Module 02 · From Understanding to Preparation and From Modeling to Evaluation
Lesson Summary: From Understanding to Preparation课程总结：从理解到准备
Congratulations! You have completed this lesson. At this point in the course, you know:
恭喜！您已完成本课程。此时，您已了解：
- The Data Understanding stage encompasses all activities related to constructing the data set and answers the question as to whether the data you collected represents the problem to be solved.
数据理解阶段涵盖了与构建数据集相关的所有活动，并回答了所收集的数据是否代表待解决问题的问题。 - During the Data Understanding stage, scientists might use descriptive statistics, predictive statistics, or both.
在数据理解阶段，科学家可能会使用描述性统计、预测性统计或两者兼用。 - Data scientists commonly apply Hurst, univariates, and other statistics on each variable, such as mean, median, minimum, maximum, standard deviation, pairwise correlation, and histograms.
数据科学家通常对每个变量应用Hurst、单变量和其他统计方法，例如均值、中位数、最小值、最大值、标准差、配对相关性以及直方图。 - Data scientists also use univariates, statistics, and histograms to assess data quality.
数据科学家还使用单变量分析、统计方法和直方图来评估数据质量。
- During the Data Preparation stage, data scientists must address missing or invalid values, remove duplicates, and validate that the data is properly formatted.
在数据准备阶段，数据科学家必须处理缺失值或无效值，删除重复数据，并验证数据是否已正确格式化。 - Feature engineering, also part of the Data Preparation stage, uses domain knowledge of the data to create features that make the machine learning algorithms work.
特征工程也是数据准备阶段的一部分，它利用数据的领域知识来创建使机器学习算法能够工作的特征。 - Text analysis during the Data Preparation stage is critical for validating that the proper groupings are set and that the programming is not overlooking hidden data.
在数据准备阶段进行文本分析对于验证分组是否正确以及编程是否遗漏了隐藏数据至关重要。