Ocean.py helps data scientists earn $ from their AI models, track provenance of data & compute, and get more data. (More details [here](../../data-science/README.md).)
* **Publish** data services: data feeds, REST APIs, downloadable files or compute-to-data. Create an ERC721 **data NFT** for each service, and ERC20 **datatoken** for access (1.0 datatokens to access).
* **Sell** datatokens via for a fixed price. Sell data NFTs.
* **Transfer** data NFTs & datatokens to another owner, and all other ERC721 & ERC20 actions using web3.
As a Python library, Ocean.py is built for the key environment of data scientists. It that can simply be imported alongside other Python data science tools like numpy, matplotlib, scikit-learn and tensorflow.
After these quickstart steps, the main [README](https://github.com/oceanprotocol/ocean.py/blob/main/README.md) points to several other use cases, such as [Data Farming - Challenge DF](https://github.com/oceanprotocol/predict-eth), [Data Farming - Volume DF](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/df.md), on-chain key-value stores ([public](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/key-value-public.md) or [private](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/key-value-private.md)), and other types of data assets ([REST API](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/publish-flow-restapi.md), [GraphQL](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/publish-flow-graphql.md), [on-chain](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/publish-flow-onchain.md)).