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Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining,Used
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Praise for the First Edition...a wellwritten book on data analysis and data mining that provides an excellent foundation...CHOICEThis is a mustread book for learning practical statistics and data analysis...Computing Reviews.comA proven goto 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 stepbystep 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 computeraided implementation with accompanying worked examples New appendices with coverage on the freely available Traceis 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 realworld examples of data preparation to establish a practical background for making decisions from dataMaking 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 graduatelevel courses in data analysis and data mining and is appropriate for crossdisciplinary courses found within computer science and engineering departments.
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