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所在平台: Udemy |
课程主页: https://www.udemy.com/course/causal-data-science/
课程评论：没有评论
课程名称：使用有向无环图进行因果数据科学 概述：本课程介绍了使用有向无环图（DAG）进行因果数据科学的基本概念。DAG 将数学图论与统计概率概念结合，为因果推理提供了强大的方法。最初，这一理论在计算机科学和人工智能领域得到发展，如今在机器学习、经济学、金融、健康科学和哲学等领域也越来越受到重视。DAG 使得基于直观图形标准检查因果语句的有效性成为可能，而无需进行代数运算。此外，它们还使得借助特殊的识别算法完全自动化因果推断任务成为可能。作为因果思维的全面框架，DAG 正逐渐成为每位对数据科学和机器学习感兴趣者的必备工具。 本课程将提供对过去三十年因果数据科学理论进展的良好概述，重点放在理论的实际应用上，学生将能够在自己的工作中运用因果数据科学方法。通过使用统计软件 R 的实际示例，课程将逐步引导学生理解所介绍的内容。虽然没有特定的先修课程要求，但具备基本统计知识和一定编程技能将是有益的。
This course offers an introduction into causal data science with directed acyclic graphs (DAG). DAGs combine mathematical graph theory with statistical probability concepts and provide a powerful approach to causal reasoning. Originally developed in the computer science and artificial intelligence field, they recently gained increasing traction also in other scientific disciplines (such as machine learning, economics, finance, health sciences, and philosophy). DAGs allow to check the validity of causal statements based on intuitive graphical criteria, that do not require algebra. In addition, they open the possibility to completely automatize the causal inference task with the help of special identification algorithms. As an encompassing framework for causal thinking, DAGs are becoming an essential tool for everyone interested in data science and machine learning.The course provides a good overview of the theoretical advances that have been made in causal data science during the last thirty year. The focus lies on practical applications of the theory and students will be put into the position to apply causal data science methods in their own work. Hands-on examples, using the statistical software R, will guide through the presented material. There are no particular prerequisites, but a good working knowledge in basic statistics and some programming skills are a benefit.
