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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-pre-processing-for-data-analytics-and-data-science/
课程评论：没有评论
课程名称：数据分析与数据科学的数据预处理 概述： 《数据分析与数据科学的数据预处理》课程为学生提供了全面的理解，涵盖了准备原始数据进行分析的关键步骤。数据预处理是数据科学工作流程中的基本阶段，它涉及数据的转换、清洗和集成，以确保数据的质量和可用性，从而适用于后续分析。在本课程中，学生将学习如何处理真实世界的数据，这些数据往往是混乱、不一致和不完整的。学生将获得使用流行工具和库进行数据预处理的实践经验，例如Python及其数据处理库（如Pandas），并通过实际案例来巩固他们的学习。 课程涵盖的核心主题包括： - 数据预处理介绍：理解数据预处理在数据分析和数据科学中的重要性，概述数据预处理流程。 - 数据清洗技术：识别和处理缺失值，处理异常值和噪声数据，解决数据中的不一致性和错误。 - 数据转换：特征缩放与归一化，结合技术处理分类变量，降维方法（如主成分分析）。 - 数据集成和聚合：合并和连接数据集，处理多个来源的数据，为分析和可视化进行数据汇总。 - 处理文本和时间序列数据：文本预处理技术（如标记化、词干提取、去除停用词），时间序列数据清洗和特征提取。 - 数据质量评估：数据分析和探索性数据分析，数据质量指标和评估技术。 - 最佳实践和工具：有效的数据清洗和预处理策略，介绍流行的数据预处理库和工具（如Pandas、NumPy）。 通过该课程，学生将掌握数据预处理的基本技术和策略，为后续的数据分析和科学研究奠定坚实的基础。
The Data Pre-processing for Data Analytics and Data Science course provides students with a comprehensive understanding of the crucial steps involved in preparing raw data for analysis. Data pre- processing is a fundamental stage in the data science workflow, as it involves transforming, cleaning, and integrating data to ensure its quality and usability for subsequent analysis.Throughout this course, students will learn various techniques and strategies for handling real-world data, which is often messy, inconsistent, and incomplete. They will gain hands-on experience with popular tools and libraries used for data pre-processing, such as Python and its data manipulation libraries (e.g., Pandas), and explore practical examples to reinforce their learning.Key topics covered in this course include:Introduction to Data Pre-processing:- Understanding the importance of data pre-processing in data analytics and data science- Overview of the data pre-processing pipeline- Data Cleaning Techniques:Identifying and handling missing values:- Dealing with outliers and noisy data- Resolving inconsistencies and errors in the data- Data Transformation:Feature scaling and normalization:- Handling categorical variables through encoding techniques- Dimensionality reduction methods (e.g., Principal Component Analysis)- Data Integration and Aggregation:Merging and joining datasets:- Handling data from multiple sources- Aggregating data for analysis and visualization- Handling Text and Time-Series Data:Text preprocessing techniques (e.g., tokenization, stemming, stop-word removal):- Time-series data cleaning and feature extraction- Data Quality Assessment:Data profiling and exploratory data analysis- Data quality metrics and assessment techniques- Best Practices and Tools:Effective data cleaning and pre- processing strategies:- Introduction to popular data pre-processing libraries and tools (e.g., Pandas, NumPy)
