Foundations of Analytics & AI

Use of AI-Assisted Tools

The initial draft of this textbook was written by the author. Subsequent revisions and refinements were supported by digital writing tools, including large language models and grammar-support software, such as Grammarly, Gemini, ChatGPT, and various markdown, text and Python linters. These tools were used to assist with organization, revision, and clarity of exposition. All conceptual content, interpretations, examples, and pedagogical decisions remain the responsibility of the author, who reviewed, edited, and verified all material for accuracy and coherence.

Welcome

Foundations of Analytics & AI is a modular, evolving textbook designed to introduce the core ideas, computational tools, and analytical workflows that underpin modern data-driven decision-making. The text integrates conceptual understanding with hands-on examples. Including:

  • Explanations of key concepts
  • Demonstrations and worked Python examples
  • Short conceptual notes to reinforce essential ideas
  • Mini-labs that integrate skills into complete workflows

The book is structured so readers can progress linearly or for those with some experience, navigate directly to topics of interest.

How This Textbook Is Organized

The material is grouped into three major parts: foundational concepts → computational skills → applied machine learning and AI methods.

Part I — Conceptual Foundations of Analytics & AI

These chapters develop the core ideas that unify analytics, machine learning, and AI systems. They are concept-only and require no coding background.

Part II — Python & Data Foundations

These chapters introduce programming concepts and data skills used throughout modern analytics and AI practice.
They combine explanation with practical Python examples.

Readers already familiar with Python may skim or skip based on experience.

Part III — Machine Learning & Applied AI

(Chapters appear as they are completed.)

Each chapter in this part will integrate:

  1. Conceptual Foundations
  2. Python Implementation
  3. Interpretation & Diagnostics
  4. Mini-Labs

Planned topics include:

  • Supervised learning
  • Regression and classification
  • Model evaluation and diagnostics
  • Overfitting and the bias–variance tradeoff
  • ML pipelines
  • Unsupervised learning and clustering
  • PCA and dimensionality reduction
  • Neural networks and deep learning
  • Convolutional networks
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)

As chapters are released, links will appear here.

Practical Guides

Task-oriented reference pages that support installation, configuration, and technical troubleshooting.

Learning Philosophy

This textbook is built around two reinforcing goals:

AI Literacy

Understanding how analytical and AI systems are structured, why they behave as they do, and how they influence decision-making.

Practical Capabilities

Developing the ability to implement real analytical workflows using Python, diagnostic tools, and modern data processing libraries.

Accessing Code and Data

All example code and supporting data for the text are provided in organized folders:

  • code/ — downloadable Python scripts and examples
  • data/ — datasets used in demonstrations and mini-labs

Download the full repository or browse files individually.