Data Mining and Business Intelligence

Data mining is crucial in business intelligence as it enables organizations to extract valuable insights and patterns from vast datasets, ultimately supporting informed decision-making, enhancing operational efficiency, and driving strategic growth. Validations, model building and interpretations are accomplished through databases, data warehouses, various supervised and unsupervised algorithms, tools for data modeling, descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics to ensure accurate decision-making.

This book systematically explores the core concepts and techniques of data mining and business intelligence. It begins by introducing fundamental principles and key methodologies, including regression, classification, association rule mining, and clustering. The text progresses to cover business intelligence architectures, data warehousing, and essential practices like data modeling, dashboard design, and data visualization using tools like Power BI. Furthermore, it delves into advanced topics such as text mining, big data analytics, and the ethical considerations surrounding data mining and business intelligence, ensuring a well-rounded understanding.

Upon completing this book, readers will be competent in understanding various pre-processing techniques, applying appropriate data mining algorithms to large data sets, and conducting data analysis and interpretation to derive meaningful insights. They will also gain skills in data modeling and visualization to effectively communicate findings to business leaders and policymakers. Additionally, readers will develop an understanding of ethical considerations in data practices.

WHAT YOU WILL LEARN

* Conducting pre-processing of data, applying appropriate algorithm to generate model summary and communicating the result effectively.
* Master data mining, BI principles, regression, classification, association rules, and clustering.
* Design BI architectures, ETL processes, data warehouses, and effective data visualizations.
* Utilize Power BI for data modeling, dashboard design, and create compelling data visualizations.
* Explore text mining, big data analytics, and the ethical dimensions of data practices.
* Implement regression, classification, association rule mining, and clustering techniques.
* Develop expertise in data mining, business intelligence, and ethical data application.

WHO THIS BOOK IS FOR

This textbook is written for a wide range of audiences, including professionals such as data analysts, business managers, IT specialists, analytics professionals, and researchers seeking to enhance their understanding of data-driven decision-making. It is also valuable for students who want to establish foundational knowledge in data mining and business intelligence.

1. Introduction to Data Mining and Business Intelligence
2. Regression and Classification Techniques with Applications
3. Concept and Application of Association Rule Mining Algorithm
4. Clustering
5. Introduction to Business Intelligence
6. Business Intelligence Architecture, Query and Reporting Practices
7. Advanced Data Mining and Business Intelligence Techniques
8. Data Mining and Business Intelligence Ethical Framework

Dr. Jyotiranjan Hota is a distinguished academic with nearly 20 years of teaching, research and software consulting experience. He holds a B.E. in computer science and engineering from NIT Rourkela, a PGDBM from Xavier Institute of Management, Bhubaneswar, and a Ph.D. in Management Studies from Aligarh Muslim University, which was a joint program with AIMA, New Delhi. His areas of expertise includes business analytics, artificial intelligence, machine learning, data mining, text mining, visual analytics, and functional modules of SAP S4/HANA (SD, MM, PP, and FI-CO). He is also proficient in programming languages like R and Python and conversant with tools such as KNIME and Power BI. Dr. Hota's Ph.D. research delved into the adoption of Multivendor ATM technology in India. He analyzed challenges and opportunities from the perspectives of customers, suppliers, and bankers. Through this work, he developed and validated several qualitative and quantitative models addressing the drivers and barriers to technology adoption.

His broader interests focus on integrating information technology across various functional areas of management. An AIMA-accredited management teacher in the IT domain, Dr. Hota has an extensive record of publications in prominent top journals, including the International Journal of Bank Marketing, Asia Pacific Journal of Information Systems, International Journal of Management in Education, and The IUP Journal of Applied Economics, among others. He has also contributed to works by leading publishers such as Sage, Springer and Palgrave Macmillan. Dr. Hota has actively participated in international conferences in India and abroad, serving in key roles such as program committee member, advisory board member, technical committee member, track chair, and session chair. In recognition of his academic contributions, he was awarded with the ICBM-AMP Academic Excellence Award 2018 in the Best Professor in IT and Operations category in Hyderabad, India.