Part1 Web3 Data Analysis Basics

Chapter 1 Where Data and Web3 Meet

Data Ingredients

1. Make decision using data(Data driven decision is very important)
2. Communication using visualizing data

   • Web3 data science

     1. Problem definition.whether the problem can be solved with data, and if so, what data would be useful to answer it.(collaboration of data scientists and business users)
     2. **Investigation and data ingestion:**where can we find the necessary data to use for this project?
     3. **Analysis/modeling:**we analyze the data to extract conclusions and may need to model it to try to predict future outcomes.
        • Descriptive analysis
        • Diagnostic analysis
        • Predictive analysis
        • Prescriptive analysis
        • Generative AI
     4. **Evaluation:**the result of our analysis or modeling is evaluated and tested to confirm it meets the project goals and provides value to the business.
     5. **Presentation/deployment:**If it is an analysis from which the company will make a decision, our job will probably conclude with a presentation and explanation of our findings. Alternatively, if we are working as part of a larger software pipeline, our model will most likely be deployed or integrated into the data pipeline.

   • Blockchain Ingredients

     Limitation

     • Velocity
     • Intuition
     • Cost

     CBDC

     https://cbdctracker.org/

• Web3 metrics

  Block height

  Time

  Tokenomics(代币经济学)

  Total Value Locked(TVL) https://defillama.com/

  Total market cap

• Data Quality Challenge

  how do we choose when we have multiple sources of information

Google BigQuery, Covalent, or Dune

market price company api

从多个交易所获取平均价格

• concepts

Chainalysis

• confirmation
  • 不同交易金额所需要的确认次数
  • stale block / uncle blocks
  • 确认交易不可逆之前，要求一定的交易数量已经完成确认来保证安全可靠
• NFT Floor Price
• 有的人丢失了账户，僵尸地址“lost”
  • Chainalysis：Bitcoin that has not moved for five years now is considered lost.
• API
  • Remote Procedure Call (RPC) APIs
  • Representational State Transfer (REST) API
  • GraphQL

Chapter 2 Working with On-Chain Data

Data stored on-chain is serialized

• Dissecting a transaction
• Dissecting a block