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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-cleaning-and-visualization-in-python/
课程评论：没有评论
Coursera 课程《Python 数据清洗与可视化》课程总结： 本课程全面讲解机器学习生命周期中的关键环节——探索性数据分析（EDA）。EDA 有助于诊断数据问题并应用技术提高数据质量。 **第一阶段：数据清洗** * **处理缺失值：** 探讨多种填充方法，包括统计填充、基于邻近的方法和预测填充。 * **数据转换：** 学习对数转换、平方根转换和 Box-Cox 转换等技术。 * **异常值检测：** 介绍 Z 分数、IQR 和马哈拉诺比斯距离等方法。 **第二阶段：数据可视化** * **单变量、双变量和多变量分析：** 深入讲解直方图、箱线图、散点图、热力图等多种图表，以清晰地解读数据。 **重点与优势：** * **理论先行：** 课程高度重视理论知识，符合行业需求。 * **实践应用：** 通过真实案例研究，展示 EDA 如何提取有价值的见解。 * **Python 实现：** 所有操作均使用 pandas、numpy、seaborn 和 matplotlib 等库在 Python 中完成。 * **技能提升：** 掌握 EDA 技巧，可提高机器学习模型性能。 * **职业发展：** 适合数据分析师、数据科学家或机器学习工程师等职业。 学习本课程将帮助您深入理解数据中的实际问题及其解决方案，掌握可视化技术，并能运用 Python 进行深入分析，对机器学习和相关职业发展大有裨益。
This course provides a comprehensive understanding of Exploratory Data Analysis (EDA), a crucial step in the machine learning lifecycle. EDA helps in diagnosing issues within datasets and applying appropriate techniques to improve data quality.The first phase of the course focuses on data cleaning, covering essential techniques such as handling missing values (imputation), data transformation, and outlier detection. Understanding these processes ensures the dataset is refined and structured for better model performance. Various imputation methods, including statistical, neighbor-based, and predictive filling, are discussed along with transformations like log, square root, and Box-Cox. Outlier detection techniques such as Z-score, IQR, and Mahalanobis distance are also explored.The second phase delves into data visualization, covering univariate, bivariate, and multivariate analysis. It provides an extensive discussion on various plots, including histograms, box plots, scatter plots, heatmaps, and more, ensuring clarity in data interpretation.The course concludes with real-world case studies, demonstrating how EDA helps derive meaningful insights. All implementations are carried out in Python, leveraging libraries such as pandas, numpy, seaborn, and matplotlib. By the end of this course, participants will have hands-on expertise in performing EDA effectively for any dataset and leverage these techniques to improvise the data for better results in machine learning analysis.This course provides more focus and priority to the theoretical aspects of the concepts, since understanding the theory is very much needed and expected in the industry also. Learning this course will give an in-depth idea on various practical issues with data and how to sort them out, followed by various visualization techniques. This knowledge can be useful to work on real time datasets and develop python programs for effective and insightful analysis. Furthermore, mastering the EDA process can be highly helpful in boosting the performance of machine learning algorithms. This can be useful for a career as data analyst, data scientist, or machine learning engineer.
