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| data analysis with javascript: JavaScript and jQuery for Data Analysis and Visualization Jon Raasch, Graham Murray, Vadim Ogievetsky, Joseph Lowery, 2014-12-03 Go beyond design concepts—build dynamic data visualizations using JavaScript JavaScript and jQuery for Data Analysis and Visualization goes beyond design concepts to show readers how to build dynamic, best-of-breed visualizations using JavaScript—the most popular language for web programming. The authors show data analysts, developers, and web designers how they can put the power and flexibility of modern JavaScript libraries to work to analyze data and then present it using best-of-breed visualizations. They also demonstrate the use of each technique with real-world use cases, showing how to apply the appropriate JavaScript and jQuery libraries to achieve the desired visualization. All of the key techniques and tools are explained in this full-color, step-by-step guide. The companion website includes all sample codes used to generate the visualizations in the book, data sets, and links to the libraries and other resources covered. Go beyond basic design concepts and get a firm grasp of visualization approaches and techniques using JavaScript and jQuery Discover detailed, step-by-step directions for building specific types of data visualizations in this full-color guide Learn more about the core JavaScript and jQuery libraries that enable analysis and visualization Find compelling stories in complex data, and create amazing visualizations cost-effectively Let JavaScript and jQuery for Data Analysis and Visualization be the resource that guides you through the myriad strategies and solutions for combining analysis and visualization with stunning results. |
| data analysis with javascript: Building Data-Driven Applications with Danfo.js Rising Odegua, Stephen Oni, 2021-09-24 Get hands-on with building data-driven applications using Danfo.js in combination with other data analysis tools and techniques Key FeaturesBuild microservices to perform data transformation and ML model serving in JavaScriptExplore what Danfo.js is and how it helps with data analysis and data visualizationCombine Danfo.js and TensorFlow.js for machine learningBook Description Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications. Starting with an overview of modern JavaScript, you'll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You'll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you'll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you'll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js. By the end of this app development book, you'll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser. What you will learnPerform data experimentation and analysis with Danfo.js and DnotebookBuild machine learning applications using Danfo.js integrated with TensorFlow.jsConnect Danfo.js with popular database applications to aid data analysisCreate a no-code data analysis and handling system using internal librariesDevelop a recommendation system with Danfo.js and TensorFlow.jsBuild a Twitter analytics dashboard for sentiment analysis and other types of data insightsWho this book is for This book is for data analysts, data scientists, and JavaScript developers who want to create data-driven applications in the JavaScript/Node.js environment. Intermediate-level knowledge of JavaScript programming and data science using pandas is expected. |
| data analysis with javascript: Data Visualization with Python and JavaScript Kyran Dale, 2016-06-30 Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries Serve data and create RESTful web APIs with Python’s Flask framework Create engaging, interactive web visualizations with JavaScript’s D3 library |
| data analysis with javascript: JavaScript and jQuery for Data Analysis and Visualization Jon Raasch, Graham Murray, Vadim Ogievetsky, Joseph Lowery, 2014-11-14 Go beyond design concepts—build dynamic data visualizations using JavaScript JavaScript and jQuery for Data Analysis and Visualization goes beyond design concepts to show readers how to build dynamic, best-of-breed visualizations using JavaScript—the most popular language for web programming. The authors show data analysts, developers, and web designers how they can put the power and flexibility of modern JavaScript libraries to work to analyze data and then present it using best-of-breed visualizations. They also demonstrate the use of each technique with real-world use cases, showing how to apply the appropriate JavaScript and jQuery libraries to achieve the desired visualization. All of the key techniques and tools are explained in this full-color, step-by-step guide. The companion website includes all sample codes used to generate the visualizations in the book, data sets, and links to the libraries and other resources covered. Go beyond basic design concepts and get a firm grasp of visualization approaches and techniques using JavaScript and jQuery Discover detailed, step-by-step directions for building specific types of data visualizations in this full-color guide Learn more about the core JavaScript and jQuery libraries that enable analysis and visualization Find compelling stories in complex data, and create amazing visualizations cost-effectively Let JavaScript and jQuery for Data Analysis and Visualization be the resource that guides you through the myriad strategies and solutions for combining analysis and visualization with stunning results. |
