Topics covered include:Basic version control commands and conceptsIntroduction to Distributed Version Control Systems (DVCS)Advanced branching workflowsStrengths and weaknesses of DVCS vs. centralized toolsBest practicesHow distributed version control works under the hoodFeaturing these open source version control tools:Apache SubversionMercurialGitVeracity

    Are You Ready To Learn Python Easily? Learning Python Programming in 7 days is possible, although it might not look like it

    Two leaders in the requirements community have teamed up to deliver a contemporary set of practices covering the full range of requirements development and management activities on software projects. Describes practical, effective, field-tested techniques for managing the requirements engineering process from end to end. Provides examples demonstrating how requirements "good practices" can lead to fewer change requests, higher customer satisfaction, and lower development costs. Fully updated with contemporary examples and many new practices and techniques. Describes how to apply effective requirements practices to agile projects and numerous other special project situations. Targeted to business analysts, developers, project managers, and other software project stakeholders who have a general understanding of the software development process. Shares the insights gleaned from the authors' extensive experience delivering hundreds of software-requirements training courses, presentations, and webinars.New chapters are included on specifying data requirements, writing high-quality functional requirements, and requirements reuse. Considerable depth has been added on business requirements, elicitation techniques, and nonfunctional requirements. In addition, new chapters recommend effective requirements practices for various special project situations, including enhancement and replacement, packaged solutions, outsourced, business process automation, analytics and reporting, and embedded and other real-time systems projects.

    This book offers an excellent foundation in ethical decision-making for current and future business managers and IT professionals.

    This Self-Paced Training Kit is designed to help maximize your performance on 70-680, the required exam for the Microsoft Certified Technology Specialist (MCTS): Windows 7, Configuration certification.This 2-in-1 kit includes the official Microsoft study guide, plus practice tests on CD to help you assess your skills. It comes packed with the tools and features exam candidates want most—including in-depth, self-paced training based on final exam content; rigorous, objective-by-objective review; exam tips from expert, exam-certified authors; and customizable testing options. It also provides real-world scenarios, case study examples, and troubleshooting labs to give you the skills and expertise you can use on the job.Work at your own pace through the lessons and lab exercises. This official study guide covers installing, upgrading, and migrating to Windows 7; configuring network connectivity, applications, and devices; implementing backup and recovery; configuring User Account Control (UAC), mobility options, and new features such as DirectAccess and BranchCache; and managing system updates.Then assess yourself using the 200 practice questions on CD, featuring multiple customizable testing options to meet your specific needs. Choose timed or untimed testing mode, generate random tests, or focus on discrete objectives. You get detailed explanations for right and wrong answers—including pointers back to the book for further study. You also get an exam discount voucher—making this kit an exceptional value and a great career investment.

    Written to aid electrical engineers, computer engineers, and computer scientists, the volume includes: KEY FEATURES: the computer architecture, organization, and design associated with computer hardware - the various digital components used in the organization and design of digital computers - detailed steps that a designer must go through in order to design an elementary basic computer - the organization and architecture of the central processing unit - the organization and architecture of input-output and memory - the concept of multiprocessing - two new chapters on pipeline and vector processing - two sections devoted completely to the reduced instruction set computer (RISC) - and sample worked-out problems to clarify topics.

    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.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    Along the way, we encounter black holes, turtles, snakes, dragons, axe-sharpening, and yak-shaving—and take a guided tour through the theories and methods, both brilliant and misguided, that litter the history of software development, from the famous ‘mythical man-month’ to Extreme Programming. Not just for technophiles but for anyone captivated by the drama of invention, Dreaming in Code offers a window into both the information age and the workings of the human mind.

    You'll find complete coverage of all exam objectives, including key topics such as project planning, execution, delivery, closure, and others. CompTIA's Project+ is the foundation-level professional exam in the complex world of project management; certified project managers often choose to go on and obtain their Project Management Professional (PMP) certifications as well Provides complete coverage of all exam objectives for CompTIA's first update to the Project+ exam in six years Covers project planning, execution, delivery, change, control, communication, and closure Demonstrates and reinforces exam preparation with practical examples and real-word scenarios Includes a CD with Sybex test engine, practice exams, electronic flashcards, and a PDF of the book Approach the new Project+ exam with confidence with this in-depth study guide! Reviews

    Under the leadership of Henrik Kniberg they experimented with different team sizes, different sprint lengths, different ways of defining "done", different formats for product backlogs and sprint backlogs, different testing strategies, different ways of doing demos, different ways of synchronizing multiple Scrum teams, etc. They also experimented with XP practices - different ways of doing continuous build, pair programming, test driven development, etc, and how to combine this with Scrum.

    " From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required just the ability to program in a modestly extended version of C. " CUDA by Example, " written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance. Major topics covered includeParallel programmingThread cooperationConstant memory and eventsTexture memoryGraphics interoperabilityAtomicsStreamsCUDA C on multiple GPUsAdvanced atomicsAdditional CUDA resources All the CUDA software tools you ll need are freely available for download from NVIDIA.http: //developer.nvidia.com/object/cuda-by-e...

    Rust's modern, flexible types ensure your program is free of null pointer dereferences, double frees, dangling pointers, and similar bugs, all at compile time, without runtime overhead. In multi-threaded code, Rust catches data races at compile time, making concurrency much easier to use.Written by two experienced systems programmers, this book explains how Rust manages to bridge the gap between performance and safety, and how you can take advantage of it. Topics include:How Rust represents values in memory (with diagrams)Complete explanations of ownership, moves, borrows, and lifetimesCargo, rustdoc, unit tests, and how to publish your code on crates.io, Rust's public package repositoryHigh-level features like generic code, closures, collections, and iterators that make Rust productive and flexibleConcurrency in Rust: threads, mutexes, channels, and atomics, all much safer to use than in C or C++Unsafe code, and how to preserve the integrity of ordinary code that uses itExtended examples illustrating how pieces of the language fit together

    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

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.