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  • Book Overview & Buying Data Science for Web3
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Data Science for Web3

Data Science for Web3

By : Gabriela Castillo Areco
5 (3)
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Data Science for Web3

Data Science for Web3

5 (3)
By: Gabriela Castillo Areco

Overview of this book

Data is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3. You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data. The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients. By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.
Table of Contents (23 chapters)
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1
Part 1 Web3 Data Analysis Basics
7
Part 2 Web3 Machine Learning Cases
15
Part 3 Appendix
1
Appendix 1
2
Appendix 2
3
Appendix 3

Sentiment Analysis – NLP and Crypto News

Natural language processing (NLP) falls under the umbrella of artificial intelligence and is concerned with the comprehension of text by computers. Recent advancements in this field, exemplified by the emergence of tools such as ChatGPT, have become an integral part of our daily lives. Yet, the financial sector has been leveraging NLP for quite some time, particularly for fundamental analysis.

Fundamental analysis seeks to ascertain the intrinsic value of assets such as stocks, tokens, or NFT art based on publicly accessible information. In traditional finance, textual data is sourced from periodic submissions to the SEC (such as Form 10K or 10Q), including financial statements, specialized media news, social media platforms such as X (formerly Twitter), and other avenues. Web3 has fostered a similar environment where market activities are continuous, and X and news platforms serve as predominant textual resources. It’s worth...

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Data Science for Web3
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