1  What Is Data Science?

Data science is the practice of learning from data. It brings together three pillars—computation, statistics, and domain knowledge—to create insights that help us understand, predict, and improve the world around us. You have already seen data science in action many times without noticing it.

This chapter introduces what data science is and what it is not, setting the stage for the hands-on tools you will learn in Part I.

1.1 Data Science Is About Asking Questions

Every data science project begins with a question.

Why has traffic increased on my school’s street?
Do certain neighborhoods file more noise complaints?
Which players contributed most to a team’s winning season?

Data science helps translate these questions into something we can analyze, measure, and explain.

1.2 Data Science Uses Tools, Not Just Math

While math is important for deeper understanding, data scientists spend much of their time using tools—command line, Git, VS Code, Quarto, and programming languages such as Python or R. These tools help you:

  • load and clean data
  • organize your work
  • build reproducible workflows
  • create visualizations
  • share results with others

This book starts with tools because tools give you the power to actually do data science. Once you know how to work with your computer the way data scientists do, everything else becomes easier.

1.3 Data Science Is Reproducible

A key idea in this book is reproducibility. A reproducible workflow is one where:

  • your analysis is neatly organized
  • every step is documented
  • results can be reproduced exactly by you or anyone else

This is why you will learn Quarto for writing reports and Git for version control. These skills turn your projects into something professional, even as a beginner.

1.4 Data Science Is Interdisciplinary

To answer real-world questions, you combine:

  • computing — writing code to process data
  • statistics — measuring uncertainty and learning patterns
  • domain knowledge — understanding the context: climate, health, sports, education, city systems, and many others

None of these pillars works alone. Together they create meaningful insights.

1.5 What You Will Learn Next

Part I of this book focuses on building your foundation:

  • using the command line
  • navigating your computer
  • creating projects with clean structure
  • using VS Code as your editing home
  • tracking your work with Git
  • writing analyses with Quarto
  • learning your first programming language (Python)

By the end of Part I, you will have built your first complete data science project: a real report, with real data, that you can publish or share.

Part II takes you through case studies in health, environment, sports, and other areas—helping you discover patterns in real datasets and understand how data science works in practice.

Data science is not something you learn in one day. It is a craft you build over time. This book gives you the tools and the mindset to begin that journey the right way.