| data analysis with javascript: Applied User Data Collection and Analysis Using JavaScript and PHP Kyle Goslin, Markus Hofmann, 2021-04-28 Applied User Data Collection and Analysis Using JavaScript and PHP is designed to provide the technical skills and competency to gather a wide range of user data from web applications in both active and passive methods. This is done by providing the reader with real-world examples of how a variety of different JavaScript and PHP based libraries can be used to gather data using custom feedback forms and embedded data gathering tools. Once data has been gathered, this book explores the process of working with numerical data, text analysis, visualization approaches, statistics and rolling out developed applications to both data analysists and users alike. Using the collected data, this book aims to provide a deeper understanding of user behavior and interests allowing application developers to further enhance web application development. Key Features: Complete real-world examples of gathering data from users and web environments Offers readers the fundamentals of text analysis using JavaScript and PHP Allows the user to understand and harness JavaScript data visualization tools Integration of new and existing data sources into a single bespoke web-based analysis environment Author Bio: Dr. Kyle Goslin is currently a Lecturer in Computing at the Technological University Dublin in Ireland, specializing in web application development, information retrieval, text analysis and data visualization. Kyle has taught for over 10 years at third level in Ireland, teaching a wide range of web development related subjects. During this time, he has been involved in several different web-based data driven start-up companies with the aim of reducing time to market for businesses. Kyle has contributed to several different open-source learning platforms with the aim of making education accessible to all learners by aiding both teachers and students. Kyle has developed and defended a number of different third level computing courses validated by Quality and Qualifications Ireland. He has published peer-reviewed articles relating to information retrieval, text analysis and learning environments. In his spare time, he is a technical reviewer for data and software development related books. He holds a Bachelor of Science (Honours) and Doctor of Philosophy from the Technological University Dublin, where he currently lectures and lives. For more information, visit www.kylegoslin.ie Dr. Markus Hofmann is currently Senior Lecturer at the Technological University Dublin in Ireland where he focuses on the areas of data mining, text mining, data exploration and visualization as well as business intelligence. He holds a Ph.D. from Trinity College Dublin, an MSc in Computing (Information Technology for Strategic Management) from the Dublin Institute |
| data analysis with javascript: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
| data analysis with javascript: Python Data Analytics Fabio Nelli, 2018-09-27 Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You'll LearnUnderstand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis |
| data analysis with javascript: Data Wrangling with JavaScript Ashley Davis, 2018-12-02 Summary Data Wrangling with JavaScript is hands-on guide that will teach you how to create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Why not handle your data analysis in JavaScript? Modern libraries and data handling techniques mean you can collect, clean, process, store, visualize, and present web application data while enjoying the efficiency of a single-language pipeline and data-centric web applications that stay in JavaScript end to end. About the Book Data Wrangling with JavaScript promotes JavaScript to the center of the data analysis stage! With this hands-on guide, you'll create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. You'll also build interactive visualizations and deploy your apps to production. Each valuable chapter provides a new component for your reusable data wrangling toolkit. What's inside Establishing a data pipeline Acquisition, storage, and retrieval Handling unusual data sets Cleaning and preparing raw dataInteractive visualizations with D3 About the Reader Written for intermediate JavaScript developers. No data analysis experience required. About the Author Ashley Davis is a software developer, entrepreneur, author, and the creator of Data-Forge and Data-Forge Notebook, software for data transformation, analysis, and visualization in JavaScript. Table of Contents Getting started: establishing your data pipeline Getting started with Node.js Acquisition, storage, and retrieval Working with unusual data Exploratory coding Clean and prepare Dealing with huge data files Working with a mountain of data Practical data analysis Browser-based visualization Server-side visualization Live data Advanced visualization with D3 Getting to production |
| data analysis with javascript: Data Structures and Algorithms with JavaScript Michael McMillan, 2014-03-10 As an experienced JavaScript developer moving to server-side programming, you need to implement classic data structures and algorithms associated with conventional object-oriented languages like C# and Java. This practical guide shows you how to work hands-on with a variety of storage mechanisms—including linked lists, stacks, queues, and graphs—within the constraints of the JavaScript environment. Determine which data structures and algorithms are most appropriate for the problems you’re trying to solve, and understand the tradeoffs when using them in a JavaScript program. An overview of the JavaScript features used throughout the book is also included. This book covers: Arrays and lists: the most common data structures Stacks and queues: more complex list-like data structures Linked lists: how they overcome the shortcomings of arrays Dictionaries: storing data as key-value pairs Hashing: good for quick insertion and retrieval Sets: useful for storing unique elements that appear only once Binary Trees: storing data in a hierarchical manner Graphs and graph algorithms: ideal for modeling networks Algorithms: including those that help you sort or search data Advanced algorithms: dynamic programming and greedy algorithms |
| data analysis with javascript: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data analysis with javascript: Research Basics James V. Spickard, 2016-09-15 Research Basics: Design to Data Analysis in Six Steps offers a fresh and creative approach to the research process based on author James V. Spickard’s decades of teaching experience. Using an intuitive six-step model, readers learn how to craft a research question and then identify a logical process for answering it. Conversational writing and multi-disciplinary examples illuminate the model’s simplicity and power, effectively connecting the “hows” and “whys” behind social science research. Students using this book will learn how to turn their research questions into results. |
| data analysis with javascript: Hands-on Machine Learning with JavaScript Burak Kanber, 2018-05-29 A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in machine learning Book Description In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. What you will learn Get an overview of state-of-the-art machine learning Understand the pre-processing of data handling, cleaning, and preparation Learn Mining and Pattern Extraction with JavaScript Build your own model for classification, clustering, and prediction Identify the most appropriate model for each type of problem Apply machine learning techniques to real-world applications Learn how JavaScript can be a powerful language for machine learning Who this book is for This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book. |
| data analysis with javascript: Interactive Data Visualization for the Web Scott Murray, 2013-03-15 Create and publish your own interactive data visualization projects on the Web, even if you have no experience with either web development or data visualization. It’s easy with this hands-on guide. You’ll start with an overview of data visualization concepts and simple web technologies, and then learn how to use D3, a JavaScript library that lets you express data as visual elements in a web page. Interactive Data Visualization for the Web makes these skills available at an introductory level for designers and visual artists without programming experience, journalists interested in the emerging data journalism processes, and others keenly interested in visualization and publicly available data sources. Get a practical introduction to data visualization, accessible for beginners Focus on web-based tools that help you publish your creations quickly to a wide audience Learn about interactivity so you can engage users in exploring your data |
| data analysis with javascript: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-06-23 Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover. |
| data analysis with javascript: Source Code Analytics With Roslyn and JavaScript Data Visualization Sudipta Mukherjee, 2016-12-20 Learn how to build an interactive source code analytics system using Roslyn and JavaScript. This concise 150 page book will help you create and use practical code analysis tools utilizing the new features of Microsoft’s Roslyn compiler to understand the health of your code and identify parts of the code for refactoring. Source code is one of the biggest assets of a software company. However if not maintained well, it can become a big liability. As source code becomes larger. more complex and accessed via the cloud, maintaining code quality becomes even more challenging. The author provides straightforward tools and advice on how to manage code quality in this new environment. Roslyn exposes a set of APIs which allow developers to parse their C# and VB.NET code and drastically lower the barrier to entry for Meta programming in .NET. Roslyn has a dedicated set of APIs for creating custom refactoring for integrating with Visual Studio. This title will show readers how to use Roslyn along with industry standard JavaScript visualization APIs like HighCharts, D3.js etc to create a scalable and highly responsive source code analytics system. What You Will Learn Understand the Roslyn Syntax API Use Data Visualization techniques to assist code analysis process visually Code health monitoring matrices (from the standard of Code Query Language) Code mining techniques to identify design patterns used in source code Code forensics techniques to identify probable author of a given source code Techniques to identify duplicate/near duplicate code Who This Book is For .NET Software Developers and Architects |
| data analysis with javascript: Machine Learning for Hackers Drew Conway, John Myles White, 2012-02-13 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data |
| data analysis with javascript: Murach's JavaScript and Jquery (4th Edition) Mary Delamater, Zak Ruvalcaba, 2020-09-04 If you're developing websites, you have to know JavaScript. There's no way around it today. And this latest edition of Murach's popular book teaches you how to code modern JavaScript that conforms to the ECMAScript standards, the way the pros do. At the same time, it teaches you how to use jQuery, the classic JavaScript library, to handle the DOM scripting that gives JavaScript so much of its power. And it works no matter whether you're a web designer who's coming from a background in HTML and CSS or a server-side programmer who's coded in languages like PHP, C#, Java, and Python. |
