Note: full code to this article can be found on GitHub.

Previously I shared how to access blockchain data for DeFi projects using various centralized APIs like DeFi Llama and decentralized APIs like The Graph. This time around we’re going to query the blockchain directly using the Web3.py Python library.

Why?

Web3.py is a Python library built for interacting with the Ethereum blockchain. With it we can build all sorts of core functionality for decentralized applications. We can interact with smart contracts directly, gather blockchain data, and send transactions. If you’re not a Python user, there are libraries for other…

Note: nothing herein is financial advice. Crypto projects are inherently associated with risk, largely unaudited, and experimental in nature. No charts, images, statistics, or observations herein should be used for investment decisions.

In this article we’ll access these APIs while providing sample code for how to work with REST, GraphQL, and WebSockets in Python. This sample code will be applicable across most APIs you encounter.

You can find the sample code here: GitHub

Additionally, these are the sources I use for data. My use-case is primarily fundamental research and monitoring activity on these protocols. Do your own research and find…

Note: nothing herein is financial advice. Crypto projects are inherently associated with risk, largely unaudited, and experimental in nature. No charts, images, statistics, or observations herein should be used for investment decisions.

You can find the full code associated with this article here: GitHub Repo.

This article should act as an introduction for studying crypto analytics. You’ll learn some ways to access data, manipulate data, and produce insightful findings.

First, we’ll make some assumptions on what drives activity in these networks. Next, we’ll dive into the data to try and (dis)prove some of these statements. …

Note: I am not affiliated with any of the writers in this article. These are simply books and essays that I’m excited to share with you. There are no referrals or a cent going in my pocket from the authors or publishers mentioned; I prefer to align my incentives with the reader rather than the publishers. Reading is a vitamin for the brain; please support your favorite writers and enjoy!

Welcome to another month of our Data Science reading list! We do these each month, and I love all the positive feedback. I especially like when folks share their own…

Note: I am not affiliated with any of the writers in this article. These are simply books and essays that I’m excited to share with you. There are no referrals or a cent going in my pocket from the authors or publishers mentioned. Reading is a vitamin for the brain; please support your favorite writers and enjoy!

Welcome to this month’s reading list. …

Applied AI continues to accelerate, largely fueled by the maturation of tooling and infrastructure. Couple this infrastructure with a strong pool of talent and enthusiasm, readily accessible capital, and high customer willingness to adopt AI/ML and you’ve got something special. We’re turning the corner into a new decade where AI/ML will create real value for both the consumer and the enterprise at an accelerating pace.

Defining Terms

Applied AI: anything to do with taking AI research from the lab to a use-case and everything in-between. From the infrastructure and tooling, to the hardware, to the deployment surfaces in industry, to the models…

Before we start

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Thanks for reading!

Motivation

I’m often asked what gets me…

Note: I am not affiliated with any of the writers in this article. These are simply books and essays that I’m excited to share with you. There are no referrals or a cent going in my pocket from the authors or publishers mentioned. Reading is a vitamin for the brain; please support your favorite writers and enjoy!

Welcome to this month’s reading list. Slight change to the typical format — this month I’ll go over both some books and essays that I’ve read or plan on reading through the course of July.

On to the list!

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking