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Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications.
The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics.
The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data.
The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data.
Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.
|Publisher:||Taylor & Francis|
|Edition description:||New Edition|
|Product dimensions:||7.10(w) x 10.00(h) x 0.70(d)|
About the Author
David R. Hardoon is head of analytics at SAS Singapore, where he is responsible for the positioning of business analytics capabilities and solutions to customers across different business sectors. Dr. Hardoon is also an adjunct faculty member in the School of Information Systems at Singapore Management University and an honorary senior research associate in the Centre for Computational Statistics and Machine Learning at University College London. His research interests include developing and applying computational analytical models for business knowledge discovery and analysis in areas such as taxonomy, neuroscience, aerospace, and finance. He earned a PhD in computer science in the field of machine learning from the University of Southampton.
Galit Shmueli is a SRITNE chaired professor of data analytics and associate professor of statistics and information systems at the Indian School of Business. She is the author of 70 journal articles, references, textbooks, and book chapters in statistics, management, information systems, and marketing. Her research and teaching focus on statistical and data mining methods for contemporary data and applications in information systems and healthcare. She earned a PhD in statistics from the Israel Institute of Technology.
Table of Contents
Introduction to Business Analytics
The Paradigm Shift
From Data to Insight
From Business Intelligence to Business Analytics
Levels of "Intelligence"
The Business Analytics Cycle
Analytic Tools and Methods
Requirements for Integrating Business Analytics
Data Mining and Data Analytics
Data Mining in a Nutshell
What Is Data Mining?
From Data Mining to Data Analytics
"Know Thy Customer"
Mining Online Buzz
Human Resources and Workforce Management
What People are Saying About This
This book offers an introduction to the essence of business analytics, providing a good summary of the analytical solutions employed across these industries today, including an updated vocabulary on new domains such as social media. The reader will appreciate the difference between supervised and unsupervised learning, k-means clustering and regression tree classification. … Getting Started with Business Analytics will simplify, and demystify, the concepts around the "science of data." Looking back at my career in the field of business analytics, I realize that it would have been extremely helpful to have had such a book in hand. It would have provided me with guidance on structuring my analytical solutions and would have inspired me to greater creativity. I hope this book will light the spark of curiosity for a new generation of data scientists.
—Eric Sandosham, Managing Director and Regional Head of Decision Management at Citibank, Asia Pacific 2010–2012