Mastering Python Data Visualization
Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will lea ...
Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will lea ...
Interactive Data Visualization for the Web, 2nd Edition
Create and publish your own interactive data visualization projects on the web—even if you have little or no experience with data visualization or web development. It's inspiring and fun with this friendly, accessible, and practical hands-on introduction. This fully updated and expanded second edition takes you through the fundamental concepts and methods of D3, the most powerful JavaScript library for expressing data visually in a web browser. Ideal for designers with no coding experience, reporters exploring data journalism, and anyone who wants to visualize and share data, this step-by-step guide will also help you expand your web programming skills by teaching you the basics of HTML, CSS, JavaScript, and SVG. Learn D3 4.x—the latest D3 version—with downloadable code and over 140 examples; Create bar charts, scatter plots, pie charts, stacked bar charts, and force-directed graphs; Use smooth, animated transitions to show changes in your data; Introduce interactivity to h ...
Create and publish your own interactive data visualization projects on the web—even if you have little or no experience with data visualization or web development. It's inspiring and fun with this friendly, accessible, and practical hands-on introduction. This fully updated and expanded second edition takes you through the fundamental concepts and methods of D3, the most powerful JavaScript library for expressing data visually in a web browser. Ideal for designers with no coding experience, reporters exploring data journalism, and anyone who wants to visualize and share data, this step-by-step guide will also help you expand your web programming skills by teaching you the basics of HTML, CSS, JavaScript, and SVG. Learn D3 4.x—the latest D3 version—with downloadable code and over 140 examples; Create bar charts, scatter plots, pie charts, stacked bar charts, and force-directed graphs; Use smooth, animated transitions to show changes in your data; Introduce interactivity to h ...
Data Science with Java
Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form;Understand the matrix structure that your data should take;Learn basic concepts for testing the origin and validity of data;Transform your data into stable and usable numerical val ...
Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form;Understand the matrix structure that your data should take;Learn basic concepts for testing the origin and validity of data;Transform your data into stable and usable numerical val ...
Learn Computer Science with Swift
Master the basics of solving logic puzzles, and creating algorithms using Swift on Apple platforms. This book is based on the curriculum currently being used in common computer classes. You'll learn to automate algorithmic processes that scale using Swift in the context of iOS, macOS, tvOS, and watchOS. Begin by understanding how to think computationally: to formulate a computational problem and recognize patterns and ways to validate it. Then jump ahead past the abstractions and conceptual work into using code snippets to build frameworks and write code using Xcode and Swift. Once you have frameworks in place, you'll learn to use algorithms and structure data. Finally, you'll see how to bring people into what you've built through a useable UI and how UI and code relate. ...
Master the basics of solving logic puzzles, and creating algorithms using Swift on Apple platforms. This book is based on the curriculum currently being used in common computer classes. You'll learn to automate algorithmic processes that scale using Swift in the context of iOS, macOS, tvOS, and watchOS. Begin by understanding how to think computationally: to formulate a computational problem and recognize patterns and ways to validate it. Then jump ahead past the abstractions and conceptual work into using code snippets to build frameworks and write code using Xcode and Swift. Once you have frameworks in place, you'll learn to use algorithms and structure data. Finally, you'll see how to bring people into what you've built through a useable UI and how UI and code relate. ...
Text Analytics with Python
Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure; Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summa ...
Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure; Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summa ...
Mastering Machine Learning with Python in Six Steps
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining / analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. All the code presented in the book will be available in the form of iPython ...
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining / analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. All the code presented in the book will be available in the form of iPython ...
Kafka: The Definitive Guide
Every enterprise application creates data, whether it's log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you're an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem;Explore Kafka producers and consumers for wri ...
Every enterprise application creates data, whether it's log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you're an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem;Explore Kafka producers and consumers for wri ...
Mastering Azure Analytics
Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution. You'll not only be able to determine which service best fits the job, but also learn how to implement a complete solution that scales, provides human fault tolerance, and supports future needs.Understand the fundamental patterns of the data lake and lambda architecture;Recognize the canonical steps in the analytics data pipeline and learn how to use Azure Data Factory to orchestrate them;Implement data lakes and lambda architectures, using Azure Data Lake Store, Data Lake Analytics, HDInsight (including Spark), Stream Analytics, SQL Data Warehouse, and Event Hubs;Understand where Azure Machine Learning fits i ...
Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution. You'll not only be able to determine which service best fits the job, but also learn how to implement a complete solution that scales, provides human fault tolerance, and supports future needs.Understand the fundamental patterns of the data lake and lambda architecture;Recognize the canonical steps in the analytics data pipeline and learn how to use Azure Data Factory to orchestrate them;Implement data lakes and lambda architectures, using Azure Data Lake Store, Data Lake Analytics, HDInsight (including Spark), Stream Analytics, SQL Data Warehouse, and Event Hubs;Understand where Azure Machine Learning fits i ...
Making Data Visual
You have a mound of data front of you and a suite of computation tools at your disposal. Which parts of the data actually matter? Where is the insight hiding? If you're a data scientist trying to navigate the murky space between data and insight, this practical book shows you how to make sense of your data through high-level questions, well-defined data analysis tasks, and visualizations to clarify understanding and gain insights along the way. When incorporated into the process early and often, iterative visualization can help you refine the questions you ask of your data. Authors Danyel Fisher and Miriah Meyer provide detailed case studies that demonstrate how this process can evolve in the real world. You'll learn:The data counseling process for moving from general to more precise questions about your data, and arriving at a working visualization;The role that visual representations play in data discovery;Common visualization types by the tasks they f ...
You have a mound of data front of you and a suite of computation tools at your disposal. Which parts of the data actually matter? Where is the insight hiding? If you're a data scientist trying to navigate the murky space between data and insight, this practical book shows you how to make sense of your data through high-level questions, well-defined data analysis tasks, and visualizations to clarify understanding and gain insights along the way. When incorporated into the process early and often, iterative visualization can help you refine the questions you ask of your data. Authors Danyel Fisher and Miriah Meyer provide detailed case studies that demonstrate how this process can evolve in the real world. You'll learn:The data counseling process for moving from general to more precise questions about your data, and arriving at a working visualization;The role that visual representations play in data discovery;Common visualization types by the tasks they f ...
Identity and Data Security for Web Development
Developers, designers, engineers, and creators can no longer afford to pass responsibility for identity and data security onto others. Web developers who don't understand how to obscure data in transmission, for instance, can open security flaws on a site without realizing it. With this practical guide, you'll learn how and why everyone working on a system needs to ensure that users and data are protected. Authors Jonathan LeBlanc and Tim Messerschmidt provide a deep dive into the concepts, technology, and programming methodologies necessary to build a secure interface for data and identity - without compromising usability. You'll learn how to plug holes in existing systems, protect against viable attack vectors, and work in environments that sometimes are naturally insecure.Understand the state of web and application security today;Design security password encryption, and combat password attack vectors;Create digital fingerprints to identify users through ...
Developers, designers, engineers, and creators can no longer afford to pass responsibility for identity and data security onto others. Web developers who don't understand how to obscure data in transmission, for instance, can open security flaws on a site without realizing it. With this practical guide, you'll learn how and why everyone working on a system needs to ensure that users and data are protected. Authors Jonathan LeBlanc and Tim Messerschmidt provide a deep dive into the concepts, technology, and programming methodologies necessary to build a secure interface for data and identity - without compromising usability. You'll learn how to plug holes in existing systems, protect against viable attack vectors, and work in environments that sometimes are naturally insecure.Understand the state of web and application security today;Design security password encryption, and combat password attack vectors;Create digital fingerprints to identify users through ...