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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-architecture-for-data-scientists/
课程评论：没有评论
课程名称：数据科学家的数据架构 课程概述：机器学习模型的优劣取决于其训练所用的数据，因此数据科学家必须理解数据架构，以构建有效的机器学习模型。本课程将教授以下内容： - 数据架构的基本概念 - 数据类型回顾，包括结构化、非结构化和半结构化数据 - 数据仓库基础知识 - 数据湖基础知识 - 数据仓库与数据湖的区别 - 数据湖房屋（Data Lakehouse）基本概念 - 数据网格（Data Mesh）基础知识，涉及数据的去中心化治理，包括数据目录、数据合同和数据网络等主题 - 在数据科学中整合流数据的挑战 - 与机器学习相关的数据基础设施，例如特征库和向量数据库 课程的帮助包括： - 就数据基础设施的架构做出明智的决策，从而提升模型的准确性和有效性 - 采用现代技术和实践以改善工作流程 - 增强对数据工程师的理解与同理心 - 提升作为全能数据科学家的声誉 课程的核心思想是：数据架构就像支撑机器学习模型构建的框架。建筑需要坚固的框架来支撑结构，机器学习模型则需要可靠的数据架构来保证其准确性和有效性。不了解数据架构的原则，数据科学家可能面临模型产生不准确或偏见结果的风险。通过掌握数据架构的基本知识，数据科学家可以确保其数据基础设施健壮、可靠，能够支持准确有效的机器学习模型的训练和部署。 完成课程后，您将拥有知识，能够指导团队和组织创建适合数据科学应用的正确数据架构。
Machine learning models are only as good as the data they are trained on, which is why understanding data architecture is critical for data scientists building machine learning models.This course will teach you:The fundamentals of data architectureA refresher on data types, including structured, unstructured, and semi-structured dataDataWarehouse FundamentalsData Lake FundamentalsThe differences between data warehouses and data lakesDataLakehouse FundamentalsData Mesh fundamentals for decentralized governance of data including topics like data catalog, data contracts and data fabric.The challenges of incorporating streaming data in data scienceSome machine learning-specific data infrastructure, such as feature stores and vector databasesThe course will help you:Make informed decisions about the architecture of your data infrastructure to improve the accuracy and effectiveness of your modelsAdopt modern technologies and practices to improve workflowsDevelop a better understanding and empathy for data engineersImprove your reputation as an all-around data scientistThink of data architecture as the framework that supports the construction of a machine learning model. Just as a building needs a strong framework to support its structure, a machine learning model needs a solid data architecture to support its accuracy and effectiveness. Without a strong framework, the building is at risk of collapsing, and without a strong data architecture, machine learning models are at risk of producing inaccurate or biased results. By understanding the principles of data architecture, data scientists can ensure that their data infrastructure is robust, reliable, and capable of supporting the training and deployment of accurate and effective machine learning models.By the end of this course, you'll have the knowledge to help guide your team and organization in creating the right data architecture for deploying data science use cases.
