About This Textbook¶
📖 About This Textbook¶
Welcome to the Introduction to Data Science — Textbook project.
This open educational resource is designed to provide students, educators, and self-learners with a comprehensive yet practical introduction to Data Science, integrating mathematics, programming, visualization, and real-world problem solving.
🎯 Purpose and Goals¶
Data Science lies at the intersection of mathematics, computer science, and domain expertise.
This textbook aims to:
- Build conceptual clarity in core Data Science principles.
- Provide hands-on examples in Python that can be run and modified locally.
- Serve as a teaching companion for undergraduate or postgraduate Data Science courses.
- Promote open access and reproducibility in data-driven research and learning.
The content is organized to align with academic curricula while remaining accessible for independent learners.
🧩 Structure of the Book¶
The material is divided into thematic parts:
| Part | Focus | Example Chapters |
|---|---|---|
| Part I | Foundations of Data Science | Introduction, What is Data Science, Data Types |
| Part II | Descriptive Statistics and Visualization | Measures of Central Tendency, Dispersion, Graphical Methods |
| Part III | Data Preparation and Processing | Data Cleaning, Integration, Reduction, Transformation |
| Part IV | Modeling and Evaluation | Regression, Classification, Clustering, Model Metrics |
| Part V | Deployment and Applications | Model Evaluation, Generalization, Case Studies |
Each chapter includes: - 📘 Theoretical background
- 🧮 Mathematical formulation
- 🧠 Manual (“by hand”) examples
- 💻 Python code to generate plots and figures
🧑🏫 Intended Audience¶
This material is suitable for:
- Undergraduate and graduate students beginning in Data Science or AI
- Faculty members preparing lecture content or lab exercises
- Professionals and researchers refreshing mathematical and statistical concepts
- Self-learners following an open-source data science curriculum
No prior exposure to advanced machine learning is required, though a basic familiarity with Python, statistics, and data analysis concepts is beneficial.
⚙️ Technical Stack¶
| Component | Purpose |
|---|---|
| MkDocs Material | Website framework and navigation |
| MathJax | LaTeX-style rendering of formulas |
| Mermaid | Flowcharts and concept diagrams |
| Matplotlib + NumPy | Figure generation from Python scripts |
| GitHub Pages | Continuous deployment and versioning |
All figures and diagrams in this textbook are reproducible using the included Python scripts, which are stored alongside each Markdown file.
🌐 Accessibility and Open Use¶
This textbook is distributed under the Creative Commons BY–NC 4.0 License.
You are free to use, share, and adapt the material for non-commercial educational purposes, provided proper attribution is given.
“Knowledge grows when shared. The intent of this project is to make Data Science learning universally accessible.”
👨💻 About the Author¶
Dr. J. M. Reddy
Educator, Researcher, and Developer in Artificial Intelligence & Data Science
- Focus Areas: AI-guided Big Data Analytics, Machine Learning, and Educational Technologies
- Projects include: automated debugging tools, AI-guided medical imaging, and outcome-based education systems
For collaborations or citations, please refer to the repository:
👉 https://github.com/jmreddy2106/Introduction-to-Data-Science-textbook
🔗 Citation¶
If you use or reference this textbook in research, please cite it as:
Reddy, J. M. (2025). Introduction to Data Science — Textbook (Version 1.0).
GitHub Repository: https://github.com/jmreddy2106/Introduction-to-Data-Science-textbook
🧭 Acknowledgments¶
This work draws inspiration from: - University-level Data Science syllabi and open courseware (MIT, Stanford, IITs)
- The open-source scientific Python community
- Contributions from educators, students, and reviewers supporting open education
Special thanks to contributors who tested chapters, generated plots, and refined the visualizations.
💬 Feedback¶
Contributions, corrections, and suggestions are welcome!
Open an issue or submit a pull request on GitHub.
📧 Contact: issues/new on GitHub
Last updated: 2025-10-31