Data Science on Blockchains

Data Science on Blockchains, available at $54.99, has an average rating of 4.21, with 43 lectures, 16 quizzes, based on 7 reviews, and has 64 subscribers.

You will learn about Learn how the blockchain technology, Web3 and decentralized finance works Learn to parse the data from Bitcoin and Ethereum to develop machine learning models on the data Mine blockchain data for price prediction Mine blockchain data for ransomware payment, darknet market payment and pump/dump scheme detection Track investor behavior on multiple DeFi networks on Ethereum This course is ideal for individuals who are Data scientists or Machine learners or Graduate students or Blockchain analysts or Blockchain engineers It is particularly useful for Data scientists or Machine learners or Graduate students or Blockchain analysts or Blockchain engineers.

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Summary

Title: Data Science on Blockchains

Price: $54.99

Average Rating: 4.21

Number of Lectures: 43

Number of Quizzes: 16

Number of Published Lectures: 43

Number of Published Quizzes: 16

Number of Curriculum Items: 59

Number of Published Curriculum Objects: 59

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how the blockchain technology, Web3 and decentralized finance works
Learn to parse the data from Bitcoin and Ethereum to develop machine learning models on the data
Mine blockchain data for price prediction
Mine blockchain data for ransomware payment, darknet market payment and pump/dump scheme detection
Track investor behavior on multiple DeFi networks on Ethereum

Who Should Attend

Data scientists
Machine learners
Graduate students
Blockchain analysts
Blockchain engineers

Target Audiences

Data scientists
Machine learners
Graduate students
Blockchain analysts
Blockchain engineers

Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed an unprecedented attention. As Blockchain applications proliferate, so does the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in the information sciences. Although there is a vast quantity of information available, the consequent challenge is to develop tools and algorithms to analyze the large volumes of user-generated content and transactions on blockchains, to glean meaningful insights from Blockchain data. The objective of the course is to train students in data collection, modeling and analysis for blockchain data analytics on public blockchains, such as Bitcoin, Litecoin, Monero, Zcash, Ripple, and Ethereum.

Expectations and Goals

We will teach all core blockchain components with an eye towards building machine learning models on blockchain data. Students will be able to achieve the following learning objectives at the completion of the course.

Learn the history of digital currencies and problems that prevented their adoption. What are the real-life use cases of Blockchain? How Blockchain differs from earlier solutions?

Learn the concepts of consensus and proof-of-work in distributed computing to understand and describe how blockchain works.

Learn data models for addresses, transactions and blocks in cryptocurrencies and Blockchain platforms.

Use Java Python and R to extract blockchain blocks and store the transaction network on Bitcoin, Ripple, IOTA and Ethereum blockchains.

Model weighted, directed multi-graph blockchain networks and use graph mining algorithms to identify influential users and their transactions.

Predict cryptocurrency and crypto-asset prices in real time.

Extract and mine data from smart contracts on the Ethereum blockchain.

We would like to thank Ignacio Segovia-Dominguez of UT Dallas and Nasa for his help in editing and providing feedback on the course content.

Course Curriculum

Chapter 1: Origins and Consensus

Lecture 1: Origins of Digital Currencies

Lecture 2: Networks, Traditional Consensus

Lecture 3: Nakamoto Consensus

Lecture 4: Tenets of Bitcoin

Chapter 2: Bitcoin: the first Blockchain

Lecture 1: Hash Functions and SHA256

Lecture 2: Address, Transaction, Block and Output

Lecture 3: Transaction Fee, Change Address, Address Reuse and Merkle Tree

Lecture 4: Transaction Creation, Forks

Lecture 5: Forks in the Transaction Network

Lecture 6: Proof-of-work, Block Mining, Rewards

Lecture 7: Proof-of-X: Alternatives to POW

Lecture 8: Mining Pools

Lecture 9: Output Types

Lecture 10: Wallets

Lecture 11: The Script Language and Locking/Unlocking Scripts

Lecture 12: Forks in the Protocol

Lecture 13: Segregated Witness and Taproot/Schnorr

Chapter 3: Ethereum: the World Computer

Lecture 1: Ethereum Fundamentals

Lecture 2: Ethereum Addresses, Account Nonce

Lecture 3: Smart Contracts, Capabilities and Life Cycles

Lecture 4: Ethereum Transaction Types

Lecture 5: Gas Cost, Gas Price and Gas Fee

Lecture 6: Ethereum Data Storage: Stack, Memory and Storage

Lecture 7: Transaction and Message Call, Logs and Events

Lecture 8: ETHash: the POW mining for Ethereum until 2022

Lecture 9: Ethereum Block Header, Patricia-Merkle Tree, Block Gas Limit and Size

Lecture 10: Web3, DAO, dApps

Chapter 4: Decentralized Finance

Lecture 1: Decentralized Finance Primitives

Lecture 2: ERC20, ERC721, ERC1155 Tokens

Lecture 3: Stablecoins

Lecture 4: Oracles

Chapter 5: Graph Structures of UTXO Blockchains

Lecture 1: UTXO Transaction Network and Three Graph Rules

Lecture 2: Address and Transaction Graphs

Lecture 3: UTXO Chainlets: Merge, Split and Transition Chainlets

Chapter 6: Graph Structures of Account Blockchains

Lecture 1: Transaction Networks on Account-based Blockchains

Lecture 2: Transaction Networks of ERC20 and ERC721 (NFT) tokens

Lecture 3: Networks of Smart Contract Transactions and Calls

Chapter 7: E-Crime on Blockchains

Lecture 1: Darknet Markets

Lecture 2: Ransomware

Lecture 3: Obfuscation: Peeling Chains, Coin Mixing, Shapeshifting

Chapter 8: Temporal Data Mining on Blockchains

Lecture 1: Network Snapshots, Address and Transaction Features

Lecture 2: Transaction Fingerprinting

Lecture 3: Training a Classifier on a Temporal Window, Performance Metrics

Instructors

Cuneyt Gurcan Akcora
Professor of Computer Science and Statistics
Murat Kantarcioglu
Professor of Computer Science at Univ. Texas at Dallas
Yulia Gel
Professor

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 1 votes
4 stars: 2 votes
5 stars: 3 votes

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