Module 02 · From Understanding to Preparation and From Modeling to Evaluation
Module 2 Lesson 1 Glossary: From Understanding to Preparation模块 2 课程 1 术语表：从理解到准备
Welcome! This alphabetized glossary contains many of the terms you'll find within this lesson. These terms are important for you to recognize when working in the industry, when participating in user groups, and when participating in other certificate programs.
欢迎！这个按字母顺序排列的术语表包含了本课程中许多你将遇到的术语。这些术语对于你在行业内工作、参与用户组以及参与其他证书项目时识别它们非常重要。
| Term 术语 | Definition 定义 |
|---|---|
| Automation 自动化 | Using tools and techniques to streamline data collection and preparation processes. 运用工具和技术简化数据收集和准备流程。 |
| Data Collection 数据收集 | The phase of gathering and assembling data from various sources. 从各种来源收集和组装数据的阶段。 |
| Data Compilation 数据汇编 | The process of organizing and structuring data to create a comprehensive data set. 组织数据以创建全面数据集的过程。 |
| Data Formatting 数据格式化 | The process of standardizing the data to ensure uniformity and ease of analysis. 将数据标准化以确保统一性和便于分析的过程。 |
| Data Manipulation 数据操作 | The process of transforming data into a usable format. 将数据转换为可用格式的过程。 |
| Data Preparation 数据准备 | The phase where data is cleaned, transformed, and formatted for further analysis, including feature engineering and text analysis. 数据清洗、转换和格式化的阶段，用于进一步分析，包括特征工程和文本分析。 |
| Data Preparation 数据准备 | The stage where data is transformed and organized to facilitate effective analysis and modeling. 数据转换和整理的阶段，以促进有效的分析和建模。 |
| Data Quality 数据质量 | Assessment of data integrity and completeness, addressing missing, invalid, or misleading values. 数据完整性和完整性的评估，处理缺失、无效或误导性值。 |
| Data Quality Assessment 数据质量评估 | The evaluation of data integrity, accuracy, and completeness. 数据完整性、准确性和完整性的评估。 |
| Data Set 数据集 | A collection of data used for analysis and modeling. 用于分析和建模的数据集合。 |
| Data Understanding 数据理解 | The stage in the data science methodology focused on exploring and analyzing the collected data to ensure that the data is representative of the problem to be solved. 在data科学方法论中，专注于探索和分析收集到的数据，以确保数据能够代表待解决的问题。 |
| Descriptive Statistics 描述性统计 | Summary statistics that data scientists use to describe and understand the distribution of variables, such as mean, median, minimum, maximum, and standard deviation. 数据科学家用来描述和理解变量分布的统计摘要，如均值、中位数、最小值、最大值和标准差。 |
| Feature 特征 | A characteristic or attribute within the data that helps in solving the problem. 数据中的一个特性或属性，有助于解决问题。 |
| Feature Engineering 特征工程 | The process of creating new features or variables based on domain knowledge to improve machine learning algorithms' performance. 基于领域知识创建新特征或变量的过程，以提升机器学习算法的性能。 |
| Feature Extraction 特征提取 | Identifying and selecting relevant features or attributes from the data set. 从数据集中识别和选择相关的特征或属性。 |
| Interactive Processes 交互过程 | Iterative and continuous refinement of the methodology based on insights and feedback from data analysis. 基于数据分析的洞察和反馈，对方法进行迭代和持续改进。 |
| Missing Values 缺失值 | Values that are absent or unknown in the dataset, requiring careful handling during data preparation. 数据集中缺失或未知的数据，需要在数据准备过程中进行仔细处理。 |
| Model Calibration 模型校准 | Adjusting model parameters to improve accuracy and alignment with the initial design. 调整模型参数以提高精度并与初始设计保持一致。 |
| Pairwise Correlations 成对相关性 | An analysis to determine the relationships and correlations between different variables. 一种分析不同变量之间关系和相关性分析方法。 |
| Text Analysis 文本分析 | Steps to analyze and manipulate textual data, extracting meaningful information and patterns. 分析和操作文本数据的方法，提取有意义的信息和模式。 |
| Text Analysis Groupings 文本分析分组 | Creating meaningful groupings and categories from textual data for analysis. 从文本数据中创建具有意义的分组和类别以进行分析。 |
| Visualization techniques 可视化技术 | Methods and tools that data scientists use to create visual representations or graphics that enhance the accessibility and understanding of data patterns, relationships, and insights. 数据科学家使用的方法和工具，用于创建可视化表示或图形，以增强数据模式、关系和见解的可访问性和理解。 |