Matplotlib with Python: E-commerce Data Visualization

本课程是 Matplotlib: Python Data Visualization & Wrangling 专项课程的一部分

位教师：EDUCBA

访问权限由 New York State Department of Labor 提供

2个模块

深入了解一个主题并学习基础知识。

6 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。

6 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Prepare and explore e-commerce datasets using Python and Matplotlib basics.
Create line, bar, scatter, pie, and box plots to analyze trends and insights.
Interpret patterns, distributions, and outliers for actionable business decisions.

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

8 项作业

授课语言：英语（English）

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Matplotlib: Python Data Visualization & Wrangling 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 course provides a practical, hands-on case study in e-commerce data analysis using Matplotlib with Python. Designed at the intermediate level, it equips learners with the skills to set up the environment, explore datasets, and construct compelling visualizations that reveal meaningful business insights.

In Module 1, learners will demonstrate the installation of Anaconda and Matplotlib, analyze e-commerce datasets by identifying unique values and preparing data, and apply visualization basics by working with figures, axes, and plotting approaches. In Module 2, learners will construct line graphs and histograms to interpret trends and data distributions, apply bar graphs and scatter plots to compare categories and evaluate relationships between variables, and create pie charts and boxplots to assess proportions, quartiles, and outliers for statistical insights. By the end of this course, learners will be able to analyze raw e-commerce datasets, apply Matplotlib techniques to construct visualizations, and evaluate patterns for actionable insights — essential skills for anyone pursuing data science, business analytics, or Python visualization projects.

This module introduces learners to the foundational concepts and tools required to perform e-commerce data analysis with Python and Matplotlib. Students will begin by setting up the project environment, installing essential software, and preparing the dataset for analysis. The lessons progressively cover inline functions, unique value identification, and data preprocessing for effective handling of real-world datasets. Finally, the module establishes a strong base in visualization principles, explaining figures, axes, and object-oriented plotting methods in Matplotlib. By the end of this module, learners will have the skills to set up their environment, explore datasets, and apply basic visualization techniques for business insights.

涵盖的内容

8个视频4个作业

8个视频总计63分钟

Introduction to project 2分钟
Installation of Software's 5分钟
Installation of Anaconda and Code 8分钟
Inline Function 11分钟
Unique Value 10分钟
Prices Condition 9分钟
Understanding Basics of Graph 11分钟
Data Visualization 7分钟

4个作业总计60分钟

Project Setup and Tools 10分钟
Data Exploration and Preparation 10分钟
Introduction to Visualization Concepts 10分钟
Foundations of E-commerce Data Analysis 30分钟

This module focuses on applying Matplotlib to create meaningful and professional visualizations for e-commerce datasets. Learners will progress from plotting simple line graphs to more advanced techniques including histograms, bar graphs, scatter plots, pie charts, and boxplots. Each lesson demonstrates how to interpret data visually, customize graphs with parameters, and highlight key insights for business decision-making. By completing this module, learners will develop the ability to transform raw e-commerce data into clear, visually appealing insights using Matplotlib.