Design interactive graphics and visuals for your data-driven applications using the popular open-source Chart.js data visualization library. Key Features * Harness the power of JavaScript, HTML, and CSS to create interactive visualizations * Display quantitative information efficiently in the form of attractive charts by using Chart.js * A practical guide for creating data-driven applications using open-source JavaScript library Book Description Chart.js is a free, open-source data visualization library, maintained by an active community of developers in GitHub, where it rates as the second most popular data visualization library. If you want to quickly create responsive Web-based data visualizations for the Web, Chart.js is a great choice. This book guides the reader through dozens of practical examples, complete with code you can run and modify as you wish. It is a practical hands-on introduction to Chart.js. If you have basic knowledge of HTML, CSS and JavaScript you can learn to create beautiful interactive Web Canvas-based visualizations for your data using Chart.js. This book will help you set up Chart.js in a Web page and show how to create each one of the eight Chart.js chart types. You will also learn how to configure most properties that override Chart’s default styles and behaviors. Practical applications of Chart.js are exemplified using real data files obtained from public data portals. You will learn how to load, parse, filter and select the data you wish to display from those files. You will also learn how to create visualizations that reveal patterns in the data. This book is based on Chart.js version 2.7.3 and ES2015 JavaScript. By the end of the book, you will be able to create beautiful, efficient and interactive data visualizations for the Web using Chart.js. What you will learn * Learn how to create interactive and responsive data visualizations using Chart.js * Learn how to create Canvas-based graphics without Canvas programming * Create composite charts and configure animated data updates and transitions * Efficiently display quantitative information using bar and line charts, scatterplots, and pie charts * Learn how to load, parse, and filter external files in JSON and CSV formats * Understand the benefits of using a data visualization framework Who this book is for The ideal target audience of this book includes web developers and designers, data journalists, data scientists and artists who wish to create interactive data visualizations for the Web. Basic knowledge of HTML, CSS, and JavaScript is required. No Canvas knowledge is necessary.

Enhance your development skills with Java’s state-of-the-art features and projects to make your applications leaner and faster Key Features * Overcome the challenges involved in migrating to new versions of Java * Discover how Oracle has bridged the gap between Java and native code * Make the best use of new Java features and libraries in your applications Book Description With its new six-monthly release cadence, Java is moving forward faster. In addition to planned version releases, a lot of work is currently being undertaken on various Java projects at Oracle. In order to make best use of the new features in their applications and libraries, you must be well-versed with the most recent advancements. Java 11 and 12 – New Features will take you through the latest developments in Java, right from variable type inference and simplified multithreading through to performance improvements, which are covered in depth to help you make your applications more efficient. This book explains the relevance and applicability of Java's new features, and answers your questions on whether to invest in migrating to new Java versions and when to migrate. You'll also get to grips with platform features, such as AppCDS and new garbage collectors, to tune and optimize your application—from reduced launch time and latency to improved performance and throughput. By the end of this book, you will be equipped with a thorough understanding of the new features of Java 11, 12, and Project Amber, and possess the skills to apply them with a view to improving your application's performance. What you will learn * Study type interference and how to work with the var type * Understand Class-Data Sharing, its benefits, and limitations * Discover platform options to reduce your application’s launch time * Improve application performance by switching garbage collectors * Get up to date with the new Java release cadence * Define and assess decision criteria for migrating to a new version of Java Who this book is for If you’re an executive or solutions architect responsible for technology selection or Java migration decisions, this Java book is for you. You’ll also benefit from this book if you’re a computer science enthusiast curious to learn about the latest and upcoming Java features. This book will help you migrate your solutions from Java 8 or older to the latest Java release.

Understanding the concept Dark Web and Dark Net to utilize it for effective cybersecurity Key Features *Understand the concept of Dark Net and Deep Web *Use Tor to extract data and maintain anonymity *Develop a security framework using Deep web evidences Book Description The overall world wide web is divided into three main areas - the Surface Web, the Deep Web, and the Dark Web. The Deep Web and Dark Web are the two areas which are not accessible through standard search engines or browsers. It becomes extremely important for security professionals to have control over these areas to analyze the security of your organization. This book will initially introduce you to the concept of the Deep Web and the Dark Web and their significance in the security sector. Then we will deep dive into installing operating systems and Tor Browser for privacy, security and anonymity while accessing them. During the course of the book, we will also share some best practices which will be useful in using the tools for best effect. By the end of this book, you will have hands-on experience working with the Deep Web and the Dark Web for security analysis What you will learn *Access the Deep Web and the Dark Web *Learn to search and find information in the Dark Web *Protect yourself while browsing the Dark Web *Understand what the Deep Web and Dark Web are *Learn what information you can gather, and how Who this book is for This book is targeted towards security professionals, security analyst, or any stakeholder interested in learning the concept of deep web and dark net. No prior knowledge on Deep Web and Dark Net is required

