Now, in an even more forward-thinking and necessary book, he shows how to take that critical next step on a creative journey—getting known. Show Your Work! is about why generosity trumps genius. It’s about getting findable, about using the network instead of wasting time “networking.” It’s not self-promotion, it’s self-discovery—let others into your process, then let them steal from you. Filled with illustrations, quotes, stories, and examples, Show Your Work! offers ten transformative rules for being open, generous, brave, productive. In chapters such as You Don’t Have to Be a Genius; Share Something Small Every Day; and Stick Around, Kleon creates a user’s manual for embracing the communal nature of creativity— what he calls the “ecology of talent.” From broader life lessons about work (you can’t find your voice if you don’t use it) to the etiquette of sharing—and the dangers of oversharing—to the practicalities of Internet life (build a good domain name; give credit when credit is due), it’s an inspiring manifesto for succeeding as any kind of artist or entrepreneur in the digital age.

    The ability to effectively articulate design decisions is critical to the success of a project, because the most articulate person often wins. This practical book provides principles, tactics, and actionable methods for talking about designs with executives, managers, developers, marketers, and other stakeholders who have influence over the project with the goal of winning them over and creating the best user experience.

    More than a series of fascinating case studies, To Engineer Is Human is a work that looks at our deepest notions of progress and perfection, tracing the fine connection between the quantifiable realm of science and the chaotic realities of everyday life."Alert, inquisitive, unspecialized, wholly human...refreshingly eclectic." --The Spectator"Henry Petroski is an ardent engineer, and if he writes more good books like this, he might find himself nominated to become the meistersinger of the guild. [This is] a refreshing plunge into the dynamics of the engineering ethos...as straightforward as an I-beam."--Science

    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.

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

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Weinberg adds new insights and highlights the similarities and differences between now and then. Using a conversational style that invites the reader to join him, Weinberg reunites with some of his most insightful writings on the human side of software engineering.Topics include egoless programming, intelligence, psychological measurement, personality factors, motivation, training, social problems on large projects, problem-solving ability, programming language design, team formation, the programming environment, and much more.Dorset House Publishing is proud to make this important text available to new generations of programmers -- and to encourage readers of the first edition to return to its valuable lessons.

    "His book is very compatible with XP. It is not about drawing pictures of a domain; it is about how you think of it, the language you use to talk about it, and how you organize your software to reflect your improving understanding of it. Eric thinks that learning about your problem domain is as likely to happen at the end of your project as at the beginning, and so refactoring is a big part of his technique. "The book is a fun read. Eric has lots of interesting stories, and he has a way with words. I see this book as essential reading for software developers--it is a future classic." --Ralph Johnson, author of Design Patterns "If you don't think you are getting value from your investment in object-oriented programming, this book will tell you what you've forgotten to do. "Eric Evans convincingly argues for the importance of domain modeling as the central focus of development and provides a solid framework and set of techniques for accomplishing it. This is timeless wisdom, and will hold up long after the methodologies du jour have gone out of fashion." --Dave Collins, author of Designing Object-Oriented User Interfaces "Eric weaves real-world experience modeling--and building--business applications into a practical, useful book. Written from the perspective of a trusted practitioner, Eric's descriptions of ubiquitous language, the benefits of sharing models with users, object life-cycle management, logical and physical application structuring, and the process and results of deep refactoring are major contributions to our field." --Luke Hohmann, author of Beyond Software Architecture "This book belongs on the shelf of every thoughtful software developer." --Kent Beck "What Eric has managed to capture is a part of the design process that experienced object designers have always used, but that we have been singularly unsuccessful as a group in conveying to the rest of the industry. We've given away bits and pieces of this knowledge...but we've never organized and systematized the principles of building domain logic. This book is important." --Kyle Brown, author of Enterprise Java(TM) Programming with IBM(R) WebSphere(R) The software development community widely acknowledges that domain modeling is central to software design. Through domain models, software developers are able to express rich functionality and translate it into a software implementation that truly serves the needs of its users. But despite its obvious importance, there are few practical resources that explain how to incorporate effective domain modeling into the software development process. Domain-Driven Design fills that need. This is not a book about specific technologies. It offers readers a systematic approach to domain-driven design, presenting an extensive set of design best practices, experience-based techniques, and fundamental principles that facilitate the development of software projects facing complex domains. Intertwining design and development practice, this book incorporates numerous examples based on actual projects to illustrate the application of domain-driven design to real-world software development. Readers learn how to use a domain model to make a complex development effort more focused and dynamic. A core of best practices and standard patterns provides a common language for the development team. A shift in emphasis--refactoring not just the code but the model underlying the code--in combination with the frequent iterations of Agile development leads to deeper insight into domains and enhanced communication between domain expert and programmer. Domain-Driven Design then builds on this foundation, and addresses modeling and design for complex systems and larger organizations.Specific topics covered include:Getting all team members to speak the same language Connecting model and implementation more deeply Sharpening key distinctions in a model Managing the lifecycle of a domain object Writing domain code that is safe to combine in elaborate ways Making complex code obvious and predictable Formulating a domain vision statement Distilling the core of a complex domain Digging out implicit concepts needed in the model Applying analysis patterns Relating design patterns to the model Maintaining model integrity in a large system Dealing with coexisting models on the same project Organizing systems with large-scale structures Recognizing and responding to modeling breakthroughs With this book in hand, object-oriented developers, system analysts, and designers will have the guidance they need to organize and focus their work, create rich and useful domain models, and leverage those models into quality, long-lasting software implementations.

