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Seaborn Setup: Tools, Data Prep & EDA for Visualization

本课程是 Seaborn Python Data Visualization & Analysis 专项课程的一部分

位教师：EDUCBA

包含在中

了解更多

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Configure Python and Seaborn to prepare and explore census datasets.
Generate scatter, violin, heatmap, and multivariate visualizations.
Enhance plots for readability and interpret data insights effectively.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

7 项作业

授课语言：英语（English）

91% of learners achieved a positive career outcome

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积累特定领域的专业知识

本课程是 Seaborn Python Data Visualization & Analysis 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有2个模块

This hands-on course teaches learners how to prepare, analyze, and visually interpret data using Python’s Seaborn library, with a focus on census datasets. Beginning with foundational setup—such as installing Anaconda, configuring Jupyter Notebook, and loading libraries—the course progresses into exploratory data analysis and practical visualization techniques.

Learners will gain proficiency in generating a range of plots including scatter plots, line graphs, swarm plots, violin plots, heatmaps, and advanced visual grids. Emphasis is placed on enhancing plot readability through axis formatting, label alignment, and plot configuration to support data storytelling. Throughout the course, learners will apply Bloom’s Taxonomy skills such as identifying trends (Understand), configuring tools (Apply), modifying visuals (Analyze), and interpreting relationships (Evaluate). Ideal for data enthusiasts and analysts, this course equips learners to effectively visualize multivariate data, uncover insights, and support data-driven decision-making.

单元详情

This module introduces learners to the foundational setup required for performing data visualization using Seaborn on census datasets. It covers essential technical prerequisites including tool installation, library setup, environment management, and preliminary data preparation. Learners will install necessary software, configure a Python environment using Anaconda and Jupyter Notebook, and explore the structure and purpose of the dataset. The module also walks through the beginning stages of exploratory data analysis (EDA), including understanding data structures and manipulating datasets to prepare them for visualization in later modules.

涵盖的内容

5个视频1篇阅读材料3个作业

5个视频总计29分钟

Introduction of Project2分钟
Installation of Tools7分钟
Libraries8分钟
Exploratory Data Analysis6分钟
Add Columns to Dataset6分钟

1篇阅读材料总计10分钟

Laying the Groundwork: Tools, Data Prep, and EDA for Reliable Visualizations10分钟

3个作业总计50分钟

Introduction and Setup10分钟
Working with Data10分钟
Preparing the Data and Essential Tools30分钟

This module explores advanced data visualization techniques using Seaborn to analyze census data. Learners will apply core and advanced plotting tools to generate meaningful visual interpretations, manage axis readability, and derive statistical insights through categorical and continuous data relationships. The focus includes creating scatter plots, line plots, swarm plots, violin plots, point plots, heatmaps, and grid-based multivariate plots, with an emphasis on enhancing plot clarity and interpretability.