Learn to work with XRP and build applications on Ripple's blockchain Key Features *Learn to use Ripple’s decentralized system for transfering digital assets globally *A simpilfied and shortened learning curve to understand the Ripple innovation and Blockchain *Takes a hands-on approach to work with XRP – Ripple’s native currency Book Description This book starts by giving you an understanding of the basics of blockchain and the Ripple protocol. You will then get some hands-on experience of working with XRP. You will learn how to set up a Ripple wallet and see how seamlessly you can transfer money abroad. You will learn about different types of wallets through which you can store and transact XRP, along with the security precautions you need to take to keep your money safe. Since Ripple is currency agnostic, it can enable the transfer of value in USD, EUR, and any other currency. You can even transfer digital assets using Ripple. You will see how you can pay an international merchant with their own native currency and how Ripple can exchange it on the ?y. Once you understand the applications of Ripple, you will learn how to create a conditionally-held escrow using the Ripple API, and how to send and cash checks. Finally, you will also understand the common misconceptions people have about Ripple and discover the potential risks you must consider before making investment decisions. By the end of this book, you will have a solid foundation for working with Ripple's blockchain. Using it, you will be able to solve problems caused by traditional systems in your respective industry. What you will learn *Understand the fundamentals of blockchain and Ripple *Learn how to choose a Ripple wallet *Set up a Ripple wallet to send and receive XRP *Learn how to protect your XRP *Understand the applications of Ripple *Learn how to work with the Ripple API *Learn how to build applications on check and escrow features of Ripple Who this book is for This book is for anyone interested in getting their hands on Ripple technology and learn where it can be used to gain competitive advantages in their respective fields. For most parts of the book, you need not have any pre-requisite knowledge. However, you need to have basic background of JavaScript to write an escrow.

Learn best practices for testing with Jira and model industry workflows that can be used during the software development lifecycle Key Features * Integrate Jira with test management tools such as Zephyr, Test Management, and SynapseRT * Understand test case management, traceability, and test execution with reports * Implement continuous integration using Jira, Jenkins, and automated testing tools Book Description Hands-On Test Management with Jira begins by introducing you to the basic concepts of Jira and takes you through real-world software testing processes followed by various organizations. As you progress through the chapters, the book explores and compares the three most popular Jira plugins—Zephyr, Test Management, and synapseRT. With this book, you’ll gain a practical understanding of test management processes using Jira. You’ll learn how to create and manage projects, create Jira tickets to manage customer requirements, and track Jira tickets. You’ll also understand how to develop test plans, test cases, and test suites, and create defects and requirement traceability matrices, as well as generating reports in Jira. Toward the end, you’ll understand how Jira can help the SQA teams to use the DevOps pipeline for automating execution and managing test cases. You’ll get to grips with configuring Jira with Jenkins to execute automated test cases in Selenium. By the end of this book, you’ll have gained a clear understanding of how to model and implement test management processes using Jira. What you will learn * Understand QMS to effectively implement quality systems in your organization * Explore a business-driven structured approach to Test Management using TMap NEXT * Implement different aspects of test planning, test strategy, and test execution * Organize and manage Agile projects in Scrum and Kanban * Uncover Jira plugins available in the Atlassian Marketplace for testing and project management * Configure a DevOps pipeline for continuous integration using Jira with Jenkins Who this book is for If you’re a quality assurance professional, software project manager, or test manager interested in learning test management best practices in your team or organization, this book is for you. Prior knowledge of test management and Jenkins will be beneficial in understanding the concepts covered in this book.

