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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 deep learning

In Part 2 of this book, we will also use deep learning methodologies when solving the use cases. Deep learning models employ multiple layers of interconnected nodes called neurons, which process input data and produce outputs based on learned weights and activation functions. The connections between neurons facilitate information flow, and the architecture of the network determines how information is processed and transformed.

We will study three types of neural network architectures in detail in their corresponding chapters. For now, let’s introduce the framework and terminology that we will use in them.

The neuron serves as the fundamental building block of the system and can be defined as a node with one or more input values, weights, and output values:

Figure 7.9 – A neuron’s structure

Figure 7.9 – A neuron’s structure

When we stack multiple layers with this structure, it becomes a neural network. This architecture typically consists...

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Data Science for Web3
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