Introduction to Computational Archaeology

This book is designed to help archaeologists develop foundational and applied skills in data analysis and visualization using programming languages like Python and R. Whether you are working with excavation records, environmental data, radiocarbon dates, or artifact distributions, this book will guide you through hands-on, project-based exercises grounded in real-world archaeological questions.

Who Is This Book For?

This book is intended for students, researchers, or professionals in archaeology who want to:

  Develop essential data literacy and computational thinking
Use programming tools to clean, analyze, and visualize archaeological data
Apply statistical models to real-world excavation or survey data
Understand archaeological networks, typologies, and temporal trends

What Will You Learn?

Over the course of 12 chapters, you will explore topics such as:

  Sets, relations, and structuring archaeological data in Python
Probability, exploratory data analysis, and Bayesian inference
Decision trees, feature engineering, and categorical data modeling
Network visualization, GIS concepts, and spatial analysis
Regression, hypothesis testing, Monte Carlo simulations
Time series analysis and forecasting in archaeological research

Why Does This Matter?

  Important: Archaeology is increasingly data-driven. From spatial modeling to typological classification, today’s archaeologists must be able to work confidently with datasets, digital tools, and visualizations. This book provides the practical skills to make sense of the complex, multi-dimensional data encountered in the field, lab, and archive.

How Is the Book Structured?

Each chapter includes:

  A clear overview of the concepts and tools covered
Examples relevant to archaeological fieldwork and analysis
Hands-on exercises using Python and R
A tutorial that builds toward your capstone projects
A short activity at the end of each section

Getting Started

🧭 Activity: Prepare Your Toolkit

To begin, please install the following tools:

Python 3.x – preferably with Anaconda or Jupyter Notebooks
R and RStudio – for Chapters 8–12
VS Code or another text editor for working with Python files
Download the example archaeological datasets provided in Chapter 1

Once your environment is set up, proceed to Chapter 1: Sets, Relations, and Data Structures in Python.