Foreword. From Data Abundance to Decision Excellence in the Age of Generative AI
Sets the book's central thesis: analytics creates value only when it improves decisions, and AI raises the importance of human judgment.
Modern Business Analytics with Artificial Intelligence
Author Armando Vieira, Tartu University
Open Full BookChapter 1. Introduction to Business Analytics
Builds the core definition of business analytics, its value in organizations, and the analytics lifecycle used throughout the book.
Chapter 2. Business Analytics in End-to-End Processes and Workflows
Explains how analytics fits into business workflows from data capture to decisions and operational execution.
Chapter 3. Data Foundations for Business Analytics
Covers data types, data quality, preparation principles, and governance foundations needed for reliable analytics.
Chapter 4. Statistical and Probabilistic Foundations for Business
Introduces descriptive statistics, uncertainty, and probability concepts that support evidence-based business decisions.
Chapter 5. The Four Pillars of Analytics: Descriptive to Prescriptive
Compares descriptive, diagnostic, predictive, and prescriptive analytics and when each method delivers value.
Chapter 6. Data Visualization and Storytelling for Decision-Makers
Focuses on effective charts, executive communication, and narrative techniques for persuasive data storytelling.
Chapter 7. Working with Python in Cloud-Based Environments
Walks through practical Python workflows in online notebooks and cloud tools for collaborative analytics work.
Chapter 8. Data Preparation and Feature Engineering in Python
Shows how to clean, transform, and engineer features so machine learning models can perform effectively.
Chapter 9. Machine Learning for Business Analytics: Concepts and Workflow
Introduces the end-to-end machine learning process, from framing the problem to evaluating business impact.
Chapter 10. Classification Models for Business Decisions
Covers classification techniques and use cases such as churn prediction, risk labeling, and customer targeting.
Chapter 11. Regression Models for Forecasting and Estimation
Explores regression methods for continuous outcomes, scenario analysis, and planning decisions.
Chapter 12. Clustering and Segmentation for Business Insight
Demonstrates unsupervised learning for grouping customers, products, or operations into actionable segments.
Chapter 13. Using LLMs in Business Analytics
Examines large language model applications, prompt workflows, and governance considerations in analytics teams.
Chapter 14. Forecasting Methods for Business Planning
Presents forecasting methods and model selection approaches for planning demand, budgets, and capacity.
Chapter 16. Leveraging AI in Business Analytics: Augmentation vs. Automation
Distinguishes augmentation from automation and maps where AI should support analysts versus replace manual tasks.
Chapter 17. AI Agents Concepts, Architectures, and Use Cases
Introduces AI agent patterns, architecture choices, and practical enterprise use cases for autonomous workflows.
Chapter 18. Integrating Analytics and AI into Strategy and Operations
Shows how to align analytics and AI capabilities with strategy, operations, and measurable organizational outcomes.
Chapter 19. Real-World Case Studies Across Industries
Provides cross-industry case studies highlighting successful implementation patterns and common pitfalls.
Chapter 20. The Future of Business Analytics in AI-Driven Organizations
Looks ahead at future capabilities, organizational shifts, and emerging technology trends shaping business analytics.