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Data Science for Web3

You're reading from Data Science for Web3

Product type Book

Published in Dec 2023

Publisher Packt

ISBN-13 9781837637546

Pages 344 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Gabriela Castillo Areco

Table of Contents (23) Chapters

Preface

1. Part 1 Web3 Data Analysis Basics

2. Chapter 1: Where Data and Web3 Meet

3. Chapter 2: Working with On-Chain Data

4. Chapter 3: Working with Off-Chain Data

5. Chapter 4: Exploring the Digital Uniqueness of NFTs – Games, Art, and Identity

6. Chapter 5: Exploring Analytics on DeFi

7. Part 2 Web3 Machine Learning Cases

8. Chapter 6: Preparing and Exploring Our Data

9. Chapter 7: A Primer on Machine Learning and Deep Learning

10. Chapter 8: Sentiment Analysis – NLP and Crypto News

11. Chapter 9: Generative Art for NFTs

12. Chapter 10: A Primer on Security and Fraud Detection

13. Chapter 11: Price Prediction with Time Series

14. Chapter 12: Marketing Discovery with Graphs

15. Part 3 Appendix

16. Chapter 13: Building Experience with Crypto Data – BUIDL

17. Chapter 14: Interviews with Web3 Data Leaders

18. Index

Why subscribe?

19. Other Books You May Enjoy

Appendix 1

1. Appendix 2

2. Appendix 3

Lending and borrowing services on Web3

The bank receives deposits, saves part of them to be able to repay in case the owner wants the cash back, and offers the other part as a loan in the market at a certain interest rate. The loan can be collateralized or non-collateralized, depending on the risk of the loan taker. The interest rate is set high enough to generate revenue for the bank and the depositor. Certain protocols on-chain reproduce a similar mechanism, as we will describe in this section.

At the time of writing, the main lending and borrowing protocols only work with collateralized loans. The process resembles a mortgage where the user provides the house as collateral as insurance for the loan payment. If the loan is not repaid in full, the bank keeps the house. In this case, if the collateral ratio of the loan is not maintained, the protocol liquidates the collateral.

Let’s imagine that Alice wants to invest in a project in USD stablecoin but her collateral is...

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Authors (1)

Gabriela Castillo Areco

Gabriela Castillo Areco holds an M.Sc. in big data science from the TECNUM School of Engineering, University of Navarra. With extensive experience in both the business and data facets of blockchain technology, Gabriela has undertaken roles as a data scientist, machine learning analyst, and blockchain consultant in both large corporations and small ventures. She served as a professor of new crypto businesses at Torcuato di Tella University and is currently a member of the BizOps data team at IOV Labs.

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