Khan A. Business Intelligence and Data Analysis in the Age of AI 2025

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Khan A. Business Intelligence and Data Analysis in the Age of AI 2025

Textbook in PDF format Unlock data-driven decision-making with Business Intelligence and Data Analysis in the Age of AI. This book embarks on a journey through the evolving landscape of Business Intelligence (BI) and data analysis, exploring the intersection of traditional methodologies with cutting-edge Artificial Intelligence (AI) technologies. The book begins with an introduction to BI, then explores the analytics process and essential tools. Chapters on understanding data, big data, and data integration provide insights into managing and effectively leveraging vast amounts of information. Additionally, topics such as governance, security, privacy, and ethics shed light on the ethical considerations and regulatory frameworks surrounding data usage. As we delve deeper, we explore the intricacies of data warehousing, data lakes, extraction, transformation, and loading (ETL) and data design. We will introduce you to the benefits and application of programming languages like R, SQL, and Python, which are extensively used for data manipulation and analysis. The journey continues with chapters dedicated to reporting in BI, cloud computing, and data visualization, emphasizing the importance of presenting data in meaningful and accessible formats. Furthermore, we explore predictive analytics, unlocking the potential to forecast future trends and outcomes based on historical data. In the latter part of the book, we examine advanced topics such as fundamental and advanced insights in data analysis. Whether you are a seasoned data professional or a novice navigating the world of BI and data analysis, this book serves as a comprehensive guide to understanding, implementing, and leveraging AI in the age of data-driven decision-making. Join us on this enlightening journey as we unravel the intricacies of BI and data analysis in the era of AI. Preface Introduction to Business Intelligence Analytics Process and Tools Understanding Data Big Data Data Integration and Quality Governance, Security, Privacy, and Ethics Data Warehousing Data Lakes Extraction, Transformation, and Loading (ETL) Data Design R, SQL, and Python Reporting in BI Cloud Computing Data Visualization Predictive Analytics BI and Data Analysis Data Analysis: Fundamentals and Analytics Data Analysis: Advanced Insights AI in BI and Data Analysis AI in BI and Data Analysis: Risks, Ethics, and Future[b][/b]