After successful completion of the course, students are able to apply the following competences:

1. Explaining distributed ledger technology and its associated cryptoassets, including their underlying principles, history, and emerging trends.
2. Distinguishing different types of cryptoassets based on their function and technical characteristics, such as utility tokens, security tokens, and stablecoins.
3. Applying fundamental analysis algorithms on cryptoasset transaction datasets to identify market trends and patterns, as well as to evaluate the performance of specific cryptoassets.
4. Implementing specific cryptoasset analytics tasks using open source tools, including data visualization, statistical analysis, and machine learning algorithms.
5. Explaining the features of privacy-centric cryptocurrencies and analyzing their transaction data using specialized tools and techniques.
6. Analyzing footprints of off-chain payment channels in blockchains and assessing their impact on the overall network performance and security.
7. Presenting cryptoasset application use cases and scenarios to showcase the practical applications of blockchain technology in different industries.
8. Designing and implementing their own cryptoasset analytics tasks, including collecting and processing data, selecting appropriate methods, and presenting their findings in a clear and concise manner.

• Cryptoassets and Distributed Ledger Technology Recap
• Fundamental Cryptocurrency Analytics Methods
• Analysis of Mixing Services & Privacy-Centric Cryptocurrencies (Monero, Zcash, etc.)
• Analysis of Smart Contracts and Token Systems
• Analysis of Decentralized Finance (DeFi) Protocols
• Analysis of Cross-layer and Cross-ledger solutions

• Lectures
• Weekly homework assignments (Paper reading and programming tasks)
• Presentations
• Student project (specific data analytics task)

This course features two parts: the first part (beginning of the semester) will feature lectures held by the instructor, invited talks, and weekly homework assignments, mostly in-class presentation of related work and literature. In the second part, students will build on learned analytics methods and techniques and work on a defined project in the field of crypto asset analytics.

Grading

• Weekly Homework 50%
• Student Project 50%

ECTS Breakdown:
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12h Lecture

13h Self-Study, Readings and Homeworks

50h Project

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75h = (3 ECTS)

• Programming and analytics skills (e.g., Python).
• Basic Knowledge of Bitcoin and Cryptocurrency Techniques (e.g., passing “VU 192.065 Cryptocurrencies”)
• Basic knowledge of network analytics and machine learning techniques (supervised, unsupervised)