This complete study guide provides in-depth, up-to-date coverage of all the exam objectives, and goes a step beyond to cover the Java Developer exam (now an Oracle Certified Expert level credential).This book provides an integrated study system based on proven pedagogy--step-by-step exercises, special Exam Watch, Inside-the-Exam, and On-the-Job notes, and chapter self tests help reinforce and teach practical skills while preparing you for the exam. The CD-ROM includes MasterExam practice exam software featuring more than 100 questions that appear only on the CD, and a searchable e-book."OCP Java SE 7 Programmer Study Guide" Covers all new OCP Java SE 7 Programmer exam objectives Written by the co-developers of the original SCJP exam Filled with accurate test questions that simulate the type and style of questions found on the live exam Contains two complete practice exams--250+ challenging practice exam questions in book and on CD All practice questions include answer explanations for both the correct and incorrect options

    The NYSE computers went down and trading was suspended for several hours. The culprit wasn't hackers or a rogue algorithm. It was just... a glitch. And it's just the beginning. Technological complexity is no trivial matter. While a few hours of suspended trading may not have had lasting impact on the markets, imagine the damage that could result from a breakdown of our air traffic control systems, or earthquake warning systems. We need a new way to think about technology, and we need it fast. In Overcomplicated, complexity scientist Samuel Arbesman argues that we've reached a new era: a time when our technological systems have become too complex and interconnected for us to fully understand or predict.  From our machines and software to our legal frameworks and urban infrastructure, Arbesman explores the forces that lead us to continue to make systems more complicated and more incomprehensible, despite our best efforts to make them simpler. He goes on to identify a new framework for thinking about (and planning within) complex systems. We must abandon the idea that we will understand the rules, and instead become field biologists for technology--relying on description and observation to uncover facts about how a system might work.  Whether you work in business, finance, science, or IT, or you simply own a smart phone, Overcomplicated offers valuable insight on how to adapt to the complex age we are living in.

     Now, in Forward, Yang reveals that UBI and the threat of job automation are only the beginning, diagnosing how a series of cascading problems within our antiquated systems keeps us stuck in the past—imperiling our democracy at every level. With America’s stagnant institutions failing to keep pace with technological change, we grow more polarized as tech platforms supplant our will while feasting on our data. Yang introduces us to the various “priests of the decline” of America, including politicians whose incentives have become divorced from the people they supposedly serve. The machinery of American democracy is failing, Yang argues, and we need bold new ideas to rewire it for twenty-first-century problems. Inspired by his experience running for office and as an entrepreneur, and by ideas drawn from leading thinkers, Yang offers a series of solutions, including data rights, ranked-choice voting, and fact-based governance empowered by modern technology, writing that “there is no cavalry”—it’s up to us. This is a powerful and urgent warning that we must step back from the brink and plot a new way forward for our democracy.

    Gain a deep understanding of the “what, why, how, when, how much” questions of your life. Become a Systems Thinker and discover how to approach your life from a completely new perspective. What is systems thinking? Put it simply, thinking about how things interact with one another. Why should this matter to you? Because you are a system. You are a part of smaller and larger systems – your community, your country, your species. Understanding your role within these systems and how these systems affect, hinder, or aid the fulfillment of your life can lead you to better answers about yourself and the world. Information is the most precious asset these days. Evaluating that information correctly is almost priceless. Systems thinkers are some of the bests in collecting and assessing information, as well as creating impactful solutions in any context. The Systems Thinker will help you to implement systems thinking at your workplace, human relations, and everyday thinking habits. Boost your observation and analytical skills to find the real triggers and influencing forces behind contemporary politics, economics, health, and education changes. Systems thinking clears your vision by teaching you not only to find the differences between the elements but also the similarities. This bi-directional analyzing ability will give you a more complex worldview, deeper understanding of problems, and thus better solutions. The car stopped because its tank is empty – so it needs gas. Easy problem, easy solution, right? But could you explain just as easily why did the price of gas raise with 5% the past month? After becoming a systems thinker, you’ll be able to answer that question just as easily. Change your thoughts, change your results. •What are the main elements, questions and methods of thinking in systems? •The most widely used systems archetypes, maps, models, and analytical methods. •Learn to identify and provide solutions even the most complex system problems. •Deepen your understanding about human motivation with systems thinking. The past fifty years brought so many changes in our lives. The world has become more interconnected than ever. Old rules can’t explain the new world anymore. But systems thinking can. Embrace systems thinking and become a master of analytical, critical, and creative thinking.

