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by Ronald Pearson

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Covers an unusual combination of topics such as interestingness measures for categorical variables, outlier detection, logistic regression, the highly effective multi-window approach to nonparametric spectrum estimaton, and the influence of patchy outliers on spectrum estimation. Exploring Data in Engineering, the Sciences, and Medicine. Two recent and ongoing developments have greatly increased both the range of opportunities for exploratory data analysis and the variety of tools to support this type of analysis.
The book assumes familiarity with calculus and linear algebra, but does not assume any prior exposure to probability or statistics. Both simulation-based and real data examples are included and the book is intended either as an introductory textbook for an exploratory data analysis course like ones the author taught at the ETH where some of this material was used, or for self-study.
Published by Oxford University Press, USA, 2011. Bookseller Inventory XH0DTOROIK. Bibliographic Details. Title: Exploring Data in Engineering, the Sciences
Published by Oxford University Press, USA, 2011. Publisher: Oxford University Press, USA Publication Date: 2011 Binding: Hardcover Book Condition: Good. 1. Published by Oxford University Press, USA (2011). ISBN 10: 0195089650 ISBN 13: 9780195089653.
Learn Data Science from the comfort of your browser, at your own pace . He holds a PhD in Electrical Engineering and Computer Science from .
Learn Data Science from the comfort of your browser, at your own pace with DataCamp's video tutorials & coding challenges on R, Python, Statistics & more He holds a PhD in Electrical Engineering and Computer Science from .
I've read this multiple times that a data scientist spends about 60% of time in understanding and cleansing the data that is being worked on. With this being the context, it becomes crucial to know.