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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-on-blockchains/
课程评论：没有评论
课程名称：区块链数据科学 课程概述： 比特币加密货币及其基础的区块链技术正遭受前所未有的关注。随着区块链应用的增多，存储在区块链上的数据复杂性和数量也在不断增加。分析这些数据已成为一个重要的研究课题，并在信息科学领域带来了方法上的进展。尽管信息量巨大，相关的挑战在于开发工具和算法，以分析区块链上大量用户生成的内容和交易，从而从区块链数据中获得有意义的见解。该课程旨在培训学生在公共区块链（如比特币、莱特币、门罗币、Zcash、瑞波币和以太坊）上进行数据收集、建模和分析。 课程目标和期望： 本课程将教授所有核心区块链组件，重点是如何在区块链数据上构建机器学习模型。学生完成课程后将能够实现以下学习目标：了解数字货币的历史及其被采纳的障碍；认识区块链的实际应用案例；了解区块链与早期解决方案的区别；学习共识和工作量证明的概念，以理解和描述区块链的工作原理；掌握加密货币和区块链平台上地址、交易和区块的数据模型；使用Java、Python和R提取区块链区块，并存储比特币、瑞波币、IOTA和以太坊区块链上的交易网络；建模加权、有向的多图区块链网络，并使用图挖掘算法识别有影响力的用户及其交易；预测加密货币和加密资产实时价格；提取和挖掘以太坊区块链上智能合约的数据。 特别感谢UT Dallas的Ignacio Segovia-Dominguez和NASA对课程内容的编辑和反馈支持。
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.
