Blockchain Fundamentals, Data Structures, and Algorithms for Data Science

Offering a systematic exploration of blockchain networks from both technical and analytical viewpoints, this book introduces the core structures that underpin blockchain systems, transactions, addresses, and smart contracts and explains how these can be modeled, visualized, and analyzed using modern data science methods. Bridging computer science, finance, and statistics, it integrates algorithmic reasoning with economic intuition to study decentralization, risk, and trust in digital economies. Through examples drawn from Bitcoin, Ethereum, Ripple, Monero, Zcash, IOTA, and DeFi, readers learn how blockchain data can be transformed into graph and temporal models for fraud detection, systemic risk analysis, and network behavior prediction. Featuring clear explanations, illustrative figures, and Solidity code, this volume serves as an essential reference for students, researchers, and practitioners in finance, data science, statistics, machine learning, and distributed systems.

Cüneyt Gürcan Akçora is Associate Professor of Finance with a joint appointment in Computer Science at the University of Central Florida in Orlando. He is also a member of the university's AI Institute. His research focuses on data science for blockchains, integrating graph machine learning, temporal modeling, and network theory to study risk, fraud, and systemic behavior in decentralized finance.

Murat Kantarcioglu is Professor and CCI Faculty Fellow in the Department of Computer Science at Virginia Tech. He is also a Faculty Associate at Harvard University's Data Privacy Lab. He is a fellow of the American Association for the Advancement of Science. His research focuses on AI for cybersecurity and cybersecurity for AI, integrating data science and blockchain technologies to design secure and efficient data systems.

Yulia R. Gel is Professor of Statistics at Virginia Tech. She is a Fellow of the American Statistical Association and a recipient of the NSF Director's Award for Superior Accomplishment. Her research focuses on the mathematical and statistical foundations of data science, risk quantification, and topological and geometric methods for blockchain data analytics.