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Categorical data (qualitative) is split into binary, nominal, and ordinal data, such as agree/disagree, male/female, and timeline or process ordering.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},657812,1793051273,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,104,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":102,"slug":103},6,50,"technology",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},7,"Healthcare",40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":101,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":30,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},962093541613,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","|  |\n| --- |\n| \u003Cbr>\u003Cbr>Data Visualization\u003Cbr>|\n| Introduction to data visualization, Visualization\u003Cbr>foundations |\n\nIntroduction to data visualization  \n􀁹 Data visualization is a powerful tool for enhancing understanding and communication of complex data. It involves representing data in a graphical or pictorial form, making it easier to understand and interpret.  \n􀁹 The importance of data visualization in effectively communicating and analyzing data to provide insights into the various types of data visualization tools and techniques available.  \nOverview of DataVisualization Tools :  \n􀁹 Data visualization tools can be broadly classified into three categories: spreadsheets, data visualization software, and programming libraries.  \n􀁹 Spreadsheets-Spreadsheets, such as Microsoft Excel and Google Sheets, are one of the most common data visualization tools used in various domains.They provide basic data visualization capabilities, such as bar charts, line graphs, and scatter plots.  \n􀁹 Data Visualization Software-Data visualization software is a specialized tool designed for data visualization and analysis. Examples of data visualization software includeTableau, QlikView, and Power BI.These tools provide advanced data visualization capabilities, including interactive dashboards, heat maps, and network diagrams.  \n􀁹 Programming Libraries-Programming libraries, such as Matplotlib, ggplot2, and D3.js, are a type of data visualization tool that can be used to create custom data visualizations.  \nCATEGORIES OF DATA VISUALIZATION  \n􀁹 Data visualization is very critical to market research where both numerical and categorical data can be visualized that helps in an increase in impacts of insights and also helps in reducing risk of analysis paralysis. So, data visualization is categorized into following categories :  \nNumerical Data :  \n􀁹 Numerical data is also known as Quantitative data. Numerical data is any data where data generally represents amount such as height, weight, age of a person, etc.  \n􀁹 Numerical data is categorized into two categories :  \nContinuous Data –  \n􀁹 It can be narrowed or categorized (Example: Height measurements) .  \nDiscrete Data –  \n􀁹 This type of data is not “continuous”(Example: Number of cars or children’s a household has) .  \nExamples are Pie Charts, Bar Charts, Averages, Scorecards, etc.  \nCategorical Data :  \n􀁹 Categorical data is also known as Qualitative data. Categorical data is any data where data generally represents groups. It simply consists of categorical variables that are used to represent characteristics such as a person’s ranking, a person’s gender, etc.  \n􀁹 Binary Data –  \n􀁹 In this, classification is based on positioning (Example:Agrees or Disagrees) .  \n􀁹 Nominal Data –  \n􀁹 In this, classification is based on attributes (Example: Male or Female) .  \n􀁹 Ordinal Data –  \n􀁹 In this, classification is based on ordering of information (Example: Timeline or processes) .  \nFoundations of Data Visualization  \n􀁹 Visualization Process Details:  \n􀁹 Data preprocessing and transformation: The starting point is to process the raw data into something usable by the visualization system. The first part is to make sure that the data are mapped to fundamental data types for computer ingestion.  \n􀁹 The second step entails dealing with specific application data issues such as missing values, errors in input, and data too large for processing.The data may be simulated or sampled.  \n􀁹 Mapping for visualizations : Once the data are clean, we can decide on a specific visual representation.This requires representation mappings:  \n􀁹 Rendering transformations :The final stage involves mapping from geometry data to the image.This includes interfacing with a computer graphics Application Programmer’s Interface (API).We need to select the viewing parameters, shading technique if 3D, device transformations (for display, printers, . . . ).This stage of the pipeline is very dependent on the underlying graphics library.  \n","cbCaigu3zZ1jrFEF","https://ap.wps.com/l/cbCaigu3zZ1jrFEF","pdf",1583976,82,"English","# Introduction to data visualization\n## Data visualization overview and importance\n## Data visualization tools\n## Categories of data visualization\n### Numerical data (continuous and discrete)\n### Categorical data (binary, nominal, ordinal)\n## Foundations of data visualization\n### Visualization process details\n### Expressiveness and effectiveness\n# Data visualization techniques","[{\"question\":\"What is data visualization and why is it important?\",\"answer\":\"Data visualization represents data in a graphical or pictorial form to make complex information easier to understand and interpret. It supports effective communication and analysis by revealing insights and helping reduce analysis paralysis.\"},{\"question\":\"How are data visualization tools classified?\",\"answer\":\"They are grouped into three categories: spreadsheets, data visualization software, and programming libraries. Spreadsheets offer basic charts, software enables advanced interactive dashboards, and libraries support custom visualizations.\"},{\"question\":\"What are the main categories and types of data used in data visualization?\",\"answer\":\"Numerical data (quantitative) is split into continuous and discrete data, with examples like height measurements or number of cars. Categorical data (qualitative) is split into binary, nominal, and ordinal data, such as agree/disagree, male/female, and timeline or process ordering.\"}]","Unit-3-Data-Visualization - Introduction to Data Visualization, Foundations | PDF",1792990622,207]