AI-Powered Business Analytics:

From Data to Decisions
1. Auflage 2026
ISBN: 978-1-041-13839-6
Verlag: Taylor & Francis

Buch, Englisch, 196 Seiten, Format (B × H): 156 mm x 234 mm

This book offers an integrated framework that actively leverages AI-driven analytics, empowering businesses to transform raw data into strategic insights, actionable intelligence, and informed decision-making. It expertly covers core AI techniques and presents real-world case studies from diverse industries. Furthermore, the book provides step-by-step methodologies for constructing effective AI analytics solutions, guiding readers through the practical application of these powerful tools.

The author empowers the reader to adopt strategies that actively increase predictive accuracy, automate complex analyses, and improve business processes, ultimately leading to better outcomes. The clear and structured chapters facilitate understanding, starting with the basics and progressing to advanced machine learning algorithms, equipping readers with hands-on skills for future practical applications. Key topics covered include neural networks, deep learning fundamentals, and convolutional and recurrent networks. The book illustrates this essential knowledge with real-world applications for solving complex business problems. Furthermore, it delves into Natural Language Processing (NLP) methods, sentiment analysis, text classification, and document analytics within the context of business cases like customer feedback analysis and market intelligence. By actively engaging with the material, the reader will learn and understand the tools for both predictive and prescriptive analytics, gaining valuable competitive insights and enhanced strategic decision-making capabilities. This comprehensive approach ensures readers can confidently apply AI to drive tangible business improvements.

This book is targeted at upper-level undergraduates and graduates in Business Analytics, Data Science, AI in Business, MBA in Analytics, and Digital Transformation. Industry professionals like business analysts, data analysts, data scientists, AI specialists, and decision-makers seeking strategic AI analytics implementation will also benefit.

Zielgruppe

Professional Reference, Undergraduate Advanced, and Undergraduate Core

Autoren/Hrsg.

Usman Tariq, Muhammad

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

1. Introduction to AI in Business Analytics 2. Foundations of Business Analytics and AI 3. Data Collection, Integration, and Management 4. Machine Learning in Business Analytics 5. Deep Learning for Advanced Analytics 6. Natural Language Processing and Text Analytics 7. Predictive Analytics and Forecasting Techniques 8. Prescriptive Analytics and Decision Optimization 9. Visualization, Reporting, and Communication of Insights 10. Future Trends and Ethical Considerations in AI Analytics

Über Autor(innen)

Muhammad Usman Tariq, is a distinguished professional with over sixteen years of experience in both academia and industry, focusing on the integration of artificial intelligence (AI) and emerging technologies. As a leading expert in research and case studies, Dr. Tariq has worked in private and public sectors across the United States, Asia, and the Middle East. Currently he is Associate Professor of Business Analytics at the Abu Dhabi University.