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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

Introducing machine learning

The definition of machine learning, as provided by Computer Science Wiki, is “a field of inquiry devoted to understanding and building methods that “learn” – that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.”

(Source: https://computersciencewiki.org/index.php/Machine_learning)

Professor Jason Brownlee defines deep learning as “a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.” Deep learning is distinguishable from other machine learning methods because it uses artificial neural networks as a basis for its methods.

The relationship between these two fields...

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Data Science for Web3
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