Begin writing graphical user interface(GUI) applications for building human machine interfaces with a clear understanding of key concepts of the Qt framework Key Features * Learn how to write, assemble, and build Qt application from the command line * Understand key concepts like Signals and Slots in Qt * Best practices and effective techniques for designing graphical user interfaces using Qt 5 Book Description Qt is a cross-platform application framework and widget toolkit that is used to create GUI applications that can run on different hardware and operating systems. The main aim of this book is to introduce Qt to the reader. Through the use of simple examples, we will walk you through building blocks without focusing too much on theory. Qt is a popular tool that can be used for building a variety of applications, such as web browsers, media players such as VLC, and Adobe Photoshop. Following Qt installation and setup, the book dives straight into helping you create your first application. You will be introduced to Widgets, Qt's interface building block, and the many varieties that are available for creating GUIs. Next, Qt's core concept of signals and slots are well illustrated with sufficient examples. The book further teaches you how to create custom widgets, signals and slots, and how to communicate useful information via dialog boxes. To cap everything off, you will be taken through writing applications that can connect to databases in order to persist data. By the end of the book, you should be well equipped to start creating your own Qt applications and confident enough to pick up more advanced Qt techniques and materials to hone your skills. What you will learn * Set up and configure your machine to begin developing Qt applications * Discover different widgets and layouts for constructing UIs * Understand the key concept of signals and slots * Understand how signals and slots help animate a GUI * Explore how to create customized widgets along with signals and slots * Understand how to subclass and create a custom windows application * Understand how to write applications that can talk to databases. Who this book is for Anyone trying to start development of graphical user interface application will find this book useful. One does not need prior exposure to other toolkits to understand this book. In order to learn from this book you should have basic knowledge of C++ and a good grasp of Object Oriented Programming. Familiarity with GNU/Linux will be very useful though it's not a mandatory skill.

Write high-level abstractions while retaining full control of the hardware, performances, and maintainability. Key Features * Transform your ideas into modern C++ code, with both C++11 and C++17 * Explore best practices for creating high-performance solutions * Understand C++ basics and work with concrete real-world examples Book Description C++ Fundamentals begins by introducing you to the C++ syntax. You will study the semantics of variables along with their advantages and trade-offs, and see how they can be best used to write safe and efficient code. With the help of this book, you’ll be able to compile fully working C++ programs and understand how variables, references, and pointers can be used to manipulate the state of the program. Then you'll explore functions and classes — the features that C++ offers to organize a program — and use them to solve more complex problems such as functions and classes. You’ll also understand common pitfalls and modern best practices, especially the ones that diverge from the C++98 guideline. As you advance through the chapters, you’ll study the advantages of generic programming and write your own templates to make generic algorithms that work with any type. This C++ book will guide you in fully exploiting standard containers and understanding how to pick the appropriate container for each problem. You will even work with a variety of memory management tools in C++. By the end of this book, you will not only be able to write efficient code, but also be equipped to improve the readability, performance, and maintainability of your programs using standard algorithms. What you will learn * Work with the C++ compilation model and syntaxes * Apply best practices for writing functions and classes * Write safe, generic, and efficient code with templates * Explore the containers that C++ standard offers * Discover the new paradigms introduced with C++11, C++14, and C++17 * Get to grips with the core language features of C++ * Abstract complex problems using object-oriented programming in C++ Who this book is for If you’re a developer looking to learn a new powerful language or are familiar with C++ but want to update your knowledge with modern paradigms of C++11, C++14, and C++17, this book is for you. To easily understand the concepts in the book, you must be familiar with the basics of programming.

Enhance your career options with this well-crafted object-oriented programming language that enjoys the support of an enormous ecosystem of tools and libraries Key Features * Get introduced to Java, its features, and its ecosystem * Understand how Java uses object-oriented programming * Become an expert Java exception handler Book Description Since its inception, Java has stormed the programming world. Its features and functionalities provide developers with the tools needed to write robust cross-platform applications. Java Fundamentals introduces you to these tools and functionalities that will enable you to create Java programs. The book begins with an introduction to the language, its philosophy, and evolution over time, until the latest release. You'll learn how the javac/java tools work and what Java packages are - the way a Java program is usually organized. Once you are comfortable with this, you'll be introduced to advanced concepts of the language, such as control flow keywords. You'll explore object-oriented programming and the part it plays in making Java what it is. In the concluding chapters, you'll get to grips with classes, typecasting, and interfaces, and understand the use of data structures, arrays, strings, handling exceptions, and creating generics. By the end of this book, you will have learned to write programs, automate tasks, and follow advanced courses on algorithms and data structures or explore more advanced Java courses. What you will learn * Create and run Java programs * Use data types, data structures, and control flow in your code * Implement best practices while creating objects * Work with constructors and inheritance * Understand advanced data structures to organize and store data * Employ generics for stronger check-types during compilation * Learn to handle exceptions in your code Who this book is for Java Fundamentals is designed for tech enthusiasts who are familiar with some programming languages and want a quick introduction to the most important principles of Java.

