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![]( "Buchcover ISBN 9781118422106")
Making Sense of Data I
A Practical Guide to Exploratory Data Analysis and Data Mining
von Glenn J. Myatt und Wayne P. JohnsonPraise for the First Edition
„... a well-written book on data analysis anddata mining that provides an excellent foundation...“
--CHOICE
„This is a must-read book for learning practicalstatistics and data analysis...“
--Computing Reviews. com
A proven go-to guide for data analysis, Making Sense of DataI: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches thatare necessary to make timely and accurate decisions in a diverserange of projects. Based on the authors' practical experiencein implementing data analysis and data mining, the new editionprovides clear explanations that guide readers from almost everyfield of study. In order to facilitate the needed steps when handling a dataanalysis or data mining project, a step-by-step approach aidsprofessionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The toolsto summarize and interpret data in order to master data analysisare integrated throughout, and the Second Edition alsofeatures:
* Updated exercises for both manual and computer-aidedimplementation with accompanying worked examples
* New appendices with coverage on the freely availableTraceis(TM) software, including tutorials using data from avariety of disciplines such as the social sciences, engineering, and finance
* New topical coverage on multiple linear regression and logisticregression to provide a range of widely used and transparentapproaches
* Additional real-world examples of data preparation to establisha practical background for making decisions from data
Making Sense of Data I: A Practical Guide to Exploratory DataAnalysis and Data Mining, Second Edition is an excellentreference for researchers and professionals who need to achieveeffective decision making from data. The Second Edition isalso an ideal textbook for undergraduate and graduate-level coursesin data analysis and data mining and is appropriate forcross-disciplinary courses found within computer science andengineering departments.
„... a well-written book on data analysis anddata mining that provides an excellent foundation...“
--CHOICE
„This is a must-read book for learning practicalstatistics and data analysis...“
--Computing Reviews. com
A proven go-to guide for data analysis, Making Sense of DataI: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches thatare necessary to make timely and accurate decisions in a diverserange of projects. Based on the authors' practical experiencein implementing data analysis and data mining, the new editionprovides clear explanations that guide readers from almost everyfield of study. In order to facilitate the needed steps when handling a dataanalysis or data mining project, a step-by-step approach aidsprofessionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The toolsto summarize and interpret data in order to master data analysisare integrated throughout, and the Second Edition alsofeatures:
* Updated exercises for both manual and computer-aidedimplementation with accompanying worked examples
* New appendices with coverage on the freely availableTraceis(TM) software, including tutorials using data from avariety of disciplines such as the social sciences, engineering, and finance
* New topical coverage on multiple linear regression and logisticregression to provide a range of widely used and transparentapproaches
* Additional real-world examples of data preparation to establisha practical background for making decisions from data
Making Sense of Data I: A Practical Guide to Exploratory DataAnalysis and Data Mining, Second Edition is an excellentreference for researchers and professionals who need to achieveeffective decision making from data. The Second Edition isalso an ideal textbook for undergraduate and graduate-level coursesin data analysis and data mining and is appropriate forcross-disciplinary courses found within computer science andengineering departments.
