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

    Tracy Kidder got a preview of this world in the late 1970s when he observed the engineers of Data General design and build a new 32-bit minicomputer in just one year. His thoughtful, prescient book, The Soul of a New Machine, tells stories of 35-year-old "veteran" engineers hiring recent college graduates and encouraging them to work harder and faster on complex and difficult projects, exploiting the youngsters' ignorance of normal scheduling processes while engendering a new kind of work ethic.These days, we are used to the "total commitment" philosophy of managing technical creation, but Kidder was surprised and even a little alarmed at the obsessions and compulsions he found. From in-house political struggles to workers being permitted to tease management to marathon 24-hour work sessions, The Soul of a New Machine explores concepts that already seem familiar, even old-hat, less than 20 years later. Kidder plainly admires his subjects; while he admits to hopeless confusion about their work, he finds their dedication heroic. The reader wonders, though, what will become of it all, now and in the future. —Rob Lightner

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Here is a complete library of dimensional modeling techniques-- the most comprehensive collection ever written. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is more than sixty percent updated.The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including:* Retail sales and e-commerce* Inventory management* Procurement* Order management* Customer relationship management (CRM)* Human resources management* Accounting* Financial services* Telecommunications and utilities* Education* Transportation* Health care and insuranceBy the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.This book is also available as part of the Kimball's Data Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with the following 3 books:The Data Warehouse Toolkit, 2nd Edition (9780471200246)The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775)The Data Warehouse ETL Toolkit (9780764567575)

    How do you present large volumes of information to people who need to find what they're looking for quickly? This classic primer shows information architects, designers, and web site developers how to build large-scale and maintainable web sites that are appealing and easy to navigate. The new edition is thoroughly updated to address emerging technologies -- with recent examples, new scenarios, and information on best practices -- while maintaining its focus on fundamentals. With topics that range from aesthetics to mechanics, Information Architecture for the World Wide Web explains how to create interfaces that users can understand right away. Inside, you'll find:* An overview of information architecture for both newcomers and experienced practitioners* The fundamental components of an architecture, illustrating the interconnected nature of these systems. Updated, with updates for tagging, folksonomies, social classification, and guided navigation* Tools, techniques, and methods that take you from research to strategy and design to implementation. This edition discusses blueprints, wireframes and the role of diagrams in the design phase* A series of short essays that provide practical tips and philosophical advice for those who work on information architecture* The business context of practicing and promoting information architecture, including recent lessons on how to handle enterprise architecture* Case studies on the evolution of two large and very different information architectures, illustrating best practices along the way* How do you document the rich interfaces of web applications? How do you design for multiple platforms and mobile devices? With emphasis on goals and approaches over tactics or technologies, this enormously popular book gives you knowledge about information architecture with a framework that allows you to learn new approaches -- and unlearn outmoded ones.

    This book is a short, quickly-readable summary and introduction to the fundamentals of DDD; it does not introduce any new concepts; it attempts to concisely summarize the essence of what DDD is, drawing mostly Eric Evans' original book, as well other sources since published such as Jimmy Nilsson's Applying Domain Driven Design, and various DDD discussion forums. The main topics covered in the book include: Building Domain Knowledge, The Ubiquitous Language, Model Driven Design, Refactoring Toward Deeper Insight, and Preserving Model Integrity. Also included is an interview with Eric Evans on Domain Driven Design today.

    By understanding what the browser can and cannot do, you’ll be able to make better design decisions and deliver faster web applications to your users.Author Ilya Grigorik—a developer advocate and web performance engineer at Google—starts with the building blocks of TCP and UDP, and then dives into newer technologies such as HTTP 2.0, WebSockets, and WebRTC. This book explains the benefits of these technologies and helps you determine which ones to use for your next application.- Learn how TCP affects the performance of HTTP- Understand why mobile networks are slower than wired networks- Use best practices to address performance bottlenecks in HTTP- Discover how HTTP 2.0 (based on SPDY) will improve networking- Learn how to use Server Sent Events (SSE) for push updates, and WebSockets for XMPP chat- Explore WebRTC for browser-to-browser applications such as P2P video chat- Examine the architecture of a simple app that uses HTTP 2.0, SSE, WebSockets, and WebRTC

    You can’t just find a pattern and copy it into your program, the way you can with off-the-shelf functions or libraries. The pattern is not a specific piece of code, but a general concept for solving a particular problem. They are like pre-made blueprints that you can customize to solve a recurring design problem in your code.The book Dive Into Design Patterns illustrates 22 classic design patterns and 8 design principles that these patterns are based on.- Every chapter starts from a discussion of a real life software design problem which is then progressively solved by applying one of the patterns.- Then goes a detailed review of the pattern’s structure and its variations, followed by a code example.- Then the books shows various applications of the pattern and teaches how to implement the pattern step by step, even in an existing program.- Each chapter concludes with a discussion of pros and cons of the pattern and its relations, similarities and differences with other patterns.

    Your process may be agile, but are you building agility directly into the code base? This book teaches .NET programmers how to give code the flexibility to adapt to changing requirements and customer demands by applying cutting-edge techniques, including SOLID principles, design patterns, and other industry best practices.Understand why composition is preferable to inheritance and how flexible the interface really can be Gain deep knowledge of key design patterns and anti-patterns, when to apply them, and how to give their code agility Bridge the gap between the theory behind SOLID principles, design patterns, and industry best practices by pragmatically solving real-world problems Get code samples written in upcoming version of Microsoft Visual C# Topics include: Agile with Scrum process; dependencies and layering; the interface; patterns and anti-patterns; introduction to SOLID principles, including open/closed and dependency interjection; and using application templates

    

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    You'll find the latest methodologies for the design of effective test cases, including information on psychological and economic principles, managerial aspects, test tools, high-order testing, code inspections, and debugging. Accessible, comprehensive, and always practical, this edition provides the key information you need to test successfully, whether a novice or a working programmer. Buy your copy today and end up with fewer bugs tomorrow.

    

    "Peter Cooper, Editor of" Ruby Inside Using Rails, developers can build web applications of exceptional elegance and power. Although its remarkable capabilities have made Ruby on Rails one of the world s most popular web development frameworks, it can be challenging to learn and use. " Ruby on Rails Tutorial, Second Edition, " is the solution. Best-selling author and leading Rails developer Michael Hartl teaches Rails by guiding you through the development of your own complete sample application using the latest techniques in Rails web development. The updates to this edition include all-new site design using Twitter s Bootstrap; coverage of the new asset pipeline, including Sprockets and Sass; behavior-driven development (BDD) with Capybara and RSpec; better automated testing with Guard and Spork; roll your own authentication with has_secure_password; and an introduction to Gherkin and Cucumber. You ll find integrated tutorials not only for Rails, but also for the essential Ruby, HTML, CSS, JavaScript, and SQL skills you ll need when developing web applications. Hartl explains how each new technique solves a real-world problem, and he demonstrates this with bite-sized code that s simple enough to understand, yet novel enough to be useful. Whatever your previous web development experience, this book will guide you to true Rails mastery. This book will help you Install and set up your Rails development environment Go beyond generated code to truly understand how to build Rails applications from scratch Learn test-driven development (TDD) with RSpec Effectively use the Model-View-Controller (MVC) pattern Structure applications using the REST architecture Build static pages and transform them into dynamic ones Master the Ruby programming skills all Rails developers need Define high-quality site layouts and data models Implement registration and authentication systems, including validation and secure passwords Update, display, and delete users Add social features and microblogging, including an introduction to Ajax Record version changes with Git and share code at GitHub Simplify application deployment with Heroku

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.