NYC Data Science Academy now offers a blockchain course. More specifically, a course aimed at Ethereum and smart contracts. According to the Academy, the course is positive for both developers and "data enthusiasts". The course starts on October 13th and will consist of 6 full-day sessions throughout the day. Classes will be held on the company's campus in downtown New York.
There has been a real explosion of decentralized applications performed on smart contracts. Ethereum is currently the most popular ecosystem to build these Dapps. Today, there are tens of thousands of developers and companies that create applications that try to stop 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 the opportunity to offer students an exclusive" data perspective "on creating smart applications for contracts and our new course will be an excellent introduction for anyone wishing to start. "
The course is for people with a technical background. Arguments, include all good things:
- cryptographic hashing and encryption / symmetric / asymmetric decryption of data, signatures with private keys
- Decentralized p2p discovery protocol and Ethereum nodes
- Data mining vs proof test block of authority of user accounts and smart accounts
- Ethereum virtual machine and its bytecode
- Gases, transactions, transaction receipts, transaction pools
- Merkle tree / Merkle proof, Merkle Patricia tree and Blockchain state tree
- Wallets like Mist / Portfolio Ethereum, the concept of DApps
- The functionality of Swarm / Whisper / Oraclize / Ethereum Bridge
The instructor of the course will be Dr. Aiko Liu, who received a Ph.D. in Mathematics from Harvard before conducting research and teaching at MIT and at the University of Berkeley for nine years and then in the finance world. The Academy states that Liu has worked in the field of hedge funds on quantitative trade for a decade before plunging into full-time Data Science.