📘 Introduction to Data Science — Textbook¶

A compact, classroom-ready textbook with theory, by-hand examples.

🎯 What you’ll learn¶

• Solid foundations of Data Science: concepts, math, and workflow
• Visualization: histograms, scatter, KDE, Q–Q, boxplots, multivariate charts
• Modeling: regression, classification, clustering, evaluation, cross-validation
• Data preparation: cleaning, integration, reduction, transformation, discretization
• Hands-on: Python code to generate figures used in the chapters

Data Science Overview¶

graph TD
    A[Data Science] --> B[Prior Knowledge]
    A --> C[Data Preparation]
    A --> D[Modeling]
    A --> E[Evaluation]
    A --> F[Deployment]
    C --> C1[Cleaning]
    C --> C2[Integration]
    C --> C3[Reduction]
    D --> D1[Regression]
    D --> D2[Classification]
    D --> D3[Clustering]

🗂️ How to use this site¶

• Use the left navigation to jump between chapters.
• Each chapter includes formulas (MathJax), diagrams (Mermaid/PNG), and code snippets to reproduce figures.
• Downloadable .md and .png are available where relevant for GitHub Pages hosting.

💡 Tip: Use the search box (⌘/Ctrl + K) to find formulas, terms, or figure names instantly.

📚 Chapters¶

• Foundations
• Chapter 1 — Introduction
• Chapter 2 — What is Data Science
• Chapter 3 — Data Objects and Attribute Types

• Chapter 4 — Basic Statistical Descriptions of Data

• Visualization

• Chapter 5 · Chapter 6 · Chapter 7 · Chapter 8

• Process & Preparation

• Chapter 9 · Chapter 10 · Chapter 11 · Chapter 12 · Chapter 13

• Modeling & Evaluation

• Chapter 14 · Chapter 15 · Chapter 16 · Chapter 17

🖼️ Plot Gallery (quick preview)¶

Quantile	Histogram	Scatter

Multi-Scatter	Scatter Matrix	Bubble

KDE	Q–Q	Parallel Coordinates

Deviation	Andrews Curves	Box