NYC Data Science Academy is now offering a course on blockchain. More specifically, a course targeting Ethereum and smart contracts. According to the Academy, the course is good for both developers and “data enthusiasts”. The course begins on October 13th and will consist of 6 comprehensive, all-day sessions. The classes will be held on the company’s campus in midtown New York.

There has been a veritable explosion of decentralized applications running on smart contracts. Ethereum is currently the most popular ecosystem to which to build these Dapps. Today, there are tens of thousands of developers and companies building applications looking to disrupt just about everything.

Vivian Zhang, Co-Founder and CTO of the NYC Data Science Academy, commented on the announcement;

“We believe that blockchain technology will prove to be an important part of business applications in the near future. We have an opportunity to give students a unique ‘data perspective’ on building smart contract applications and our new course will be a great introduction for anyone looking to get started.”

The course is designed for individuals with a technical background. Topics, include all of the good stuff:

Cryptographic hashing and symmetric/asymmetric data encryption/decryption, signatures with private keys

Decentralized p2p discovery protocol and Ethereum nodes

Proof of Work block mining vs Proof of Authority block sealing user accounts and smart contract accounts

Ethereum virtual machine and its bytecode

Gas, transactions, transaction receipts, transaction pool

Merkle tree/Merkle proof, Merkle Patricia tree and Blockchain state tree

Wallets like Mist/Ethereum wallet, the concept of DApps

The functionality of Swarm/Whisper/Oraclize/ Ethereum Bridge

The instructor for the course will be Dr. Aiko Liu, who received a doctorate in Mathematics from Harvard before conducting research and teaching at M.I.T and U.C Berkeley for nine years and later moving into the world of finance. The Academy say that Liu has worked in the hedge fund industry on quantitative trading for a decade before diving into Data Science full time.