggestBy walking through colorful examples of statistics gone awry, the book offers approachable lessons on proper methodology, and each chapter ends with pro tips for practicing scientists and statisticians. No matter what your level of experience, "Statistics Done Wrong" will teach you how to be a better analyst, data scientist, or researcher.

    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.

    

     They say you shouldn't ask your mom whether your business is a good idea, because she loves you and will lie to you. This is technically true, but it misses the point. You shouldn't ask anyone if your business is a good idea. It's a bad question and everyone will lie to you at least a little . As a matter of fact, it's not their responsibility to tell you the truth. It's your responsibility to find it and it's worth doing right .Talking to customers is one of the foundational skills of both Customer Development and Lean Startup. We all know we're supposed to do it, but nobody seems willing to admit that it's easy to screw up and hard to do right. This book is going to show you how customer conversations go wrong and how you can do better.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    The Web is awash in Ruby code that is now virtually impossible to change or extend. This text helps you solve that problem by using powerful real-world object-oriented design techniques, which it thoroughly explains using simple and practical Ruby examples. Sandi Metz has distilled a lifetime of conversations and presentations about object-oriented design into a set of Ruby-focused practices for crafting manageable, extensible, and pleasing code. She shows you how to build new applications that can survive success and repair existing applications that have become impossible to change. Each technique is illustrated with extended examples, all downloadable from the companion Web site, poodr.info. The first title to focus squarely on object-oriented Ruby application design, Practical Object-Oriented Design in Ruby will guide you to superior outcomes, whatever your previous Ruby experience. Novice Ruby programmers will find specific rules to live by; intermediate Ruby programmers will find valuable principles they can flexibly interpret and apply; and advanced Ruby programmers will find a common language they can use to lead development and guide their colleagues. This guide will help you Understand how object-oriented programming can help you craft Ruby code that is easier to maintain and upgrade Decide what belongs in a single Ruby class Avoid entangling objects that should be kept separate Define flexible interfaces among objects Reduce programming overhead costs with duck typing Successfully apply inheritance Build objects via composition Design cost-effective tests Solve common problems associated with poorly designed Ruby code

    Plenty of books describe what agile development is or why it helps software projects succeed, but very few combine information for developers, managers, testers, and customers into a single package that they can apply directly. This book provides no-nonsense advice on agile planning, development, delivery, and management taken from the authors' many years of experience with Extreme Programming (XP). You get a gestalt view of the agile development process, including comprehensive guidance for non-technical readers and hands-on technical practices for developers and testers. The Art of Agile Development gives you clear answers to questions such as:How can we adopt agile development? Do we really need to pair program? What metrics should we report? What if I can't get my customer to participate? How much documentation should we write? When do we design and architect? As a non-developer, how should I work with my agile team? Where is my product roadmap? How does QA fit in? The book teaches you how to adopt XP practices, describes each practice in detail, then discusses principles that will allow you to modify XP and create your own agile method. In particular, this book tackles the difficult aspects of agile development: the need for cooperation and trust among team members. Whether you're currently part of an agile team, working with an agile team, or interested in agile development, this book provides the practical tips you need to start practicing agile development. As your experience grows, the book will grow with you, providing exercises and information that will teach you first to understand the rules of agile development, break them, and ultimately abandon rules altogether as you master the art of agile development. "Jim Shore and Shane Warden expertly explain the practices and benefits of Extreme Programming. They offer advice from their real-world experiences in leading teams. They answer questions about the practices and show contraindications - ways that a practice may be mis-applied. They offer alternatives you can try if there are impediments to applying a practice, such as the lack of an on-site customer. --Ken Pugh, Author of Jolt Award Winner, Prefactoring "I will leave a copy of this book with every team I visit." --Brian Marick, Exampler Consulting

    But how do you build the best team organization for your specific goals, culture, and needs? Team Topologies is a practical, step-by-step, adaptive model for organizational design and team interaction based on four fundamental team types and three team interaction patterns. It is a model that treats teams as the fundamental means of delivery, where team structures and communication pathways are able to evolve with technological and organizational maturity.In Team Topologies, IT consultants Matthew Skelton and Manuel Pais share secrets of successful team patterns and interactions to help readers choose and evolve the right team patterns for their organization, making sure to keep the software healthy and optimize value streams.Team Topologies is a major step forward in organizational design for software, presenting a well-defined way for teams to interact and interrelate that helps make the resulting software architecture clearer and more sustainable, turning inter-team problems into valuable signals for the self-steering organization.

    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site

    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.

    Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more. How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing". Used by many researchers and companies to convey specific data in a clear and understandable manner, the Processing beta is available free. With this tool and Visualizing Data as a guide, you'll learn basic visualization principles, how to choose the right kind of display for your purposes, and how to provide interactive features that will bring users to your site over and over. This book teaches you:The seven stages of visualizing data -- acquire, parse, filter, mine, represent, refine, and interact How all data problems begin with a question and end with a narrative construct that provides a clear answer without extraneous details Several example projects with the code to make them work Positive and negative points of each representation discussed. The focus is on customization so that each one best suits what you want to convey about your data set The book does not provide ready-made "visualizations" that can be plugged into any data set. Instead, with chapters divided by types of data rather than types of display, you'll learn how each visualization conveys the unique properties of the data it represents -- why the data was collected, what's interesting about it, and what stories it can tell. Visualizing Data teaches you how to answer questions, not simply display information.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.

    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

    The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.