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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-data-analysis-and-visualization-in-python/
课程评论：没有评论
**课程名称：** The Complete Data Analysis and Visualization in Python 2023 **课程概述：** 本课程将从零开始教授 Python 数据分析与可视化。无论您是否具备编程经验，本课程都适合您。 **核心内容：** * **NumPy & Pandas：** 学习数据分析和预处理的必备库，涵盖关键概念，并通过测试分数数据集进行实践，提升数据预处理技能。 * **Matplotlib：** 掌握数据可视化的基础，学习所有必要的可视化概念。 * **Seaborn：** 探索统计可视化库 Seaborn，使用内置的 "tips" 数据集学习常见图表，并使用钻石数据集深入挖掘 Seaborn 的强大功能。 * **实战演练：** * 使用 Netflix 数据集进行复杂数据预处理和可视化，展示进度和历史图表的绘制。 * 使用钻石数据集进行深入分析，充分展示 Seaborn 的强大功能。 **课程亮点：** * **零基础入门：** 适合无编程经验的学习者。 * **全面覆盖：** 涵盖 NumPy, Pandas, Matplotlib, Seaborn 等核心库。 * **实战导向：** 通过真实数据集进行大量练习，巩固所学知识。 * **理论与技巧并重：** 不仅教授统计上的正确方法，还将揭示常见的分析错误。 学完本课程，您将能够熟练运用 Python 进行完整的数据分析和可视化工作。
In this course, you will learn Python libraries from scratch. So, if you don't have coding experience, that is very fine.NumPy and Pandas are necessary libraries to do data analysis and preprocessing. In these course, most important concepts will be covered and after completing Pandas lectures, you will do Data Analysis exercise using Pandas for test score dataset. This is important step and aims to polish up your data preprocessing skill.Then, we will learn Matplotlib which is fundamental package for data visualization. In these lectures, we will learn all necessary concepts for data visualization.After, we will dive into Seaborn, statistical package with beautiful charts. First we will explore most important and used charts using Seaborn's built-in dataset - tips. After completing these lectures, we will dive into full data analysis and visualization exercise using complex datasets.Our first full data analysis exercise will be done using Netflix dataset where you will see how to do complex data preprocessing and applying Matplotlib functions to draw charts on progression and history.For second data analysis, dataset about diamond was used where you will explore Seaborn's full possibility.After completing this course, you will learn not only how to do everything correct statistically, but also common mistakes people often do during their analysis work.
