Business Analytics

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Business Analytics

Business Analytics

Authors: Dr. Dattatraya Pandurang Rane, Mr. Deepak Tulsiram Patil, Dr. Sule Bipin Subodh and Dr. Rajeshree Ramesh Khande

Editor: Dr. Namita Chawla

ISBN: 978-81-985805-6-6

DOI: https://doi.org/10.59646/ba/320

Date of Publication: February 26, 2025

About the Book:

In today’s fast-paced and data-driven business environment, organizations are constantly seeking ways to gain a competitive edge and make informed decisions, and it is here that business analytics has emerged as a critical tool for organizations to extract insights from their data and drive business success. This book provides a comprehensive introduction to business analytics, covering the fundamental concepts, techniques, and tools used in the field, and is designed for business professionals, students, and anyone interested in learning how to leverage data to drive business decisions. Through a combination of theoretical foundations, practical examples, and real-world case studies, this book will help readers develop a deep understanding of business analytics and its applications in various industries, including data visualization and communication, statistical modeling and machine learning, data mining and text analytics, big data and cloud computing, and business analytics in practice. Whether you are a seasoned business professional or just starting your career, this book will provide you with the knowledge and skills needed to succeed in today’s data-driven business world, and serve as a valuable resource for anyone interested in business analytics, providing a foundation for further learning and exploration in this exciting field. By exploring the various aspects of business analytics, readers will gain a deeper understanding of how to collect, analyze, and interpret large data sets, and use this information to inform business decisions, drive innovation, and gain a competitive edge in the marketplace.

References

  1. Anute N, Ghewari A, and Raje H (2022) A Study on Positioning Strategies and its Impact on Consumer Behaviour with special reference to Nutritional Products Exclusively meant for Women During Pregnancy, UGC CARE Listed (Group -I) Journal, e-ISSN 2320 –7876, Volume 11, Issue 1, Page no. 253-266.
  2. Davenport, T. H., & Harris, J. G. (2007). “Competing on analytics: The new science of winning”. Harvard Business School Press.
  3. Fitz-enz, J. (2010). “The new HR analytics: Predicting the economic value of your company’s human capital investments”. AMACOM.
  4. Hastie, T., Tibshirani, R., & Friedman, J. (2009). “The elements of statistical learning: Data mining, inference, and prediction” (2nd ed.). Springer.
  5. Khandagale V, Anute N (2021) A Study on Consumer and Retailer Buying Behaviour with Special Reference to Food and Beverage Products, STM Journals, ISSN: 2231-0398 (Online) ISSN: 2347-9949 (Print) Volume 11, Issue 1, Page no- 5-11.
  6. Knaflic, C. N. (2015). “Storytelling with data: A data visualization guide for business professionals”. Wiley.
  7. Lewis, M. (2016). “Marketing data science: Modeling techniques in predictive analytics with R and Python”. Pearson FT Press.
  8. Narayanan, A., & Bhattacharya, A. (2023). “Big data in finance: Data analytics in financial services and banking”. Wiley.
  9. Pearl, J., & Mackenzie, D. (2018). “The book of why: The new science of cause and effect”. Basic Books.
  10. Provost, F., & Fawcett, T. (2013). “Data science for business: What you need to know about data mining and data-analytic thinking”. O’Reilly Media.
  11. Raghupathi, W., & Raghupathi, V. (2014). “Big data analytics in healthcare: Promise and potential”. Health Information Science and Systems, 2(1), 1-10.
  12. Sharda, R., Delen, D., & Turban, E. (2019). “Business intelligence, analytics, and data science: A managerial perspective” (4th ed.). Pearson.
  13. Siegel, E. (2016). “Predictive analytics: The power to predict who will click, buy, lie, or die”. Wiley.
  14. Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2007). “Designing and managing the supply chain: Concepts, strategies, and case studies” (3rd ed.). McGraw-Hill/Irwin.
  15. Winston, W. L. (2014). “Marketing analytics: Data-driven techniques with Microsoft Excel”. Wiley.

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