Data Science on Blockchains
What you'll learn
- Learn how the blockchain technology, Web3 and decentralized finance works
- Learn to parse the data from Bitcoin and Ethereum to develop machine learning models on the data
- Mine blockchain data for price prediction
- Mine blockchain data for ransomware payment, darknet market payment and pump/dump scheme detection
- Track investor behavior on multiple DeFi networks on Ethereum
- Python, R or Java programming
- Basic concepts in graph analysis
Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed an unprecedented attention. As Blockchain applications proliferate, so does the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in the information sciences. Although there is a vast quantity of information available, the consequent challenge is to develop tools and algorithms to analyze the large volumes of user-generated content and transactions on blockchains, to glean meaningful insights from Blockchain data. The objective of the course is to train students in data collection, modeling and analysis for blockchain data analytics on public blockchains, such as Bitcoin, Litecoin, Monero, Zcash, Ripple, and Ethereum.
Expectations and Goals
We will teach all core blockchain components with an eye towards building machine learning models on blockchain data. Students will be able to achieve the following learning objectives at the completion of the course.
Learn the history of digital currencies and problems that prevented their adoption. What are the real-life use cases of Blockchain? How Blockchain differs from earlier solutions?
Learn the concepts of consensus and proof-of-work in distributed computing to understand and describe how blockchain works.
Learn data models for addresses, transactions and blocks in cryptocurrencies and Blockchain platforms.
Use Java Python and R to extract blockchain blocks and store the transaction network on Bitcoin, Ripple, IOTA and Ethereum blockchains.
Model weighted, directed multi-graph blockchain networks and use graph mining algorithms to identify influential users and their transactions.
Predict cryptocurrency and crypto-asset prices in real time.
Extract and mine data from smart contracts on the Ethereum blockchain.
Who this course is for:
- Data scientists
- Machine learners
- Graduate students
- Blockchain analysts
- Blockchain engineers
Cuneyt Gurcan Akcora is an Assistant Professor of Computer Science and Statistics at the University of Manitoba, Canada. He received his M.S. from State University of New York at Buffalo, the USA, and his Ph.D. from the University of Insubria, Italy. His primary research interests are Data Science on complex networks and large-scale graph analysis, with applications in social, biological, IoT and Blockchain networks. He is a Fulbright Scholarship recipient, and his research works have been published in leading conferences and journals including IEEEtran, NeurIPS, VLDB, ICDM, SDM, IJCAI, and ICDE.
Dr. Murat Kantarcioglu is an Ashbel Smith Professor in the Computer Science Department and Director of the Data Security and Privacy Lab at The University of Texas at Dallas (UTD). He held visiting positions at Harvard Data Privacy Lab and UC Berkeley.
He is the recipient of various awards including NSF CAREER award, the AMIA (American Medical Informatics Association) 2014 Homer R Warner Award and the IEEE ISI (Intelligence and Security Informatics) 2017 Technical Achievement Award for his research in data security and privacy. He is also a fellow of IEEE, AAAS and a distinguished member of ACM.
Yulia R. Gel is Professor in the Department of Mathematical Science at the University of Texas at Dallas and Visiting Scientist at Lawrence Berkeley National Lab. Her research interests include topological and geometric methods for complex networks, blockchain data analytics, statistical foundation of data science, time series analysis, and predictive modeling. She holds a Ph.D in Mathematics, followed by a postdoctoral position in Statistics at the University of Washington. Prior to joining UT Dallas, she was a tenured faculty member at the University of Waterloo, Canada. She also held visiting positions at Johns Hopkins University, University of California, Berkeley, and the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK.
Her research has been continuously supported by grants from ONR, NASA, NSF, DOE, as well industrial subcontracts. She is a co-author of more than 120 peer-reviewed publications, and her research has been recognized by multiple best paper awards from the American Statistical Association. She served as a Vice President of the International Society on Business and Industrial Statistics (ISBIS), and is a Fellow of the American Statistical Association. She also holds the Distinguished Achievement Medal from the American Statistical Association Section on Statistics and the Environment.