| data analysis with javascript: Nursing Research Using Data Analysis Mary De Chesnay, 2014-12-05 This is a concise, step-by-step guide to conducting qualitative nursing research using various forms of data analysis. It is part of a unique series of books devoted to seven different qualitative designs and methods in nursing, written for both novice researchers and specialists seeking to develop or expand their competency. This practical resource encompasses such methodologies as content analysis, a means of organizing and interpreting data to elicit themes and concepts; discourse analysis, used to analyze language to understand social or historical context; narrative analysis, in which the researcher seeks to understand human experience through participant stories; and focus groups and case studies, used to understand the consensus of a group or the experience of an individual and his or her reaction to a difficult situation such as disease or trauma. Written by a noted qualitative research scholar and contributing experts, the book describes the philosophical basis for conducting research using data analysis and delivers an in-depth plan for applying its methodologies to a particular study, including appropriate methods, ethical considerations, and potential challenges. It presents practical strategies for solving problems related to the conduct of research using the various forms of data analysis and presents a rich array of case examples from published nursing research. These include author analyses to support readers in decision making regarding their own projects. The book embraces such varied topics as data security in qualitative research, the image of nursing in science fiction literature, the trajectory of research in several nursing studies throughout Africa, and many others. Focused on the needs of both novice researchers and specialists, it will be of value to health institution research divisions, in-service educators and students, and graduate nursing educators and students. Key Features: Explains how to conduct nursing research using content analysis, discourse analysis, narrative analysis, and focus groups and case studies Presents state-of-the-art designs and protocols Focuses on solving practical problems related to the conduct of research Features rich nursing exemplars in a variety of health/mental health clinical settings in the United States and internationally |
| data analysis with javascript: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analysis with javascript: Theory-Based Data Analysis for the Social Sciences Carol S. Aneshensel, 2013 This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of third variables to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions. |
| data analysis with javascript: Concept Data Analysis Claudio Carpineto, Giovanni Romano, 2004-10-22 With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information. Concept Data Analysis: Theory & Applications is the first book that provides a comprehensive treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices. The accompanying website allows you to gain a greater understanding of the principles covered in the book through actively working on the topics discussed. The three main areas explored are interactive mining of documents or collections of documents (including Web documents), automatic text ranking, and rule mining from structured data. The potentials of conceptual data analysis in the application areas being considered are further illustrated by two detailed case studies. Concept Data Analysis: Theory & Applications is essential for researchers active in information processing and management and industry practitioners who are interested in creating a commercial product for conceptual data analysis or developing content management applications. |
| data analysis with javascript: Data Analysis in Criminal Justice and Criminology Philip McCormack, Angela Callahan, 2018-11-07 Data Analysis in Criminal Justice and Criminology: History, Concept, and Application breaks down various data analysis techniques to help students build their conceptual understanding of key methods and processes. The information in the text encourages discussion and consideration of how and why data analysis plays an important role in the fields of criminal justice and criminology. The book is divided into three units. Unit 1 discusses how data analysis is us |
| data analysis with javascript: ggplot2 Hadley Wickham, 2009-10-03 Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures |
| data analysis with javascript: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data analysis with javascript: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| data analysis with javascript: Interactive Web-Based Data Visualization with R, plotly, and shiny Carson Sievert, 2020-01-30 The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics. |
| data analysis with javascript: Murach's JavaScript Mary Delamater, 2015 Today, JavaScript is used in 89.9% of all websites, including the most heavily-trafficked sites like Google, Facebook, YouTube, and Amazon. That's why every web developer should know how to use JavaScript. The problem is that JavaScript is surprisingly difficult to learn, not only for programming novices but also for experienced programmers. But now, Murach's JavaScript (2nd Edition) makes it easier than ever to become an accomplished JavaScript programmer. |
| data analysis with javascript: Hands-On Data Structures and Algorithms with JavaScript Kashyap Mukkamala, 2018-01-30 Data structures and algorithms are the fundamental building blocks of computer programming. They are critical to any problem, provide a complete solution, and act like reusable code. Using appropriate data structures and having a good understanding of algorithm analysis are key in JavaScript to solving crises and ensuring your application is ... |
| data analysis with javascript: Advanced R Hadley Wickham, 2015-09-15 An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does. |
| data analysis with javascript: Practical Data Analysis Hector Cuesta, Dr. Sampath Kumar, 2016-09-30 A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data. |
| data analysis with javascript: Data Visualization with D3.js Scott Murray, Swizec Teller, 2013 This book is a mini tutorial with plenty of code examples and strategies to give you many options when building your own visualizations.This book is ideal for anyone interested in data visualization. Some rudimentary knowledge of JavaScript is required. |
