Introduction to Data Science
From Curiosity to Code — Your First Steps in the Science of Data
Author: DataField.Dev
First Edition — March 2026
License: Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA-4.0)
You are free to share, adapt, and build upon this work, even commercially, as long as you give appropriate credit and distribute your contributions under the same license. Full license text: https://creativecommons.org/licenses/by-sa/4.0/
About This Book
This is the free data science textbook that assumes nothing — no Python, no statistics, no SQL — and takes you from "I keep hearing about data science but have no idea where to start" to "I just built my first machine learning model and I can explain what it does."
36 chapters. 6 parts. Approximately 1,400 pages.
From your first line of Python to evaluating a random forest classifier, this book walks you through the complete journey of becoming a practicing data scientist. Every chapter includes hands-on exercises with real datasets, worked examples you can run in Jupyter notebooks, and progressive contributions to a public health data analysis project — a portfolio-ready investigation of global health patterns using WHO and CDC data that you build from the ground up.
Series Context
This is Book 1 in the DataField.Dev data science series:
| Book | Focus |
|---|---|
| Book 1 — Introduction to Data Science (this book) | From zero to your first models: Python, pandas, visualization, statistics, and introductory machine learning |
| Book 2 — Intermediate Data Science (forthcoming) | Deep machine learning, feature engineering, production pipelines, and advanced statistical methods |
What You'll Need
- A computer (Windows, macOS, or Linux)
- An internet connection (for downloading tools and datasets)
- High school algebra (variables, basic functions, reading graphs)
- Curiosity and a willingness to learn by doing
No prior programming experience. No statistics beyond basic percentages and averages. No calculus. No linear algebra. This book meets you where you are.
Technical Specifications
- Python version: 3.12+
- Primary environment: Jupyter Notebook / JupyterLab
- Key libraries: pandas, matplotlib, seaborn, plotly, scipy, scikit-learn
- Datasets: Real-world public health data (WHO, CDC), plus curated practice datasets
- Build system: mdBook / Quarto compatible
This textbook was generated with AI assistance and curated by human editors. All code examples have been tested. All datasets are real or explicitly labeled as illustrative. See the Acknowledgments for details.