Alternative Data, Cryptocurrencies & Blockchains

(3 Credits)

This full-semester elective course explores techniques for handling alternative data in quantitative and fundamental investment processes, alongside the technologies and concepts behind distributed ledger systems and crypto financial markets. Students will learn quantitative tools for alternative data, including identifier mapping, stable panel creation, dataset evaluation, and sensitive information extraction, using Python-based statistical and machine-learning techniques. Additionally, the course covers the structure of traditional banking and money creation, cryptographic principles, blockchain security challenges, and the mechanics of Bitcoin, Ethereum, and decentralized finance (DeFi). Real-world datasets, including blockchain data, will be analyzed to develop investment insights and tradable signals. Proficiency in Python and experience with the numpy/pandas/sci-kit-learn stack are essential.