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key Features * Understand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment * Train different types of neural networks using Cognitive Toolkit and deploy it to production * Evaluate the performance of your models and improve your deep learning skills Book Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learn * Set up your deep learning environment for the Cognitive Toolkit on Windows and Linux * Pre-process and feed your data into neural networks * Use neural networks to make effcient predictions and recommendations * Train and deploy effcient neural networks such as CNN and RNN * Detect problems in your neural network using TensorBoard * Integrate Cognitive Toolkit with Azure ML Services for effective deep learning Who this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key Features * Use R 3.5 to implement real-world examples in machine learning * Implement key machine learning algorithms to understand the working mechanism of smart models * Create end-to-end machine learning pipelines using modern libraries from the R ecosystem Book Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learn * Introduce yourself to the basics of machine learning with R 3.5 * Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results * Learn to build predictive models with the help of various machine learning techniques * Use R to visualize data spread across multiple dimensions and extract useful features * Use interactive data analysis with R to get insights into data * Implement supervised and unsupervised learning, and NLP using R libraries Who this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.

Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key Features * Manipulate location-based data and create intelligent geospatial data models * Build effective location recommendation systems used by popular companies such as Uber * A hands-on guide to help you consume spatial data and parallelize GIS operations effectively Book Description Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learn * Learn how companies now use location data * Set up your Python environment and install Python geospatial packages * Visualize spatial data as graphs * Extract geometry from spatial data * Perform spatial regression from scratch * Build web applications which dynamically references geospatial data Who this book is for Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.

A complete guide to understanding, developing, and testing popular security-token smart contracts Key Features * Understand key Blockchain and Ethereum platforms concepts * Step-by-step guide to developing STO smart contracts on Ethereum * Monetize digital tokens under various U.S. securities laws Book Description The failure of initial coin offerings (ICOs) is no accident, as most ICOs do not link to a real asset and are not regulated. Realizing the shortcomings of ICOs, the blockchain community and potential investors embraced security token offerings (STOs) and stablecoins enthusiastically. In this book, we start with an overview of the blockchain technology along with its basic concepts. We introduce the concept behind STO, and cover the basic requirements for launching a STO and the relevant regulations governing its issuance. We discuss U.S. securities laws development in launching security digital tokens using blockchain technology and show some real use cases. We also explore the process of STO launches and legal considerations. We introduce popular security tokens in the current blockchain space and talk about how to develop a security token DApp, including smart contract development for ERC1404 tokens. Later, you'll learn to build frontend side functionalities to interact with smart contracts. Finally, we discuss stablecoin technical design functionalities for issuing and operating STO tokens by interacting with Ethereum smart contracts. By the end of this book, you will have learned more about STOs and gained a detailed knowledge of building relevant applications—all with the help of practical examples. What you will learn * Understand the basic requirements for launching a security token offering * Explore various US securities laws governing the offering of security digital tokens * Get to grips with the stablecoin concept with the help of use cases * Learn how to develop security token decentralized applications * Understand the difference between ERC-20 and ERC-721 tokens * Learn how to set up a development environment and build security tokens * Explore the technical design of stablecoins Who this book is for This book is ideal for blockchain beginners and business user developers who want to quickly master popular Security Token Offerings and stablecoins. Readers will learn how to develop blockchain/digital cryptos, guided by U.S. securities laws and utilizing some real use cases. Prior exposure to an Object-Oriented Programming language such as JavaScript would be an advantage, but is not mandatory.

Build Vue apps the right way using Vue CLI 3. Understand how the building blocks of Vue CLI 3 work including npm, webpack, babel, eslint, plugins, GUI, testing, and SCSS. Import third-party libraries and maintain your project. Key Features * Learn to work with Vue CLI 3 both on the command line and with a GUI * Manage VueJS apps, settings, Vue plugins, and third-party libraries * Learn how to build Vue apps from scratch using webpack, babel, ES6, vue-router, Jest, Cypress, SCSS, and Git Book Description The sprawling landscape of various tools in JavaScript web development is becoming overwhelming. This book will show you how Vue CLI 3 can help you take back control of the tool chain. To that end, we'll begin by configuring webpack, utilizing HMR, and using single-file .vue components. We'll also use SCSS, ECMAScript, and TypeScript. We'll unit test with Jest and perform E2E testing with Cypress. This book will show you how to configure Vue CLI as your default way of building Vue projects. You'll discover the reasons behind using webpack, babel, eslint, and other modern JavaScript toolchain technologies. You'll learn about the inner workings of each through the lens of Vue CLI 3. We'll explore the extendibility of Vue CLI with the built-in settings, and various core and third-party plugins. Vue CLI helps you work with Vue components, routers, directives, and services in the Vue ecosystem. While learning these concepts, you'll examine the evolution of JavaScript. You'll learn about use of npm, IIFEs, modules in JavaScript, Common.js modules, task runners, npm scripts, module bundlers, and webpack. You'll get familiar with the reasons why Vue CLI 3 is set up the way it is. You'll also learn to perform linting with ESLint and Prettier. Towards the end, we'll introduce you to working with styles and SCSS. Finally, we'll show you how to deploy your very own Vue project on Github Pages. What you will learn * Work with nvm, install Node.js and npm, use Vue CLI 3 with no configuration, via the command line and the graphical user interface * Build a Vue project from scratch using npm and webpack, and learn about hot module replacement * Work with Babel settings, configurations, and presets * Work with Vue plugins, including testing plugins such as Jest and Cypress * Write, run, and watch unit and E2E tests using TDD assertions in the red-green-refactor cycle * Work with Vue router and use, nested, lazy-loading, and dynamic routes * Add SCSS to your projects and work with third-party Vue plugins * Deploy your Vue apps to Github Pages Who this book is for This book is for existing web developers and developers who are new to web development. You must be familiar with HTML, CSS, and JavaScript programming. Basic knowledge of the command line will be helpful but is not necessary.

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. Key Features * Construct and deploy machine learning systems that learn from your data and give accurate predictions * Unleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala. * Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j library Book Description Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Na?ve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala. What you will learn * Get acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4j * Learn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured data * Understand supervised and unsupervised learning techniques with best practices and pitfalls * Learn classification and regression analysis with linear regression, logistic regression, Na?ve Bayes, support vector machine, and tree-based ensemble techniques * Learn effective ways of clustering analysis with dimensionality reduction techniques * Learn recommender systems with collaborative filtering approach * Delve into deep learning and neural network architectures Who this book is for This book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key Features * Speed up your data analysis projects using powerful R packages and techniques * Create multiple hands-on data analysis projects using real-world data * Discover and practice graphical exploratory analysis techniques across domains Book Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learn * Learn powerful R techniques to speed up your data analysis projects * Import, clean, and explore data using powerful R packages * Practice graphical exploratory analysis techniques * Create informative data analysis reports using ggplot2 * Identify and clean missing and erroneous data * Explore data analysis techniques to analyze multi-factor datasets Who this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Explore the different data mining techniques using the libraries and packages offered by Python Key Features * Grasp the basics of data loading, cleaning, analysis, and visualization * Use the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data mining * Your one-stop guide to build efficient data mining pipelines without going into too much theory Book Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learn * Explore the methods for summarizing datasets and visualizing/plotting data * Collect and format data for analytical work * Assign data points into groups and visualize clustering patterns * Learn how to predict continuous and categorical outputs for data * Clean, filter noise from, and reduce the dimensions of data * Serialize a data processing model using scikit-learn’s pipeline feature * Deploy the data processing model using Python’s pickle module Who this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.

Manipulate and analyze network data with the power of Python and NetworkX Key Features * Understand the terminology and basic concepts of network science * Leverage the power of Python and NetworkX to represent data as a network * Apply common techniques for working with network data of varying sizes Book Description NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems. What you will learn * Use Python and NetworkX to analyze the properties of individuals and relationships * Encode data in network nodes and edges using NetworkX * Manipulate, store, and summarize data in network nodes and edges * Visualize a network using circular, directed and shell layouts * Find out how simulating behavior on networks can give insights into real-world problems * Understand the ongoing impact of network science on society, and its ethical considerations Who this book is for If you are a programmer or data scientist who wants to manipulate and analyze network data in Python, this book is perfect for you. Although prior knowledge of network science is not necessary, some Python programming experience will help you understand the concepts covered in the book easily.

