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所在平台: Udemy |
课程主页: https://www.udemy.com/course/crisp-mlq-data-pre-processing-using-python/
课程评论：没有评论
**课程名称：CRISP-ML(Q)-使用Python进行数据预处理 (2025)** **课程概述：** 本课程旨在帮助数据科学领域的初学者理解项目管理方法论的概念，为数据科学项目提供结构化的处理方法。您将学习理解业务问题的重要性，以及如何定义目标、约束条件和成功标准（包括业务、机器学习和经济学方面）。课程还将介绍项目启动文件——项目章程（Project Charter）。 您将深入了解各种数据类型和四种数据度量方式，以及数据收集机制，从而确保获取适用于后续分析的合适数据。课程将详细讲解主要数据收集技术，包括问卷调查和实验。 此外，课程将重点讲解探索性数据分析（EDA）或描述性分析，包括“4个业务时刻”以及各种图形表示方法，如单变量、双变量和多变量图。箱线图、直方图、散点图和Q-Q图也将进行讲解。 课程的核心将是**使用Python进行数据预处理技术**的学习，确保将适当的数据输入到模型构建流程中。您将通过实践导向的数据集，学习并应用各种数据预处理技术，包括异常值分析、插补技术、缩放技术等。
This program will help aspirants getting into the field of data science understand the concepts of project management methodology. This will be a structured approach in handling data science projects. Importance of understanding business problem alongside understanding the objectives, constraints and defining success criteria will be learnt. Success criteria will include Business, ML as well as Economic aspects. Learn about the first document which gets created on any project which is Project Charter. The various data types and the four measures of data will be explained alongside data collection mechanisms so that appropriate data is obtained for further analysis. Primary data collection techniques including surveys as well as experiments will be explained in detail. Exploratory Data Analysis or Descriptive Analytics will be explained with focus on all the ‘4' moments of business moments as well as graphical representations, which also includes univariate, bivariate and multivariate plots. Box plots, Histograms, Scatter plots and Q-Q plots will be explained. Prime focus will be in understanding the data preprocessing techniques using Python. This will ensure that appropriate data is given as input for model building. Data preprocessing techniques including outlier analysis, imputation techniques, scaling techniques, etc., will be discussed using practical oriented datasets.
