Enhanced descriptions from Syndetics:

Assuming no prior knowledge or technical skills,Getting Started with Business Analytics: Insightful Decision-Makingexplores 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.

You can check out the book's website here .

Table of contents provided by Syndetics

• Foreword (p. ix)
• Preface (p. xi)
• Acknowledgments (p. xiii)
• I Introduction to Business Analytics (p. 1)
• 1 The Paradigm Shift (p. 3)
• 1.1 From Data to Insight (p. 4)
• 1.2 From Business Intelligence to Business Analytics (p. 7)
• 1.3 Levels of "Intelligence" (p. 13)
• 2 The Business Analytics Cycle (p. 17)
• 2.2 Objective (p. 18)
• 2.2 Data (p. 19)
• 2.3 Analytic Tools and Methods (p. 22)
• 2.4 Implementation (p. 22)
• 2.5 Guiding Questions (p. 24)
• 2.6 Requirements for Integrating Business Analytics (p. 26)
• 2.7 Common Questions (p. 31)
• II Date Mining and Data Analytics (p. 39)
• 3 Date Mining in a Nutshell (p. 41)
• 3.2 What Is Data Mining? (p. 41)
• 3.2 Predictive Analytics (p. 42)
• 3.3 Forecasting (p. 64)
• 3.4 Optimization (p. 68)
• 3.5 Simulation (p. 75)
• 4 From Date Mining to Data Analytics (p. 83)
• 4.1 Network Analytics (p. 83)
• 4.2 Text Analytics (p. 86)
• III Business Analytics (p. 103)
• 5 Customer Analytics (p. 105)
• 5.1 "Know Thy Customer" (p. 110)
• 5.2 Targeting Customers (p. 117)
• 5.3 Project Suggestions (p. 125)
• 6 Social Analytics (p. 129)
• 6.1 Customer Satisfaction (p. 130)
• 6.2 Mining Online Buzz (p. 135)
• 6.3 Project Suggestions (p. 142)
• 7 Operational Analytics (p. 147)
• 7.1 Inventory Management (p. 147)
• 7.2 Marketing Optimization (p. 151)
• 7.3 Predictive Maintenance (p. 153)
• 7.4 Human Resources & Workforce Management (p. 157)
• 7.5 Project Suggestions (p. 159)
• Epilogue (p. 163)
• Bibliography (p. 165)
• Index (p. 167)

Reviews provided by Syndetics

CHOICE Review

Hardoon (head of analytics, SAS Singapore; adjunct faculty, Singapore Management Univ.) and Shmueli (data analytics, Indian School of Business, India) have written an interesting "how to get started" book about a contemporary and challenging development in business. The authors guide the reader into the world of business analytics. They do so without involving the reader in the mathematical and statistical underpinnings that underlie business analytics. Instead they try to explain and demystify the main concepts and terminologies and provide many examples of real-world applications. Part 1 offers a general introduction to business analytics, which supports fact-based decision making. Part 2 covers the basics of data mining and data analytics. Part 3 examines three main areas of business analytics: customer analytics, social analytics, and operational analytics. Since this is a starter text, no reader will understand all the complexities associated with any of the main areas. However, the chapters provide a good basis for readers to pursue further study of these areas. In addition, chapters in part 3 end with suggested projects using publicly available data. This timely, accessible book is relevant to students, managers, analysts, executives, consultants, and the general public. Summing Up: Recommended. Business collections at all levels. E. J. Szewczak Canisius College

Author notes provided by Syndetics

David R. Hardoon is the Senior Advisor for Data and Artificial Intelligence at UnionBank Philippines, Chair of Data Committee at Aboitiz Group and acting in capacity of Managing Director for Aboitiz Data Innovation. Concurrently David is an external advisor to Singapore's Corrupt Investigation Practices Bureau (CPIB) in the capacity of Senior Advisor (Artificial Intelligence) and to Singapore's Central Provident Fund Board (CPF) in the capacity of Senior Advisor (Data Science).

David has extensive exposure and experience in both industry and academia and he has consistently applied advanced technology with an analytical mindset to shape and deliver new innovation. David holds a PhD in Computer Science in the field of Machine Learning from the University of Southampton and graduated from Royal Holloway, University of London with First Class Honors B.Sc. in Computer Science and Artificial Intelligence.

Galit Shmueli is Distinguished Professor at the Institute of Service Science, National Tsing Hua University, Taiwan. Between 2011-2014 she was the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business, and earlier Associate Professor of Statistics at University of Maryland's Smith School of Business. She is best known for her research and teaching in business analytics, with a focus on statistical and data mining methods for contemporary data and applications in information systems and healthcare.

Dr. Shmueli's research has been published in the statistics, management, information systems, and marketing literature. She authors over seventy journal articles, books, textbooks and book chapters, including the popular textbook Data Mining for Business Intelligence and Practical Time Series Forecasting. Dr. Shmueli is an award-winning teacher and speaker on data analytics.