| data analysis with javascript: Mining Social Media Lam Thuy Vo, 2019-11-25 BuzzFeed News Senior Reporter Lam Thuy Vo explains how to mine, process, and analyze data from the social web in meaningful ways with the Python programming language. Did fake Twitter accounts help sway a presidential election? What can Facebook and Reddit archives tell us about human behavior? In Mining Social Media, senior BuzzFeed reporter Lam Thuy Vo shows you how to use Python and key data analysis tools to find the stories buried in social media. Whether you're a professional journalist, an academic researcher, or a citizen investigator, you'll learn how to use technical tools to collect and analyze data from social media sources to build compelling, data-driven stories. Learn how to: Write Python scripts and use APIs to gather data from the social web Download data archives and dig through them for insights Inspect HTML downloaded from websites for useful content Format, aggregate, sort, and filter your collected data using Google Sheets Create data visualizations to illustrate your discoveries Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library Apply what you've learned to research topics on your own Social media is filled with thousands of hidden stories just waiting to be told. Learn to use the data-sleuthing tools that professionals use to write your own data-driven stories. |
| data analysis with javascript: Visualization Analysis and Design Tamara Munzner, 2014-12-01 Learn How to Design Effective Visualization SystemsVisualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques |
| data analysis with javascript: Murach's JavaScript and JQuery (3rd Edition) Mary Delamater, Zak Ruvalcaba, 2017 Presents the JavaScript and jQuery skills that every web developer needs, with complete coding examples and practice exercises. |
| data analysis with javascript: The Statistical Analysis of Categorical Data Erling B. Andersen, 2012-12-06 The aim of this book is to give an up to date account of the most commonly uses statist i cal models for categoriCal data. The emphasis is on the connection between theory and appIications to real data sets. The book only covers models for categorical data. Various n:t0dels for mixed continuous and categorical data are thus excluded. The book is written as a textbook, although many methods and results are quite recent. This should imply, that the book can be used for a graduate course in categorical data analysis. With this aim in mind chapters 3 to 12 are concluded with a set of exer eises. In many cases, the data sets are those data sets, which were not included in the examples of the book, although they at one point in time were regarded as potential can didates for an example. A certain amount of general knowledge of statistical theory is necessary to fully benefit from the book. A summary of the basic statistical concepts deemed necessary pre requisites is given in chapter 2. The mathematical level is only moderately high, but the account in chapter 3 of basic properties of exponential families and the parametric multinomial distribution is made as mathematical preeise as possible without going into mathematical details and leaving out most proofs. |
| data analysis with javascript: Data Analysis Using Stata Ulrich Kohler (Dr. phil.), Frauke Kreuter, 2005-06-15 This book provides a comprehensive introduction to Stata with an emphasis on data management, linear regression, logistic modeling, and using programs to automate repetitive tasks. Using data from a longitudinal study of private households in Germany, the book presents many examples from the social sciences to bring beginners up to speed on the use of Stata. -- BACK COVER. |
| data analysis with javascript: Introduction to Educational Research W. Newton Suter, 2012 W. Newton Suter argues that what is important in a changing education landscape is the ability to think clearly about research methods, reason through complex problems and evaluate published research. He explains how to evaluate data and establish its relevance. |
| data analysis with javascript: Essential First Steps to Data Analysis Carol S. Parke, 2012-12-13 Carol S. Parke's Essential First Steps to Data Analysis: Scenario-Based Examples Using SPSS provides instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Such preparation may involve data management and manipulation tasks, data organization, structural changes to the data files, or conducting preliminary analysis. Twelve research-based scenarios are used to present the content. Each scenario tells the story of a researcher who thoroughly examined their data and the decisions they made along the way. The scenario begins with a description of the researcher's study and his/her data file(s), then describes the issues the researcher must address, explains why they are important, shows how SPSS was used to address the issues and prepare data, and shares the researcher's reflections and any additional decision-making. Finally, each scenario ends with the researcher's written summary of the procedures and outcomes from the initial data preparation or analysis. |
| data analysis with javascript: Mastering Shiny Hadley Wickham, 2021-04-29 Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production |
| data analysis with javascript: Smashing WebKit Jon Raasch, 2011-11-29 As the default browser for both iPhone and Android, WebKit has become a major player in the mobile arena, offering a wider array of HTML5 and CSS3 support than any other major engine. Written by expert author Jon Raasch, this essential book teaches you how to use WebKit to make web sites more engaging, faster, and more visually appealing. You'll explore the variety of unique interface options that WebKit offers as you quickly discover how to create a unique web experience that provides functionality and entertainment. --Publisher description. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
