Data Mining for Business Analytics
eBook - ePub

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner

Galit Shmueli, Peter C. Bruce, Nitin R. Patel

Share book
English
ePUB (mobile friendly)
Available on iOS & Android
eBook - ePub

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner

Galit Shmueli, Peter C. Bruce, Nitin R. Patel

Book details
Book preview
Table of contents
Citations

About This Book

An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.

Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:

  • Real-world examples to build a theoretical and practical understanding of key data mining methods
  • End-of-chapter exercises that help readers better understand the presented material
  • Data-rich case studies to illustrate various applications of data mining techniques
  • Completely new chapters on social network analysis and text mining
  • A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com
  • Free 140-day license to use XLMinerfor Education software

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

Praise for the Second Edition

"…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing." – Research Magazine

"Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization

Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters.

Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley.

Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.

Frequently asked questions
How do I cancel my subscription?
Simply head over to the account section in settings and click on “Cancel Subscription” - it’s as simple as that. After you cancel, your membership will stay active for the remainder of the time you’ve paid for. Learn more here.
Can/how do I download books?
At the moment all of our mobile-responsive ePub books are available to download via the app. Most of our PDFs are also available to download and we're working on making the final remaining ones downloadable now. Learn more here.
What is the difference between the pricing plans?
Both plans give you full access to the library and all of Perlego’s features. The only differences are the price and subscription period: With the annual plan you’ll save around 30% compared to 12 months on the monthly plan.
What is Perlego?
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, we’ve got you covered! Learn more here.
Do you support text-to-speech?
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Is Data Mining for Business Analytics an online PDF/ePUB?
Yes, you can access Data Mining for Business Analytics by Galit Shmueli, Peter C. Bruce, Nitin R. Patel in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2016
ISBN
9781118729243

Part I
Preliminaries

Chapter 1
Introduction

1.1 What is Business Analytics?

Business analytics (BA) is the practice and art of bringing quantitative data to bear on decision making. The term means different things to different organizations.
Consider the role of analytics in helping newspapers survive the transition to a digital world. One tabloid newspaper with a working-class readership in Britain had launched a web version of the paper, and did tests on its home page to determine which images produced more hits: cats, dogs, or monkeys. This simple application, for this company, was considered analytics. By contrast, the Washington Post has a highly influential audience that is of interest to big defense contractors: it is perhaps the only newspaper where you routinely see advertisements for aircraft carriers. In the digital environment, the Post can track readers by time of day, location, and user subscription information. In this fashion, the display of the aircraft carrier advertisement in the online paper may be focused on a very small group of individuals—say, the members of the House and Senate Armed Services Committees who will be voting on the Pentagon's budget.
Business analytics, or more generically, analytics, includes a range of data analysis methods. Many powerful applications involve little more than counting, rule checking, and basic arithmetic. For some organizations, this is what is meant by analytics.
The next level of business analytics, now termed business intelligence (BI), refers to data visualization and reporting for understanding “what happened and what is happening.” This is done by use of charts, tables, and dashboards to display, examine, and explore data. BI, which earlier consisted mainly of generating static reports, has evolved into more user-friendly and effective tools and practices, such as creating interactive dashboards that allow the user not only to access real-time data but also to directly interact with it. Effective dashboards are those that tie directly into company data, and give managers a tool to quickly see what might not readily be apparent in a large complex database. One such tool for industrial operations managers displays customer orders in a single two-dimensional display, using color and bubble size as added variables, showing customer name, type of product, size of order, and length of time to produce.
Business analytics now typically includes BI as well as sophisticated data analysis methods, such as statistical models and data mining algorithms used for exploring data, quantifying and explaining relationships between measurements, and predicting new records. Methods like regression models are used to describe and quantify “on average” relationships (e.g., between advertising and sales), to predict new records (e.g., whether a new patient will react positively to a medication), and to forecast future values (e.g., next week's web traffic).
Readers familiar with earlier editions of this book might have noticed that the book title changed from Data Mining for Business Intelligence to Data Mining for business analytics in this edition. The change reflects the more recent term BA, which overtook the earlier term BI to denote advanced analytics. Today, BI is used to refer to data visualization and reporting.

Table of contents

Citation styles for Data Mining for Business Analytics
APA 6 Citation
Bruce, P., Shmueli, G., & Patel, N. (2016). Data Mining for Business Analytics (3rd ed.). Wiley. Retrieved from https://www.perlego.com/book/990841/data-mining-for-business-analytics-concepts-techniques-and-applications-with-xlminer-pdf (Original work published 2016)
Chicago Citation
Bruce, Peter, Galit Shmueli, and Nitin Patel. (2016) 2016. Data Mining for Business Analytics. 3rd ed. Wiley. https://www.perlego.com/book/990841/data-mining-for-business-analytics-concepts-techniques-and-applications-with-xlminer-pdf.
Harvard Citation
Bruce, P., Shmueli, G. and Patel, N. (2016) Data Mining for Business Analytics. 3rd edn. Wiley. Available at: https://www.perlego.com/book/990841/data-mining-for-business-analytics-concepts-techniques-and-applications-with-xlminer-pdf (Accessed: 14 October 2022).
MLA 7 Citation
Bruce, Peter, Galit Shmueli, and Nitin Patel. Data Mining for Business Analytics. 3rd ed. Wiley, 2016. Web. 14 Oct. 2022.