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Web3 Analytics 101: Async Course
An open course that teaches students how to understand and analyze blockchain data using SQL.
This course is designed for students who want to answer questions with data, provide context to protocol growth and change, and understand what is happening behind the scenes.
The goal of this course is to take interested students and teach them the requisite knowledge to be a data analyst in web3. Our goal is that students emerge from the course with an intermediate level of knowledge. We define that as being able to confidently tackle challenges with tools such as Flipside, Dune, Footprint, Dapplooker, etc. While a "beginner" analyst may be limited to high-level overviews, an "intermediate" analyst is able to push beyond surface-level analysis, extracting insights from curiosity-directed exploration.
In short, a successful course will produce a cohort of talented analysts creating great content and actively competing in bounty programs.
We will explore Ethereum data throughout this course as it has the most robust tooling and data available. Core concepts can be applied across a range of EVM-compatible chains and alternate blockchains.
The following is intended to set expectations about the content of this course, while the content covered will go into more depth than what is listed.
We will be compiling vetted resources that students can dive into, on their own time, to get up to speed with the main topics that this course covers: blockchains and crypto, data analysis using SQL, working in web3, etc.
Finally, find some MetricsDAO resources here:
Establishing foundational knowledge
- Blockchain, distilled, and what is Ethereum?
- Why do this kind of work?
- The analytics ecosystem and work2earn.
Expand technical abilities with live use-cases.
- Available data
- Exploratory analysis and understanding what we're looking at.
- Observing trends with high-level aggregations.
- Comparing Ethereum transactions in data tables to block explorers.
- Domain knowledge - understanding your subject
- Exploring data through visual representations
Introducing intermediate SQL techniques and diving into (more) raw transaction data.
- SQL Aggregations - benefits and pitfalls
- Data, uncurated
- Moving beyond
ez_tables working with JSON message objects.
- SQL syntax: Dune Engine V2 vs. V1
- Column-oriented data
- Using Dune abstractions
- Cross-chain analysis
- Creating professional outputs
- Breaking into web3 data.