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 Decentralized Finance, 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.