The tools and recipes in this book will help you uncover and solve hidden (and not-so-hidden) problems with your technology, your methodology, and those difficult "people" issues on your team.Project retrospectives help teams examine what went right and what went wrong on a project. But traditionally, retrospectives (also known as "post-mortems") are only helpful at the end of the project--too late to help. You need agile retrospectives that are iterative and incremental. You need to accurately find and fix problems to help the team today.Now, Derby and Larsen show you the tools, tricks, and tips you need to fix the problems you face on a software development project on an on-going basis. You'll see how to architect retrospectives in general, how to design them specifically for your team and organization, how to run them effectively, how to make the needed changes, and how to scale these techniques up. You'll learn how to deal with problems, and implement solutions effectively throughout the project--not just at the end.With regular tune-ups, your team will hum like a precise, world-class orchestra.ContentsForwardPrefaceIntroduction1. Helping your team inspect and adapt2. A retrospective custom-fit for your team3. Leading retrospectives4. Activities to set the stage5. Activities to gather data6. Activities to gather insights7. Activities to decide what to do8. Activities to close the retrospective9. Releases and project retrospectives10. Make it soA1. Facilitation suppliesA2. Debriefing activitiesA3. Activities quick reference matrixA4. Resources for learning facilitation skillsA5. Bibliography

    This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts.The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants.Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.

    Rich Stevens describes more than 200 system calls and functions; since he believes the best way to learn code is to read code, a brief example accompanies each description.Building upon information presented in the first 15 chapters, the author offers chapter-long examples teaching you how to create a database library, a PostScript printer driver, a modem dialer, and a program that runs other programs under a pseudo terminal. To make your analysis and understanding of this code even easier, and to allow you to modify it, all of the code in the book is available via UUNET.A 20-page appendix provides detailed function prototypes for all the UNIX, POSIX, and ANSI C functions that are described in the book, and lists the page on which each prototype function is described in detail. Additional tables throughout the text and a thorough index make Advanced Programming in the UNIX Environment an invaluable reference tool that all UNIX programmers - beginners to experts - w

    If you are an organization that really needs to start grokking UX this book is also for you. " -- Chris Bernard, User Experience Evangelist, Microsoft User experience design is the discipline of creating a useful and usable Web site or application--one that's easily navigated and meets the needs of both the site owner and its users. But there's a lot more to successful UX design than knowing the latest Web technologies or design trends: It takes diplomacy, project management skills, and business savvy. That's where this book comes in. Authors Russ Unger and Carolyn Chandler show you how to integrate UX principles into your project from start to finish. - Understand the various roles in UX design, identify stakeholders, and enlist their support- Obtain consensus from your team on project objectives- Define the scope of your project and avoid mission creep- Conduct user research and document your findings- Understand and communicate user behavior with personas- Design and prototype your application or site- Make your product findable with search engine optimization- Plan for development, product rollout, and ongoing quality assurance

    Business analysis involves understanding how organizations function to accomplish their purposes and defining the capabilities an organization requires to provide products and services to external stakeholders. It includes the definition of organizational goals, understanding how those goals connect to specific objectives, determining the courses of action that an organization has to undertake to achieve those goals and objectives, and defining how the various organizational units and stakeholders within and outside of that organization interact. A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide) contains a description of generally accepted practices in the field of business analysis. The content included in this release has been verified through reviews by practitioners, surveys of the business analysis community, and consultations with recognized experts in the field. In less than five years, the BABOK® Guide has been recognized around the world as a key tool for the practice of business analysis and become a widely-accepted standard for the profession, with over 200,000 copies downloaded from the IIBA® website. Version 2.0 represents a major advance on that standard, and will become an essential reference for business analysis professionals.

    You’ll begin at square one, learning how the Web and web pages work, and then steadily build from there. By the end of the book, you’ll have the skills to create a simple site with multi-column pages that adapt for mobile devices.Learn how to use the latest techniques, best practices, and current web standards—including HTML5 and CSS3. Each chapter provides exercises to help you to learn various techniques, and short quizzes to make sure you understand key concepts.This thoroughly revised edition is ideal for students and professionals of all backgrounds and skill levels, whether you’re a beginner or brushing up on existing skills.Build HTML pages with text, links, images, tables, and formsUse style sheets (CSS) for colors, backgrounds, formatting text, page layout, and even simple animation effectsLearn about the new HTML5 elements, APIs, and CSS3 properties that are changing what you can do with web pagesMake your pages display well on mobile devices by creating a responsive web designLearn how JavaScript works—and why the language is so important in web designCreate and optimize web graphics so they’ll download as quickly as possible

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Aiming to tell the dark secrets of computer arithmetic, this title is suitable for library developers, compiler writers, and lovers of elegant hacks.

    Is it Rita's years of PMP exam preparation experience? The endless hours of ongoing research? The interviews with project managers who failed the exam, to identify gaps in their knowledge? Or is it the razor-sharp focus on making sure project managers don't waste a single minute of their time studying any more than they absolutely have to? Actually, it's all of the above. PMP Exam Prep, Sixth Edition by Rita Mulcahy contains hundreds of updates and improvements from previous editions--including new exercises and sample questions never before in print. Offering hundreds of sample questions, critical time-saving tips plus games and activities available nowhere else, this book will help you pass the PMP exam on your first try.

    His factory is rapidly heading for disaster. So is his marriage. He has ninety days to save his plant—or it will be closed by corporate HQ, with hundreds of job losses. It takes a chance meeting with a colleague from student days—Jonah—to help him break out of conventional ways of thinking to see what needs to be done.The story of Alex's fight to save his plant is more than compulsive reading. It contains a serious message for all managers in industry and explains the ideas which underline the Theory of Constraints (TOC) developed by Eli Goldratt.

    Over the past five years, authors Dustin Boswell and Trevor Foucher have analyzed hundreds of examples of "bad code" (much of it their own) to determine why they’re bad and how they could be improved. Their conclusion? You need to write code that minimizes the time it would take someone else to understand it—even if that someone else is you.This book focuses on basic principles and practical techniques you can apply every time you write code. Using easy-to-digest code examples from different languages, each chapter dives into a different aspect of coding, and demonstrates how you can make your code easy to understand.Simplify naming, commenting, and formatting with tips that apply to every line of codeRefine your program’s loops, logic, and variables to reduce complexity and confusionAttack problems at the function level, such as reorganizing blocks of code to do one task at a timeWrite effective test code that is thorough and concise—as well as readable"Being aware of how the code you create affects those who look at it later is an important part of developing software. The authors did a great job in taking you through the different aspects of this challenge, explaining the details with instructive examples." —Michael Hunger, passionate Software Developer

    However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

    

    In a few years, it's expected to be found inside millions of cell phones and other mobile devices, making Android a major platform for application developers. That could be your own program running on all those devices.Getting started developing with Android is easy. You don't even need access to an Android phone, just a computer where you can install the Android SDK and the phone emulator that comes with it. Within minutes, "Hello, Android" will get you creating your first working application: Android's version of "Hello, World."From there, you'll build up a more substantial example: an Android Sudoku game. By gradually adding features to the game throughout the course of the book, you'll learn about many aspects of Android programming including user interfaces, multimedia, and the Android life cycle.If you're a busy developer who'd rather be coding than reading about coding, this book is for you. To help you find what you need to know fast, each chapter ends with "Fast forward" section. These sections provide guidance for where you should go next when you need to read the book out of order.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details