Data Mining for the Masses 2nd Edition by Dr. Matthew A North – Ebook PDF Instant Download/Delivery. 0615684378, 978-0615684376
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Product details:
ISBN 10: 0615684378
ISBN 13: 978-0615684376
Author: Dr. Matthew A North
Data Mining for the Masses 2nd Edition:
The second edition of Data Mining for the Masses is now available! The new edition includes two additional modeling techniques (k-Nearest Neighbors and Naive Bayes), along with implementations of all chapter examples in the R statistical language.
Have you ever found yourself working with a spreadsheet full of data and wishing you could make more sense of the numbers? Have you reviewed sales or operations reports, wondering if there’s a better way to anticipate your customers’ needs? Perhaps you’ve even thought to yourself: There’s got to be more to these figures than what I’m seeing!
Data Mining can help, and you don’t need a Ph.D. in Computer Science to do it. You can forecast staffing levels, predict demand for inventory, even sift through millions of lines of customer emails looking for common themes—all using data mining. It’s easier than you might think.
In Data Mining for the Masses, professor Matt North—a former risk analyst and database developer for eBay.com—uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.
You’ve got data and you know it’s got value, if only you can figure out how to unlock it. This book can show you how. Let’s start digging!
Through an agreement with the Global Text Project, an electronic version of this text is available online at (https://globaltext.terry.uga.edu/books). Proceeds from the sales of printed copies through Amazon enable the author to support the Global Text Project’s goal of making electronic texts available to students in developing economies.
Data Mining for the Masses 2nd Edition Table of contents:
SECTION ONE: Data Mining Basics
- Chapter One: Introduction to Data Mining and CRISP-DM
- Chapter Two: Organizational Understanding and Data Understanding
- Chapter Three: Data Preparation
SECTION TWO: Data Mining Models and Methods
- Chapter Four: Correlation
- Chapter Five: Association Rules
- Chapter Six: k-Means Clustering
- Chapter Seven: Discriminant Analysis
- Chapter Eight: Linear Regression
- Chapter Nine: Logistic Regression
- Chapter Ten: Decision Trees
- Chapter Eleven: Neural Networks
- Chapter Twelve: Text Mining
SECTION THREE: Special Considerations in Data Mining
- Chapter Thirteen: Evaluation and Deployment
- Chapter Fourteen: Data Mining Ethics
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