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.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    It requires you to develop a data culture that involves people throughout the organization. In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt. You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century. You’ll explore: Data scientist skills—and why every company needs a Spock How the benefits of giving company-wide access to data outweigh the costs Why data-driven organizations use the scientific method to explore and solve data problems Key questions to help you develop a research-specific process for tackling important issues What to consider when assembling your data team Developing processes to keep your data team (and company) engaged Choosing technologies that are powerful, support teamwork, and easy to use and learn

    The revised edition is updated with a greater focus on digital recording techniques, the most powerful tools available to the home recordist. There are chapters devoted to instrument recording, humanizing drum patterns, mixing with plug-ins and virtual consoles, and a new section on using digital audio skills. And since, many true "Guerrillas" still record to analog tape, we have retained the best of that world. This edition features many more graphics than in the original edition, further enforcing Guerrilla Home Recording's reputation as the most readable, user-frienly recording title on the market.

    This makes the book random-access: Items are easy to browse and study in whatever order the reader needs. I will be recommending "Effective Python" to students as an admirably compact source of mainstream advice on a very broad range of topics for the intermediate Python programmer. " Brandon Rhodes, software engineer at Dropbox and chair of PyCon 2016-2017" It s easy to start coding with Python, which is why the language is so popular. However, Python s unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. " Effective Python " will help you master a truly Pythonic approach to programming, harnessing Python s full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers best-selling "Effective C++, " Brett Slatkin brings together 59 Python best practices, tips, and shortcuts, and explains them with realistic code examples. Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You ll learn the best way to accomplish key tasks, so you can write code that s easier to understand, maintain, and improve. Key features includeActionable guidelines for all major areas of Python 3.x and 2.x development, with detailed explanations and examples Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Coverage of how to accurately express behaviors with classes and objects Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient approaches to concurrency and parallelism Better techniques and idioms for using Python s built-in modules Tools and best practices for collaborative development Solutions for debugging, testing, and optimization in order to improve quality and performance "

    Windows Presentation Foundation (WPF) is a key component of the .NET Framework 3.0, giving you the power to create richer and more compelling applications than you dreamed possible. Whether you want to develop traditional user interfaces or integrate 3D graphics, audio/video, animation, dynamic skinning, rich document support, speech recognition, or more, WPF enables you to do so in a seamless, resolution-independent manner. Windows Presentation Foundation Unleashed is the authoritative book that covers it all, in a practical and approachable fashion, authored by .NET guru and Microsoft developer Adam Nathan. - Covers everything you need to know about Extensible Application Markup Language (XAML) - Examines the WPF feature areas in incredible depth: controls, layout, resources, data binding, styling, graphics, animation, and more - Features a chapter on 3D graphics by Daniel Lehenbauer, lead developer responsible for WPF 3D - Delves into non-mainstream topics: speech, audio/video, documents, bitmap effects, and more - Shows how to create popular UI elements, such as features introduced in the 2007 Microsoft Office System: Galleries, ScreenTips, custom control layouts, and more - Demonstrates how to create sophisticated UI mechanisms, such as Visual Studio-like collapsible/dockable panes - Explains how to develop and deploy all types of applications, including navigation-based applications, applications hosted in a Web browser, and applications with great-looking non-rectangular windows - Explains how to create first-class custom controls for WPF - Demonstrates how to create hybrid WPF software that leverages Windows Forms, ActiveX, or other non-WPF technologies - Explains how to exploit new Windows Vista features in WPF applications

    Build hands-on expertise through a series of lessons, exercises, and suggested practices—and help maximize your performance on the job.Provides in-depth, hands-on training you take at your own pace Focuses on job-role-specific expertise for using HTML5, JavaScript, and CSS3 to begin building modern web and Windows 8 apps Features pragmatic lessons, exercises, and practices Creates a foundation of skills which, along with on-the-job experience, can be measured by Microsoft Certification exams such as 70-480 Coverage includes: creating HTML5 documents; implementing styles with CSS3; JavaScript in depth; using Microsoft developer tools; AJAX; multimedia support; drawing with Canvas and SVG; drag and drop functionality; location-aware apps; web storage; offline apps; writing your first simple Windows 8 apps; and other key topics

    Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it--whether we realize it or not.Where do you send your child for the best education? Big Data. Which airline should you choose to ensure a timely arrival? Big Data. Who will you vote for in the next election? Big Data.The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations.In Numbersense, expert statistician Kaiser Fung explains when you should accept the conclusions of the Big Data experts--and when you should say, Wait . . . what? He delves deeply into a wide range of topics, offering the answers to important questions, such as:How does the college ranking system really work?Can an obesity measure solve America's biggest healthcare crisis?Should you trust current unemployment data issued by the government?How do you improve your fantasy sports team?Should you worry about businesses that track your data?Don't take for granted statements made in the media, by our leaders, or even by your best friend. We're on information overload today, and there's a lot of bad information out there.Numbersense gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician. But you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.Praise for NumbersenseNumbersense correctly puts the emphasis not on the size of big data, but on the analysis of it. Lots of fun stories, plenty of lessons learned--in short, a great way to acquire your own sense of numbers!Thomas H. Davenport, coauthor of Competing on Analytics and President's Distinguished Professor of IT and Management, Babson CollegeKaiser's accessible business book will blow your mind like no other. You'll be smarter, and you won't even realize it. Buy. It. Now.Avinash Kaushik, Digital Marketing Evangelist, Google, and author, Web Analytics 2.0Each story in Numbersense goes deep into what you have to think about before you trust the numbers. Kaiser Fung ably demonstrates that it takes skill and resourcefulness to make the numbers confess their meaning.John Sall, Executive Vice President, SAS InstituteKaiser Fung breaks the bad news--a ton more data is no panacea--but then has got your back, revealing the pitfalls of analysis with stimulating stories from the front lines of business, politics, health care, government, and education. The remedy isn't an advanced degree, nor is it common sense. You need Numbersense.Eric Siegel, founder, Predictive Analytics World, and author, Predictive AnalyticsI laughed my way through this superb-useful-fun book and learned and relearned a lot. Highly recommended! Tom Peters, author of In Search of Excellence

