FutureCentral > Business Analytics, Data Science, and AI: From Foundations to Autonomous Agents
Business Analytics, Data Science, and AI book cover
Business Analytics & Programming · MBA / Postgraduate

Business Analytics, Data Science, and AI

From Foundations to Autonomous Agents

Austin PM · FutureCentral Press

From analytical foundations to autonomous agents.

MBA / PostgraduateBook in development
Explore the bookInquire about this bookView contents
AudienceMBA / Postgraduate
Structure20 chapters listed
StatusBook in development

About the book

Business problem framing guides the planned contents of this developing book. Classical analytics and machine learning lead into deep learning, language models, retrieval, agents, and responsible implementation.

What you will learn

  • Frame business problems for analytics
  • Compare analytical and machine-learning methods
  • Evaluate language-model and agentic applications
  • Design a responsible implementation roadmap
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Table of contents

View all 20 chapters

The chapter structure is in development and may change.

  1. The Data Science Imperative for Business
  2. Data Thinking: Framing Business Problems as Data Problems
  3. Data Foundations: Types, Sources, and Quality
  4. Descriptive Analytics: Seeing Patterns in Data
  5. Probability, Inference, and Hypothesis Testing for Managers
  6. Regression and Predictive Modelling
  7. Classification, Clustering, and Unsupervised Learning
  8. Time Series Analysis and Forecasting
  9. The Machine Learning Pipeline: From Data to Deployed Model
  10. Deep Learning: Neural Networks for Business Applications
  11. Natural Language Processing and Text Analytics
  12. The Transformer Revolution and Large Language Models
  13. Prompt Engineering and Retrieval-Augmented Generation
  14. Generative AI for Data Science and Business Functions
  15. From Chatbots to Agents: The Agentic AI Paradigm
  16. Agentic AI in the Enterprise: Workflows, Orchestration, and Governance
  17. Data Strategy and the AI-Ready Organisation
  18. Responsible AI: Ethics, Bias, Fairness, and Regulation
  19. Industry Applications: Data Science Across Domains
  20. Capstone: Building Your Data Science and AI Roadmap
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