Concepts, tools, and techniques to explore deep learning architectures and methodologies Key Features * Explore advanced deep learning architectures using various datasets and frameworks * Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more * Discover design patterns and different challenges for various deep learning architectures Book Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learn * Implement CNNs, RNNs, and other commonly used architectures with Python * Explore architectures such as VGGNet, AlexNet, and GoogLeNet * Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more * Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples * Master artificial intelligence and neural network concepts and apply them to your architecture * Understand deep learning architectures for mobile and embedded systems Who this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

Understand the nitty-gritty of Cybersecurity with ease Key Features * Align your security knowledge with industry leading concepts and tools * Acquire required skills and certifications to survive the ever changing market needs * Learn from industry experts to analyse, implement, and maintain a robust environment Book Description It's not a secret that there is a huge talent gap in the cybersecurity industry. Everyone is talking about it including the prestigious Forbes Magazine, Tech Republic, CSO Online, DarkReading, and SC Magazine, among many others. Additionally, Fortune CEO's like Satya Nadella, McAfee's CEO Chris Young, Cisco's CIO Colin Seward along with organizations like ISSA, research firms like Gartner too shine light on it from time to time. This book put together all the possible information with regards to cybersecurity, why you should choose it, the need for cyber security and how can you be part of it and fill the cybersecurity talent gap bit by bit. Starting with the essential understanding of security and its needs, we will move to security domain changes and how artificial intelligence and machine learning are helping to secure systems. Later, this book will walk you through all the skills and tools that everyone who wants to work as security personal need to be aware of. Then, this book will teach readers how to think like an attacker and explore some advanced security methodologies. Lastly, this book will deep dive into how to build practice labs, explore real-world use cases and get acquainted with various cybersecurity certifications. By the end of this book, readers will be well-versed with the security domain and will be capable of making the right choices in the cybersecurity field. What you will learn * Get an overview of what cybersecurity is and learn about the various faces of cybersecurity as well as identify domain that suits you best * Plan your transition into cybersecurity in an efficient and effective way * Learn how to build upon your existing skills and experience in order to prepare for your career in cybersecurity Who this book is for This book is targeted to any IT professional who is looking to venture in to the world cyber attacks and threats. Anyone with some understanding or IT infrastructure workflow will benefit from this book. Cybersecurity experts interested in enhancing their skill set will also find this book useful.

Build state-of-the-art web applications quickly and efficiently using Flask and related technologies with Python 3 Key Features * Updated to Flask 1.0.3 and Python 3.7 with coverage of Microservices * Get the most out of the powerful Flask framework and maintain the flexibility of your design choices * Write cleaner and maintainable code with the help of sample apps Book Description Flask, the lightweight Python web framework, is popular due to its powerful modular design that lets you build scalable web apps. With this recipe-based guide, you’ll explore modern solutions and best practices for Flask web development. Updated to the latest version of Flask and Python 3, this second edition of Flask Framework Cookbook moves away from some of the old and obsolete libraries and introduces recipes on bleeding edge technologies. You’ll discover different ways of using Flask to create, deploy, and manage microservices. This Flask Python book starts by covering the different configurations that a Flask application can make use of, and then helps you work with templates and learn about the ORM and view layers. You’ll also be able to write an admin interface and get to grips with debugging and logging errors. Finally, you’ll grasp a variety of deployment and post-deployment techniques for platforms such as Apache, Tornado, and Heroku. By the end of this book, you’ll have gained all the knowledge you need to write Flask applications in the best possible way and scale them using standard industry practices. What you will learn * Explore web application development in Flask, right from installation to post-deployment stages * Make use of advanced templating and data modeling techniques * Discover effective debugging, logging, and error handling techniques in Flask * Integrate Flask with different technologies such as Redis, Sentry, and MongoDB * Deploy and package Flask applications with Docker and Kubernetes * Design scalable microservice architecture using AWS LambdaContinuous integration and Continuous deployment Who this book is for If you are a web developer who wants to learn more about developing scalable and production-ready applications in Flask, this is the book for you. You’ll also find this book useful if you are already aware of Flask's major extensions and want to use them for better application development. Basic Python programming experience along with basic understanding of Flask is assumed.

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python. Key Features * Get a hands-on, fast-paced introduction to the Python data science stack * Explore ways to create useful metrics and statistics from large datasets * Create detailed analysis reports with real-world data Book Description Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. What you will learn * Use Python to read and transform data into different formats * Generate basic statistics and metrics using data on disk * Work with computing tasks distributed over a cluster * Convert data from various sources into storage or querying formats * Prepare data for statistical analysis, visualization, and machine learning * Present data in the form of effective visuals Who this book is for Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.