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所在平台: Udemy |
课程主页: https://www.udemy.com/course/dataqc_x/
课程评论：没有评论
**课程总结：提升数据分析与机器学习中的数据质量** 本课程旨在帮助学员掌握数据质量的重要性、评估方法及提升策略，以支持更明智的数据驱动决策。 **核心内容:** * **数据质量的意义与范畴:** 强调高质量数据是良好决策的基础，涵盖数据收集、存储、分析等全生命周期。 * **高层策略:** 学习数据质量保障的整体框架，包括相关术语、数据文档与管理，以及在不同研究阶段进行数据质量检查和提升的方法。 * **数据质量评估:** 掌握定性和定量的数据质量评估技术，如视觉检查、错误率计算和异常值检测。课程提供Python代码（使用pandas, numpy, seaborn, matplotlib）演示如何实现这些评估方法。 * **数据清洗与异常值处理:** 学习具体的数据处理方法和算法，用于清理数据、识别并剔除不准确或异常的数据点。课程同样提供Python代码指导实现这些数据处理流程。 **目标学员:** * **数据从业者:** 寻求理解和实践数据质量评估与提升的高层策略和底层操作。 * **管理者、客户及合作者:** 希望理解数据质量的重要性，即使不直接参与数据操作。
All of our decisions are based on data. Our sense organs gather data, our memories are data, and our gut-instincts are data. If you want to make good decisions, you need to have high-quality data.This course is about data quality: What it means, why it's important, and how you can increase the quality of your data. In this course, you will learn:High-level strategies for ensuring high data quality, including terminology, data documentation and management, and the different research phases in which you can check and increase data quality.Qualitative and quantitative methods for evaluating data quality, including visual inspection, error rates, and outliers. Python code is provided to see how to implement these visualizations and scoring methods using pandas, numpy, seaborn, and matplotlib.Specific data methods and algorithms for cleaning data and rejecting bad or unusual data. As above, Python code is provided to see how to implement these procedures using pandas, numpy, seaborn, and matplotlib.This course is for Data practitioners who want to understand both the high-level strategies and the low-level procedures for evaluating and improving data quality.Managers, clients, and collaborators who want to understand the importance of data quality, even if they are not working directly with data.
