almediah.fr
» » Exploring Data in Engineering, the Sciences, and Medicine

Download Exploring Data in Engineering, the Sciences, and Medicine eBook

by Ronald Pearson

Download Exploring Data in Engineering, the Sciences, and Medicine eBook
ISBN:
0195089650
Author:
Ronald Pearson
Category:
Mathematics
Language:
English
Publisher:
Oxford University Press; 1 edition (January 21, 2011)
Pages:
792 pages
EPUB book:
1494 kb
FB2 book:
1352 kb
DJVU:
1425 kb
Other formats
azw docx lit mobi
Rating:
4.3
Votes:
849


Online shopping from a great selection at Books Store. by Ronald K. Pearson.

Online shopping from a great selection at Books Store.

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.

The recent dramatic rise in the number of public datasets available free from the Internet, coupled with the evolution of the Open Source software movement, which makes powerful analysis packages like R freely available, have greatly increased both the range of opportunities for exploratory data analysis and the variety of tools that support this type of analysis. This book will provide a thorough introduction to a useful subset of these analysis tools, illustrating what they are, what they do, and when and how they fail. Specific topics covered include descriptive characterizations like summary statistics (mean, median, standard deviation, MAD scale estimate), graphical techniques like boxplots and nonparametric density estimates, various forms of regression modeling (standard linear regression models, logistic regression, and highly robust techniques like least trimmed squares), and the recognition and treatment of important data anomalies like outliers and missing data. The unique combination of topics presented in this book separate it from any other book of its kind. Intended for use as an introductory textbook for an exploratory data analysis course or as self-study companion for professionals and graduate students, this book assumes familiarity with calculus and linear algebra, though no previous exposure to probability or statistics is required. Both simulation-based and real data examples are included, as are end-of-chapter exercises and both R code and datasets.
  • Gabar
This is a wonderful extended discussion of the intersection of traditional statistics with "big data". There's a lot of math and the mathematical development, as well as the choice of topics, is informed by the issues raised in data exploration, and therefore well motivated for readers like myself who are more engineering focused. The author is a bit obsessed with outliers and datasets that are corrupted in one way or another and analyzes several different kinds of examples throughout the book. Such an obsession is appropriate for a book about exploring data, and also for the context in which people are analyzing datasets created by others, so that the analysts aren't familiar with the measurement conditions, post-measurement processing, unstated assumptions, and so on.

I found this book to be an efficient way to get a deeper understanding of statistics as it applies to practical data analysis. What I'm looking for now is a book teaches the relationships between the techniques of machine learning and traditional statistics.
  • Mr.Champions
This is definitely a classic and a reference book to have. However, it is not integrated into any programming language such as R. So it is a textbook in then best tradition.
  • Rrinel
In terms of clarity, breadth, and accuracy, I found this book to be way above the standards in its class. It is outstanding as a reference book and as a potential textbook.
  • Chi
I found the book to be well written. The examples are interesting. It is a nice balance of rigor and practicality.