Making Sense of Data I (PDF)

Praise for the First Edition

"...a well-written book on data analysis and
data mining that provides an excellent foundation..."

--CHOICE

"This is a must-read book for learning practical
statistics and data analysis..."

--Computing Reviews.com



A proven go-to 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 step-by-step 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 computer-aided
implementation with accompanying worked examples

* New appendices with coverage on the freely available
Traceis(TM) 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 real-world examples of data preparation to establish
a practical background for making decisions from data

Making 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 graduate-level courses
in data analysis and data mining and is appropriate for
cross-disciplinary courses found within computer science and
engineering departments.