Data Science on Blockchains

Name: Data Science on Blockchains
Rating: 3.8 (16 reviews)

A complete introduction to blockchains and their data models for Data Science

Created byCuneyt Gurcan Akcora, Murat Kantarcioglu, Yulia Gel

Last updated 11/2022

English

What you'll 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

Course content

8 sections • 43 lectures • 7h 15m total length

Origins of Digital Currencies22:01
Attributes of Money
Networks, Traditional Consensus11:10
Networks
Nakamoto Consensus11:35
Finality and Attacks
Tenets of Bitcoin6:39

Hash Functions and SHA25610:55
Hash Functions
Address, Transaction, Block and Output12:29
Discover how Bitcoin uses addresses, outputs, and signatures to secure one-to-one and multi-signature transactions, create blocks, and support different address types and Schnorr signatures.
Address, Transaction and Block
Transaction Fee, Change Address, Address Reuse and Merkle Tree17:41
Fees and Addresses
Transaction Creation, Forks11:31
Learn how Bitcoin uses blockchain to solve authenticity and double spending with signed transactions and proof of funds, and how mining forms blocks with proof of work to prevent forks.
Transactions
Forks in the Transaction Network8:38
Forks
Proof-of-work, Block Mining, Rewards26:11
Proof-of-X: Alternatives to POW6:33
Compare proof-of-work with alternatives under proof-of-x, highlighting stake-based, memory-hard, delegated, ownership and publication variants, their energy costs, central authority implications, and real-world challenges.
Mining Pools2:15
Output Types8:23
Wallets5:21
Explore how Bitcoin wallets connect to the peer-to-peer network, manage private keys and addresses, and differentiate custodial vs non-custodial use, including deterministic, seed-based derivation.
The Script Language and Locking/Unlocking Scripts9:52
Forks in the Protocol6:23
Segregated Witness and Taproot/Schnorr3:26
Bitcoin Fundamentals

Ethereum Fundamentals12:03
Explore Ethereum fundamentals: smart contracts written in solidity compile to bytecode and run on the Ethereum Virtual Machine, the world computer, enabling public, replicated, unstoppable code across nodes.
Ethereum
Ethereum Addresses, Account Nonce7:22
Addresses
Smart Contracts, Capabilities and Life Cycles11:05
Ethereum Transaction Types11:42
Transactions
Gas Cost, Gas Price and Gas Fee15:02
Understand how gas cost, gas price, and gas fee set transaction costs on blockchains. See how operations and storage costs, gas limits, and dynamic data affect actual gas usage.
Costs
Ethereum Data Storage: Stack, Memory and Storage4:04
Transaction and Message Call, Logs and Events8:37
ETHash: the POW mining for Ethereum until 20227:52
Examine Ethash, Ethereum’s proof-of-work mining until 2022, highlighting the memory-hard dag, 1 gigabyte graph, 16 megabyte cache, seed-based data generation, and uncle blocks within the ghost protocol.
Ethereum Block Header, Patricia-Merkle Tree, Block Gas Limit and Size9:36
Web3, DAO, dApps4:53

Decentralized Finance Primitives1:48
Explore decentralized finance, or defi, a blockchain-based system using smart contracts without central intermediaries, featuring non-custodial, permissionless, auditable, and composable primitives like Ethereum transactions and collateral liquidations.
ERC20, ERC721, ERC1155 Tokens10:34
Stablecoins9:35
Oracles4:11
Oracles connect smart contracts to real-world data, bridging price feeds and event outcomes; decentralized oracles aggregate multiple sources to reduce central points of failure and ensure data integrity.

Transaction Networks on Account-based Blockchains12:43
Explore graph models for account-based blockchains like Ethereum, contrasting coin and token transactions, and map externally owned addresses, smart contract addresses, and null addresses with smart contracts and programmable interactions.
Transaction Networks of ERC20 and ERC721 (NFT) tokens11:38
Explore token transaction networks on Ethereum, including ERC-20 fungible tokens and ERC-721 non-fungible tokens with unique identifiers, defined by smart contracts.
Networks of Smart Contract Transactions and Calls7:56

Requirements

Python, R or Java programming
Basic concepts in graph analysis

Description

Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed 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 toward building machine learning models on blockchain data. Students will be able to achieve the following learning objectives upon completion of the course.

Learn the history of digital currencies and the problems that prevented their adoption. What are the real-life use cases of Blockchain? How does Blockchain differ 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 on 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.

Who this course is for:

Data scientists
Machine learners
Graduate students
Blockchain analysts
Blockchain engineers

Data Science on Blockchains

What you'll learn

Explore related topics

Course content

Origins and Consensus4 lectures • 51min

Bitcoin: the first Blockchain13 lectures • 2hr 10min

Ethereum: the World Computer10 lectures • 1hr 32min

Decentralized Finance4 lectures • 26min

Graph Structures of UTXO Blockchains3 lectures • 39min

Graph Structures of Account Blockchains3 lectures • 32min

E-Crime on Blockchains3 lectures • 33min

Temporal Data Mining on Blockchains3 lectures • 31min

Requirements

Description

Who this course is for: