November 2019
Beginner to intermediate
608 pages
16h 33m
English
book
Data Mining for Business Analytics
by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel
Content preview from Data Mining for Business Analytics
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Preface to the Python Edition
This textbook first appeared in early 2007 and has been used by numerous students and practitioners and in many courses, including our own experience teaching this material both online and in person for more than 15 years. The first edition, based on the Excel add-in Analytic Solver Data Mining (previously XLMiner), was followed by two more Analytic Solver editions, a JMP edition, an R edition, and now this Python edition, with its companion website, www.dataminingbook.com.
This new Python edition, which relies on the free and open-source Python programming language, presents output from Python, as well as the code used to produce that output, including specification of the appropriate packages and functions, the dominant one being scikit-learn. Unlike computer-science or statistics-oriented textbooks, the focus in this book is on data mining concepts, and how to implement the associated algorithms in Python. We assume a basic familiarity with Python.
For this Python edition, a new co-author, Peter Gedeck comes on board bringing extensive data science experience in business. In addition to providing Python code and output, this edition also incorporates updates and new material based on feedback from instructors teaching MBA, MS, undergraduate, diploma, and executive courses, and from their students as well. Importantly, this edition includes for the first time an extended section on Data Ethics (Section 2.9).
A note about the book’s title: The ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.