    The book will teach you about the most important services on AWS. You will also learn about best practices regarding automation, security, high availability, and scalability.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyPhysical data centers require lots of equipment and take time and resources to manage. If you need a data center, but don't want to build your own, Amazon Web Services may be your solution. Whether you're analyzing real-time data, building software as a service, or running an e-commerce site, AWS offers you a reliable cloud-based platform with services that scale. All services are controllable via an API which allows you to automate your infrastructure.About the BookAmazon Web Services in Action introduces you to computing, storing, and networking in the AWS cloud. The book will teach you about the most important services on AWS. You will also learn about best practices regarding security, high availability and scalability.You'll start with a broad overview of cloud computing and AWS and learn how to spin-up servers manually and from the command line. You'll learn how to automate your infrastructure by programmatically calling the AWS API to control every part of AWS. You will be introduced to the concept of Infrastructure as Code with the help of AWS CloudFormation.You will learn about different approaches to deploy applications on AWS. You'll also learn how to secure your infrastructure by isolating networks, controlling traffic and managing access to AWS resources. Next, you'll learn options and techniques for storing your data. You will experience how to integrate AWS services into your own applications by the use of SDKs. Finally, this book teaches you how to design for high availability, fault tolerance, and scalability.What's InsideOverview of cloud concepts and patternsManage servers on EC2 for cost-effectivenessInfrastructure automation with Infrastructure as Code (AWS CloudFormation)Deploy applications on AWSStore data on AWS: SQL, NoSQL, object storage and block storageIntegrate Amazon's pre-built servicesArchitect highly available and fault tolerant systemsAbout the ReaderWritten for developers and DevOps engineers moving distributed applications to the AWS platform.About the AuthorsAndreas Wittig and Michael Wittig are software engineers and consultants focused on AWS and web development.Table of ContentsPART 1 GETTING STARTEDWhat is Amazon Web Services?A simple example: WordPress in five minutesPART 2 BUILDING VIRTUAL INFRASTRUCTURE WITH SERVERS AND NETWORKINGUsing virtual servers: EC2Programming your infrastructure: the command line, SDKs, and CloudFormationAutomating deployment: CloudFormation, Elastic Beanstalk, and OpsWorksSecuring your system: IAM, security groups, and VPCPART 3 STORING DATA IN THE CLOUDStoring your objects: S3 and GlacierStoring your data on hard drives: EBS and instance storeUsing a relational database service: RDSProgramming for the NoSQL database service: DynamoDBPART 4 ARCHITECTING ON AWSAchieving high availability: availability zones, auto-scaling, and CloudWatchDecoupling your infrastructure: ELB and SQSDesigning for fault-toleranceScaling up and down: auto-scaling and CloudWatch

    One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than ever before. There is no doubt that we're performing at higher scales and going faster than ever, but what if we're headed in the wrong direction?The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of platforms can in fact lead to wasted efforts, missed opportunities, and above all an inability to break out of established patterns. Edward Tenner offers a smarter way to think about efficiency, showing how we can combine artificial intelligence and our own intuition, leaving ourselves and our institutions open to learning from the random and unexpected.

    They go far beyond generic implementation guidelines, demonstrating exactly how to make lean work in real projects, environments, and companies.The Poppendiecks organize this book around the crucial concept of frames, the unspoken mental constructs that shape our perspectives and control our behavior in ways we rarely notice. For software leaders and team members, some frames lead to long-term failure, while others offer a strong foundation for success. Drawing on decades of experience, the authors present twenty-four frames that offer a coherent, complete framework for leading lean software development. You'll discover powerful new ways to act as competency leader, product champion, improvement mentor, front-line leader, and even visionary.Systems thinking: focusing on customers, bringing predictability to demand, and revamping policies that cause inefficiency Technical excellence: implementing low-dependency architectures, TDD, and evolutionary development processes, and promoting deeper developer expertise Reliable delivery: managing your biggest risks more effectively, and optimizing both workflow and schedules Relentless improvement: seeing problems, solving problems, sharing the knowledge Great people: finding and growing professionals with purpose, passion, persistence, and pride Aligned leaders: getting your entire leadership team on the same page From the world's number one experts in Lean software development, Leading Lean Software Development will be indispensable to everyone who wants to transform the promise of lean into reality--in enterprise IT and software companies alike.

    With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer's memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R's package system and debugging toolsPractice and apply R programming concepts as you learn them

    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.

    Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades. You'll find lucid, easy-to-read coverage of problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis and specific artificial-intelligence accomplishments. Related subjects are also included: predicate-calculus theorem proving, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation and much more.A supplementary section updates the original book with major research from the decade 1974-1984. Abundant illustrations, diagrams and photographs enhance the text, and challenging practice exercises at the end of each chapter test the student's grasp of each subject.The combination of introductory and advanced material makes Introduction to Artificial Intelligence ideal for both the layman and the student of mathematics and computer science. For anyone interested in the nature of thought, it will inspire visions of what computer technology might produce tomorrow.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    In Why Greatness Cannot Be Planned, Stanley and Lehman begin with a surprising scientific discovery in artificial intelligence that leads ultimately to the conclusion that the objective obsession has gone too far. They make the case that great achievement can't be bottled up into mechanical metrics; that innovation is not driven by narrowly focused heroic effort; and that we would be wiser (and the outcomes better) if instead we whole-heartedly embraced serendipitous discovery and playful creativity.Controversial at its heart, yet refreshingly provocative, this book challenges readers to consider life without a destination and discovery without a compass.

    Now they’ve written a guide to help readers make the most of our collective future. Machine | Platform | Crowd outlines the opportunities and challenges inherent in the science fiction technologies that have come to life in recent years, like self-driving cars and 3D printers, online platforms for renting outfits and scheduling workouts, or crowd-sourced medical research and financial instruments.

    It also touches upon the fundamentals of 32 bit, and 64 bit advanced processors. The book throughout provides the most popular programming tool - the assembly language codes to enhance the knowledge of programming the processors.Clear and concise in its treatment of topics, the contents of the book is supported by learning tools such as review questions, application examples (case studies) and design-based exercises.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.