Graphical Exploratory Data Analysis
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Graphical Exploratory Data Analysis
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Author : S. H. C. DuToit
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Graphical Exploratory Data Analysis written by S. H. C. DuToit 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 2012-12-06 with Mathematics categories.
Portraying data graphically certainly contributes toward a clearer and more penetrative understanding of data and also makes sophisticated statistical data analyses more marketable. This realization has emerged from many years of experience in teaching students, in research, and especially from engaging in statistical consulting work in a variety of subject fields. Consequently, we were somewhat surprised to discover that a comprehen sive, yet simple presentation of graphical exploratory techniques for the data analyst was not available. Generally books on the subject were either too incomplete, stopping at a histogram or pie chart, or were too technical and specialized and not linked to readily available computer programs. Many of these graphical techniques have furthermore only recently appeared in statis tical journals and are thus not easily accessible to the statistically unsophis ticated data analyst. This book, therefore, attempts to give a sound overview of most of the well-known and widely used methods of analyzing and portraying data graph ically. Throughout the book the emphasis is on exploratory techniques. Real izing the futility of presenting these methods without the necessary computer programs to actually perform them, we endeavored to provide working com puter programs in almost every case. Graphic representations are illustrated throughout by making use of real-life data. Two such data sets are frequently used throughout the text. In realizing the aims set out above we avoided intricate theoretical derivations and explanations but we nevertheless are convinced that this book will be of inestimable value even to a trained statistician.
Secondary Analysis Of Electronic Health Records
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Author : MIT Critical Data
language : en
Publisher: Springer
Release Date : 2016-09-09
Secondary Analysis Of Electronic Health Records written by MIT Critical Data and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-09 with Medical categories.
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Hands On Exploratory Data Analysis With R
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Author : Radhika Datar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-31
Hands On Exploratory Data Analysis With R written by Radhika Datar 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 2019-05-31 with Computers categories.
Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learnLearn powerful R techniques to speed up your data analysis projectsImport, clean, and explore data using powerful R packagesPractice graphical exploratory analysis techniquesCreate informative data analysis reports using ggplot2Identify and clean missing and erroneous dataExplore data analysis techniques to analyze multi-factor datasetsWho this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.
Graphical Exploratory Data Analysis
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Author : S. H C Dutoit
language : en
Publisher:
Release Date : 1986-10-01
Graphical Exploratory Data Analysis written by S. H C Dutoit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986-10-01 with categories.
Interactive And Dynamic Graphics For Data Analysis
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Author : Dianne Cook
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-05
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 2007-09-05 with Mathematics categories.
This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapter topics include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The book is augmented by a wealth of online material.
Hands On Exploratory Data Analysis With Python
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Author : Suresh Kumar Mukhiya
language : en
Publisher:
Release Date : 2020-03-27
Hands On Exploratory Data Analysis With Python written by Suresh Kumar Mukhiya and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-27 with categories.
Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key Features Understand the fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different variables Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package Book 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 learn Import, clean, and explore data to perform preliminary analysis using powerful Python packages Identify and transform erroneous data using different data wrangling techniques Explore the use of multiple regression to describe non-linear relationships Discover hypothesis testing and explore techniques of time-series analysis Understand and interpret results obtained from graphical analysis Build, train, and optimize predictive models to estimate results Perform complex EDA techniques on open source datasets Who 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.
Graphical Data Analysis With R
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Author : Antony Unwin
language : en
Publisher: CRC Press
Release Date : 2015-03-25
Graphical Data Analysis With R written by Antony Unwin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-25 with Mathematics categories.
See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.
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.
Exploratory Data Analysis Using R
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Author : Ronald K. Pearson
language : en
Publisher: CRC Press
Release Date : 2018-05-04
Exploratory Data Analysis Using R written by Ronald K. Pearson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-04 with Business & Economics categories.
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).