Data Visualization In Exploratory Data Analysis

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Data Visualization In Exploratory Data Analysis

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Author : Yingsen Mao
language : en
Publisher:
Release Date : 2016

Data Visualization In Exploratory Data Analysis written by Yingsen Mao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Data mining categories.

Exploratory data analysis (EDA) refers to an iterative process through which analysts constantly 'ask questions' and extract knowledge from data. EDA is becoming more and more important for modern data analysis, such as business analytics and business intelligence, as it greatly relaxes the statistical assumption required by its counterpart- confirmation data analysis (CDA), and involves analysts directly in the data mining process. However, exploratory visual analysis, as the central part of EDA, requires heavy data manipulations and tedious visual specifications, which might impede the EDA process if the analyst has no guidelines to follow. In this paper, we present a framework of visual data exploration in terms of the type of variable given, using the effectiveness and expressiveness rules of visual encoding design developed by Munzner [1] as guidelines, in order to facilitate the EDA process. A classification problem of the Titanic data is also provided to demonstrate how the visual exploratory analysis facilitates the data mining process by increasing the accuracy rate of prediction. In addition, we classify prevailing data visualization technologies, including the layered grammar of ggplot2 [2], the VizQL of Tableau [3], d3 [4] and Shiny [5], as grammar-based and web-based, and review their adaptability for EDA, as EDA is discovery-oriented and analysts must be able to quickly change both what they are viewing and how they are viewing the data.

Hands On Exploratory Data Analysis With Python

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Author : Suresh Kumar Mukhiya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-03-27

Hands On Exploratory Data Analysis With Python written by Suresh Kumar Mukhiya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-27 with Computers categories.

Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.

Handbook Of Data Visualization

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Author : Chun-houh Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-18

Handbook Of Data Visualization written by Chun-houh Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-18 with Computers categories.

Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.

Visualization And Data Analysis

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Author :
language : en
Publisher:
Release Date : 2004

Visualization And Data Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computer graphics categories.

Exploratory Data Analytics For Healthcare

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Author : R. Lakshmana Kumar
language : en
Publisher: CRC Press
Release Date : 2021-12-23

Exploratory Data Analytics For Healthcare written by R. Lakshmana Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-23 with Computers categories.

Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Augmenting Exploratory Data Analysis With Visualization Recommendation

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Author : Kanit Wongsuphasawat
language : en
Publisher:
Release Date : 2018

Augmenting Exploratory Data Analysis With Visualization Recommendation written by Kanit Wongsuphasawat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

Exploratory data analysis is one of the key activities for understanding and discovering new insights from data. As exploratory data analysis can involve both open-ended exploration and focused question answering, analysis tool should facilitate both exploration breadth and analysis depth. However, existing data exploration tools typically require manual chart specification, which can be tedious and prevent analysts from rapidly exploring different aspects of the data. Moreover, analysts may be blindsided by their own cognitive biases and prematurely fixate on specific questions or hypotheses. Without discipline and time, analysts may overlook important insights in the data, such as potentially confounding factors and data quality issues, and produce inaccurate results in their analyses. To help analyst perform rapid and systematic data exploration, this dissertation presents the design of mixed-initiative systems that complement manual chart specification with chart recommendation. To better understand the practice and challenges of exploratory data analysis, we first conduct an interview study with 18 data analysts. From the interview data, we characterize the goals, process, and challenges of exploratory data analysis. We then identify design opportunities for exploratory analysis tools. One major opportunity is facilitating rapid and systematic exploration with automation and guidance. The rest of the dissertation addresses this opportunity by contributing a stack of systems to augment exploratory analysis tools with chart recommendation. At the foundations of this stack, we introduce new formal languages for chart specification and recommendation. The Vega-Lite visualization grammar provides a formal representation for specifying and reasoning about charts. Building on Vega-Lite, the CompassQL query language combines partial chart specification with recommendation directives to provide a generalizable framework for chart recommendation via queries over the space of visualizations. Based on these foundations, we used the iterative design process to develop and study new recommendation-powered visual data exploration tools. Voyager enables data exploration via browsing of recommended charts, while allowing users to steer the recommendations by selecting data fields and transformations. Our user study, which compares Voyager with a traditional chart authoring tool, indicates the complementary benefits of manual authoring and recommendation browsing. Inspired by the study result, Voyager~2 blends manual and automated chart authoring in a single tool to facilitate rapid and systematic data exploration while preserving users' flexibility to directly author a broad range of charts. All of these systems have been released as open-source projects and adopted by both research and professional data science communities.

Cartography And Geographic Information Science

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Author :
language : en
Publisher:
Release Date : 2008

Cartography And Geographic Information Science written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Cartography categories.

Interactive And Dynamic Graphics For Data Analysis

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Author : Dianne Cook
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-12

Interactive And Dynamic Graphics For Data Analysis written by Dianne Cook and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-12 with Computers categories.

This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: • An introduction to data visualization, explaining how it di?ers from other types of visualization. • Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. • An approach for exploring missing values in data. • An explanation of the use of these tools in cluster analysis and supervised classi?cation. • An overview of additional material available on the web. • A description of the data used in the analyses and exercises. The book’s examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2006) isafreesoftware environment for statistical computing and graphics; it is most often used from the command line, provides a wide variety of statistical methods, and includes high–quality staticgraphics.RaroseintheStatisticsDepartmentoftheUniversityofAu- land and is now developed and maintained by a global collaborative e?ort.

Fifth Ieee International Conference On Information Visualisation

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Author : Ebad Banissi
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 2001

Fifth Ieee International Conference On Information Visualisation written by Ebad Banissi and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.

Annotation The main subjects of the July 2001 conference are computer aided geometric design, medical visualization, visualization in built environment, digital art, rendering, and visual methods for parallel and distributed programming. Topics of the 110 papers include a prototype design tool for building integrated photovoltaics, finding and characterizing candidate binding sites, visualizing capacity and load in production planning, error analysis for the evaluation of rational Bezier curves, drawing conics on a hexagonal grid, visual interaction with XML metadata, virtual access to landscapes and historic gardens at linked locations, and adaptive fairing of surface meshes by geometric diffusion. No subject index. c. Book News Inc.

Making Sense Of Data I

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Author : Glenn J. Myatt
language : en
Publisher: John Wiley & Sons
Release Date : 2014-07-02

Making Sense Of Data I written by Glenn J. Myatt and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-02 with Mathematics categories.

Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.