    This is a deeply technical book and focuses on the software engineering skills to ace your interview. The book is over 500 pages and includes 150 programming interview questions and answers, as well as other advice.The full list of topics are as follows:The Interview ProcessThis section offers an overview on questions are selected and how you will be evaluated. What happens when you get a question wrong? When should you start preparing, and how? What language should you use? All these questions and more are answered.Behind the ScenesLearn what happens behind the scenes during your interview, how decisions really get made, who you interview with, and what they ask you. Companies covered include Google, Amazon, Yahoo, Microsoft, Apple and Facebook.Special SituationsThis section explains the process for experience candidates, Program Managers, Dev Managers, Testers / SDETs, and more. Learn what your interviewers are looking for and how much code you need to know.Before the InterviewIn order to ace the interview, you first need to get an interview. This section describes what a software engineer's resume should look like and what you should be doing well before your interview.Behavioral PreparationAlthough most of a software engineering interview will be technical, behavioral questions matter too. This section covers how to prepare for behavioral questions and how to give strong, structured responses.Technical Questions (+ 5 Algorithm Approaches)This section covers how to prepare for technical questions (without wasting your time) and teaches actionable ways to solve the trickiest algorithm problems. It also teaches you what exactly "good coding" is when it comes to an interview.150 Programming Questions and AnswersThis section forms the bulk of the book. Each section opens with a discussion of the core knowledge and strategies to tackle this type of question, diving into exactly how you break down and solve it. Topics covered include• Arrays and Strings• Linked Lists• Stacks and Queues• Trees and Graphs• Bit Manipulation• Brain Teasers• Mathematics and Probability• Object-Oriented Design• Recursion and Dynamic Programming• Sorting and Searching• Scalability and Memory Limits• Testing• C and C++• Java• Databases• Threads and LocksFor the widest degree of readability, the solutions are almost entirely written with Java (with the exception of C / C++ questions). A link is provided with the book so that you can download, compile, and play with the solutions yourself.Changes from the Fourth Edition: The fifth edition includes over 200 pages of new content, bringing the book from 300 pages to over 500 pages. Major revisions were done to almost every solution, including a number of alternate solutions added. The introductory chapters were massively expanded, as were the opening of each of the chapters under Technical Questions. In addition, 24 new questions were added.Cracking the Coding Interview, Fifth Edition is the most expansive, detailed guide on how to ace your software development / programming interviews.

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

    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.

    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 pytest testing framework helps you write tests quickly and keep them readable and maintainable - with no boilerplate code. Using a robust yet simple fixture model, it's just as easy to write small tests with pytest as it is to scale up to complex functional testing for applications, packages, and libraries. This book shows you how.For Python-based projects, pytest is the undeniable choice to test your code if you're looking for a full-featured, API-independent, flexible, and extensible testing framework. With a full-bodied fixture model that is unmatched in any other tool, the pytest framework gives you powerful features such as assert rewriting and plug-in capability - with no boilerplate code.With simple step-by-step instructions and sample code, this book gets you up to speed quickly on this easy-to-learn and robust tool. Write short, maintainable tests that elegantly express what you're testing. Add powerful testing features and still speed up test times by distributing tests across multiple processors and running tests in parallel. Use the built-in assert statements to reduce false test failures by separating setup and test failures. Test error conditions and corner cases with expected exception testing, and use one test to run many test cases with parameterized testing. Extend pytest with plugins, connect it to continuous integration systems, and use it in tandem with tox, mock, coverage, unittest, and doctest.Write simple, maintainable tests that elegantly express what you're testing and why.What You Need: The examples in this book are written using Python 3.6 and pytest 3.0. However, pytest 3.0 supports Python 2.6, 2.7, and Python 3.3-3.6.

    In addition to giving a strong foundation in the language itself, Lubanovic shows how to use it for a range of applications in business, science, and the arts, drawing on the rich collection of open source packages developed by Python fans.It's impressive how many commercial and production-critical programs are written now in Python. Developed to be easy to read and maintain, it has proven a boon to anyone who wants applications that are quick to write but robust and able to remain in production for the long haul.This book focuses on the current version of Python, 3.x, while including sidebars about important differences with 2.x for readers who may have to deal with programs in that version.

    It also covers MySQL, and PostgreSQL. It contains examples which have been tested against each SQL platform, with incompatibilities or platform distinctives called out and explained.