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In 25 concise steps, you will learn the basics of blockchain technology.No mathematical formulas, program code, or computer science jargon are used.No previous knowledge in computer science, mathematics, programming, or cryptography is required.Terminology is explained through pictures, analogies, and metaphors.

The topic of NoSQL databases has recently emerged, to face the Big Data challenge, namely the ever increasing volume of data to be handled.It is now recognized that relational databases are not appropriate in this context, implying that new database models and techniques are needed.

The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process.

Ethereum is a public, blockchain-based distributed computing platform featuring smart contract functionality. This book is your one-stop guide to blockchain and Ethereum smart contract development. We start by introducing you to the basics of blockchain. You'll learn about hash functions, Merkle trees, forking, mining, and much more. Then you'll learn about Ethereum and smart contracts, and we'll cover Ethereum virtual machine (EVM) in detail. Next, you'll get acquainted with DApps and DAOs and see how they work. We'll also delve into the mechanisms of advanced smart contracts, taking a practical approach.You'll also learn how to develop your own cryptocurrency from scratch in order to understand the business behind ICO. Further on, you'll get to know the key concepts of the Solidity programming language, enabling you to build decentralized blockchain-based applications. We'll also look at enterprise use cases, where you'll build a decentralized microblogging site.

Build web applications with MongoDB, ExpressJS, React, and NodeAbout This Book• Build applications with the MERN stack• Work with each component of the MERN stack• Become confident with MERN and ready for more!Who This Book Is ForThe book is for JavaScript developers who want to get stated with the MERN Stack.What You Will Learn• Get started with the MERN stack• Install Node.js and configure MongoDB• Build RESTful APIs with Express.js and Mongoose• Build real-time applications with Socket.IO • Manage synchronous and asynchronous data flows with Redux• Build web applications with React In DetailThe MERN stack is a collection of great tools—MongoDB, Express.js, React, and Node—that provide a strong base for a developer to build easily maintainable web applications. With each of them a JavaScript or JavaScript-based technology, having a shared programming language means it takes less time to develop web applications. This book focuses on providing key tasks that can help you get started, learn, understand, and build full-stack web applications. It walks you through the process of installing all the requirements and project setup to build client-side React web applications, managing synchronous and asynchronous data flows with Redux, and building real-time web applications with Socket.IO, RESTful APIs, and other concepts. This book gives you practical and clear hands-on experience so you can begin building a full-stack MERN web application.Quick Start Guides are focused, shorter titles that provide a faster paced introduction to a technology. They are for people who don't need all the detail at this point in their learning curve. The presentation has been streamlined to concentrate on the things you really need to know.Style and approachThis guide shows you how to use your JavaScript knowledge to build web applications that use the MERN stack in both client-side and in server-side environments.

This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value.The volume consists of twenty-five chapters organized into four main parts.

Cloud computing is an indispensable part of the modern Information and Communication Technology (ICT) systems. Cloud computing services have proven to be of significant importance, and promote quickly deployable and scalable IT solutions with reduced infrastructure costs. However, utilization of cloud also raises concerns such as security, privacy, latency, governance, etc., that keep it from turning into the predominant option for critical frameworks. As such, there is an urgent need to identify these concerns and to address them. Cloud Security: Concepts, Applications and Perspectives is a comprehensive work with substantial technical details for introducing the state-of-the-art research and development on various approaches for security and privacy of cloud services, novel attacks on cloud services, cloud forensics, novel defenses for cloud service attacks, and cloud security analysis. It discusses the present techniques and methodologies, and provides a wide range of examples and illustrations to effectively show the concepts, applications and perspectives of security in cloud computing. This highly informative book will prepare readers to exercise better protection by understanding the motivation of attackers and to deal with them to mitigate the situation. In addition, it covers future research directions in the domain. This book is suitable for professionals in the field, researchers and students who are want to carry out research in the field of computer and cloud security, faculty members across universities, and software developers engaged in software development in the field.

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

Get up and running with machine learning with F# in a fun and functional way About This Book • Design algorithms in F# to tackle complex computing problems • Be a proficient F# data scientist using this simple-to-follow guide • Solve real-world, data-related problems with robust statistical models, built for a range of datasets Who This Book Is For If you are a C# or an F# developer who now wants to explore the area of machine learning, then this book is for you. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage. What You Will Learn • Use F# to find patterns through raw data • Build a set of classification systems using Accord.NET, Weka, and F# • Run machine learning jobs on the Cloud with MBrace • Perform mathematical operations on matrices and vectors using Math.NET • Use a recommender system for your own problem domain • Identify tourist spots across the globe using inputs from the user with decision tree algorithms In Detail The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs. If you want to learn how to use F# to build machine learning systems, then this is the book you want. Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data. Style and approach This book is a fast-paced tutorial guide that uses hands-on examples to explain real-world applications of machine learning. Using practical examples, the book will explore several machine learning techniques and also describe how you can use F# to build machine learning systems.

CakePHP is the most popular rapid application development framework for PHP. For over 10 years, this open source project has been the go-to solution for quickly and easily building websites and web applications. Through a series of over 60 recipes, this book takes you from the basic building blocks of the CakePHP framework to the implementation of routine tasks that will help speed up your development even further. From routing to authentication, the model layer and the events system, as well as views and unit testing, you'll learn how to handle the ins and outs of developments using CakePHP. With fast paced recipes written by a core CakePHP member and official trainer, this book is your piece of the cake!