    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.

    Ironically, I must recommend that you read her every word so that you can find out why your customers won't read very many words on your website -- and what to do about it.-- Jakob Nielsen, Principal, Nielsen Norman Group"There are at least twelve billion web pages out there. Twelve billion voices talking, but saying mostly nothing. If just 1% of those pages followed Ginny's practical, clear advice, the world would be a better place. Fortunately, you can follow her advice for 100% of your own site's pages, so pick up a copy of Letting Go of the Words and start communicating effectively today."--Lou Rosenfeld, co-author, Information Architecture for the World Wide WebOn the web, whether on the job or at home, we usually want to grab information and use it quickly. We go to the web to get answers to questions or to complete tasks - to gather information, reading only what we need. We are all too busy to read much on the web.This book helps you write successfully for web users. It offers strategy, process, and tactics for creating or revising content for the web. It helps you plan, organize, write, design, and test web content that will make web users come back again and again to your site.Learn how to create usable and useful content for the web from the master - Ginny Redish. Ginny has taught and mentored hundreds of writers, information designers, and content owners in the principles and secrets of creating web information that is easy to scan, easy to read, and easy to use.This practical, informative book will help anyone creating web content do it better.Features* Clearly-explained guidelines with full color illustrations and examples from actual web sites throughout the book. * Written in easy-to-read style with many befores and afters.* Specific guidelines for web-based press releases, legal notices, and other documents.* Tips on making web content accessible for people with special needs.Janice (Ginny) Redish has been helping clients and colleagues communicate clearly for more than 20 years. For the past ten years, her focus has been helping people create usable and useful web sites. She is co-author of two classic books on usability: A Practical Guide to Usability Testing (with Joseph Dumas), and User and Task Analysis for Interface Design (with JoAnn Hackos), and is the recipient of many awards.

    Topics covered range from the difference between deductive and inductive reasoning, to a discussion of how to highlight the structure of information.

    The answers aren't easy -- just incredibly successful.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    In this lively and groundbreaking new book, economist Ian Ayres shows how today's best and brightest organizations are analyzing massive databases at lightening speed to provide greater insights into human behavior. They are the Super Crunchers. From internet sites like Google and Amazon that know your tastes better than you do, to a physician's diagnosis and your child's education, to boardrooms and government agencies, this new breed of decision makers are calling the shots. And they are delivering staggeringly accurate results. How can a football coach evaluate a player without ever seeing him play? Want to know whether the price of an airline ticket will go up or down before you buy? How can a formula outpredict wine experts in determining the best vintages? Super crunchers have the answers. In this brave new world of equation versus expertise, Ayres shows us the benefits and risks, who loses and who wins, and how super crunching can be used to help, not manipulate us.Gone are the days of solely relying on intuition to make decisions. No businessperson, consumer, or student who wants to stay ahead of the curve should make another keystroke without reading Super Crunchers.