192.080 Crypto Asset Analytics
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023S, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

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.

Subject of course

  • 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

Teaching methods

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

Mode of examination


Additional information

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.


  • Weekly Homework 50%
  • Student Project 50%



Course dates

Wed17:00 - 19:0008.03.2023EI 1 Petritsch HS Course Intro + Cryptoassets Recap
Wed17:00 - 18:3015.03.2023 Zoom Meeting (LIVE)Analyzing UTXO Ledgers
Wed17:00 - 18:3022.03.2023 Zoom Meeting (LIVE)The Limits of Known Methods: Coin Mixing & Privacy Coins
Wed17:00 - 18:3029.03.2023 Zoom Meeting (LIVE)Going Beyond UTXO: Ethereum, Smart Contracts, Tokens, Account-Model Ledgers
Wed17:00 - 19:0019.04.2023EI 3 Sahulka HS - UIW Guest Lecture (Thomas Goger, Central Office for Cybercrime, Bavaria)
Wed17:00 - 18:3026.04.2023 Zoom Meeting (LIVE)Analyzing Decentralized Finance (DeFi) Protocols
Wed17:00 - 18:3003.05.2023 Zoom Meeting (LIVE)Analyzing Transactions Across Layers and Ledgers
Wed17:00 - 18:3010.05.2023 Zoom Meeting (LIVE)Projects | Overall Approach & Initial Design Presentation
Wed17:00 - 18:3007.06.2023 Zoom Meeting (LIVE)Projects | Intermediate Results Presentation
Wed17:00 - 20:0028.06.2023EI 1 Petritsch HS Projects | Final Results Presentation + Best Student Project Award

Examination modalities

ECTS Breakdown:

12h Lecture

13h Self-Study, Readings and Homeworks

50h Project

75h = (3 ECTS)

Course registration

Begin End Deregistration end
24.02.2023 10:00 06.03.2023 23:59 15.03.2023 23:59



No lecture notes are available.

Previous knowledge

  • 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)

Preceding courses