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Covers data abstraction and validation concepts, visualization categories (scalar, point, vector, cluster, matrix), and hands-on workflows with Pandas including loading, cleaning, transformation, and descriptive statistics. Includes Matplotlib-based visualization (basic, specialized, advanced tools, waffle charts, word clouds) and Excel data analysis with cleaning, conditional formatting, filtering, lookup, pivot tables, validation, and analysis tools such as regression and smoothing.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/digital-notes-of-data-visualization-techniques-r22a6705/290147/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/digital-notes-of-data-visualization-techniques-r22a6705/290147.png","ImageObject",300,407,{"name":92,"@type":93},"4398046744996","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What are the main course objectives for data visualization techniques?","Question",{"text":112,"@type":113},"The course aims to learn statistical methods and visualization techniques, understand Python basics, and apply Matplotlib/Seaborn packages. It also covers Excel concepts and operations for visualization and analysis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What topics are included in Unit I on data visualization?",{"text":117,"@type":113},"Unit I covers introduction to data visualization, key factors, importance in business intelligence, data visualization tools and types of data, data abstraction (data types, dataset types, attribute types), task abstraction, and four levels for validation.",{"name":119,"@type":110,"acceptedAnswer":120},"What does the course cover for visualization in Python and Excel?",{"text":121,"@type":113},"Matplotlib-based visualization topics include basic and specialized/advanced plots, waffle charts, and word clouds. Excel topics include data analysis, range names, tables, cleaning data, conditional formatting, sorting, advanced filtering, lookup functions, pivot tables, data visualization, and data validation tools like ANOVA and regression.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290147,1789640800,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":66,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},4398046744996,"DIGITAL NOTES OF  \nDATA VISUALIZATION TECHNIQUES  \n(R22A6705)  \nB.TECH IV Year-I SEM  \n(2025-26)  \nPREPARED BY  \nDr. P.V. Naresh  \nAssociate Professor  \nDEPARTMENT OF INFORMATION TECHNOLOGY  \nMALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY  \n(Autonomous Institution – UGC, Govt. of India)  \nSponsored by CMR Educational Society  \nMALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY  \nIV Year B. TECH–I-SEM L/T/P/C  \n3/0/0/3  \nDATA VISUALIZATION TECHNIQUES  \n(R22A6705)  \nCOURSE OBJECTIVES:  \n1. To learn different statistical methods for Data visualization.  \n2. To learn Visulization Technoques .  \n3. To understand the basics of Python.  \n4. To understand the usage of the Matplotlib, Seaborn Packages  \n5. To Learn about Excel and various operations using Excel  \nUNIT I  \nIntroduction to Data Visualization, Key factors of Data Visualization, Importance of Data Visualization in Business Intelligence, Data Visualization tools and types of data, Data Abstraction: data types, dataset types, Attribute types, Task Abstraction, Four Levels for Validation.  \nUNIT II  \nVisualization Techniques: Scalar and point visualizations, – vector visualizations – multidimensional visualizations – Cluster Visualizations – matrix visualization in Bayesian data analysis  \nUNIT III  \nGetting Started with Pandas: Arrays and vectorized computation, Introduction to pandas Data Structures, Essential Functionality, Summarizing and Computing Descriptive Statistics. Data Loading, Storage and File Formats. Reading and Writing Data in Text Format, Web Scraping, Binary Data Formats, Interacting with Web APIs, Interacting with Databases Data Cleaning and Preparation. Handling Missing Data, Data Transformation, String Manipulation  \nUNIT IV (Data Visualization Using Matplotlib)  \nData Visualization Tools in Python- Introduction to Matplotlib, Basic plots Using matplotlib, Specialized Visualization Tools using Matplotlib, Advanced Visualization Tools using MatplotlibWaffle Charts, Word Clouds.  \nUNIT-V (Working With Excel)  \nIntroduction: Data Analysis, Excel Data analysis. Working with range names. Tables. Cleaning Data. Conditional formatting, Sorting, Advanced Filtering, Lookup functions, Pivot tables, Data Visualization, Data Validation. Understanding Analysis tool pack: Anova, correlation, covariance, moving average, descriptive statistics, exponential smoothing, fourier Analysis, Random number generation, sampling, ttest, f-test, and regression.  \nTEXT BOOKS:  \n1. Core Python Programming-Second Edition, R. Nageswara Rao, DreamtechPress.  \n2. A to Z of MS EXCEL: A Book for Learners & Trainers (MS Excel Comprehensive Guide by Rinkoo Jainn  \n3. Data Analysis with Excel by Manish Nigam. Bpb Publications  \n4. KNIME Essentials, by Gábor Bakos, 2013  \n5. Data Science Tools by Christopher Greco, 2020  \nREFERENCE BOOKS:  \n1. Introduction to Data Science a Python approach to concepts, Techniques and Applications, Igual, L;Seghi’, S. Springer, ISBN:978-3-319-50016-4.  \n2. ALL-IN-ONE-EXCEL 2022 Bible for Dummies by Bryant Shelton  \n3. Excel® 2019 BIBLE BY Michael Alexander, Dick Kusleika  \n4. Python for Data Analysis by William McKinney, Second Edition, O’Reilly MediaInc.  \nCOURSE OUTCOMES:  \nAt Completion of this course, students would be able to-  \n1. Apply statistical methods for Data visualization on Various Datasets  \n2. Understand different Visulization Tecniques.  \n3. Gain knowledge on various visualization techniques using Python  \n4. Understand usage of various packages in Python.  \n5. Understand the concept of Excel, Visualization using Excel  \nUnit-I  \nIntroduction to Data Visualization, Key factors of Data Visualization, Importance of Data Visualization in Business Intelligence, Data Visualization tools and types of data, Data Abstraction: data types, dataset types, Attribute types, Task Abstraction, Four Levels for Validation.  \nIntroduction  \nIn our increasingly data-driven world, it’s more important than ever to have accessible ways to view and understand data.","cbCaijQzbARd4Nog","https://ap.wps.com/l/cbCaijQzbARd4Nog","pdf",8078609,109,"English","# Course Objectives\n# Unit I - Introduction to Data Visualization\n## Key factors and importance in Business Intelligence\n## Data abstraction and validation\n# Unit II - Visualization Techniques\n## Scalar, point, vector, multidimensional, cluster, matrix visualizations\n# Unit III - Getting Started with Pandas\n## Data structures, descriptive statistics, loading and storage\n## Reading/writing, web scraping, APIs, databases\n## Data cleaning, transformation, and string manipulation\n# Unit IV - Data Visualization Using Matplotlib\n## Matplotlib fundamentals and advanced visualization tools\n## Waffle charts and word clouds\n# Unit V - Working With Excel\n## Data analysis, cleaning, conditional formatting, filtering\n## Lookup functions, pivot tables, validation\n## Analysis tool pack: ANOVA, correlation/covariance, smoothing, Fourier, sampling, t-test/f-test, regression\n# Text Books and Reference Books\n# Course Outcomes\n# Unit I - Introduction to Data Visualization (Expanded Notes)\n## Definition and goals\n## Relation to data science and data presentation architecture\n## Importance across careers","[{\"question\":\"What are the main course objectives for data visualization techniques?\",\"answer\":\"The course aims to learn statistical methods and visualization techniques, understand Python basics, and apply Matplotlib/Seaborn packages. It also covers Excel concepts and operations for visualization and analysis.\"},{\"question\":\"What topics are included in Unit I on data visualization?\",\"answer\":\"Unit I covers introduction to data visualization, key factors, importance in business intelligence, data visualization tools and types of data, data abstraction (data types, dataset types, attribute types), task abstraction, and four levels for validation.\"},{\"question\":\"What does the course cover for visualization in Python and Excel?\",\"answer\":\"Matplotlib-based visualization topics include basic and specialized/advanced plots, waffle charts, and word clouds. Excel topics include data analysis, range names, tables, cleaning data, conditional formatting, sorting, advanced filtering, lookup functions, pivot tables, data visualization, and data validation tools like ANOVA and regression.\"}]","DIGITAL NOTES OF DATA VISUALIZATION TECHNIQUES - (R22A6705) | PDF",275]