Data Science Techniques for Cryptocurrency Blockchains
About this ebook
The book takes the reader through basic data exploration topics, proceeding systematically, method by method, through supervised and unsupervised learning approaches and information visualization techniques, all the way to understanding the blockchain data from the network science perspective.
Chapters introduce the cryptocurrency blockchain data model and methods to explore it using structured query language, association rules, clustering, classification, visualization, and network science. Each chapter introduces basic concepts, presents examples with real cryptocurrency blockchain data and offers exercises and questions for further discussion. Such an approach intends to serve as a good starting point for undergraduate and graduate students to learn data science topics using cryptocurrency blockchain examples. It is also aimed at researchers and analysts who already possess good analytical and data skills, but who do not yet have the specific knowledge to tackle analytic questions about blockchain transactions. The readers improve their knowledge about the essential data science techniques in order to turn mere transactional information into social, economic, and business insights.
About the author
Innar Liiv is Associate Professor of Data Science at Tallinn University of Technology. He also belongs to the Future of Public e-Governance expert group at the Foresight Centre at the Parliament of Estonia. He was previously a Cyber Studies Visiting Research Fellow (2016-2017) and a Research Associate (2018-2020) at the University of Oxford, a Visiting Scholar at Stanford University (2015), and a Postdoctoral Visiting Researcher at the Georgia Institute of Technology (2009). His research interests include data science, financial technology, social network analysis, information visualization, computational international relations, and big data technology transfer to industrial and governmental applications. Innar Liiv has won the Classification Society Distinguished Dissertation Award 2009.
