Who is this course for?

This course is ideal for data analysts, aspiring data scientists, and Python programmers who want to develop skills in data visualization and exploratory data analysis using Python. A basic understanding of Python programming and familiarity with libraries like Pandas or NumPy is recommended.

Do I need prior experience in data visualization?

No prior experience in data visualization is required. This course provides a step-by-step approach, starting with foundational concepts and progressing to advanced techniques using tools like Matplotlib, Seaborn, and Plotly.

What practical skills will I gain from this course?

By the end of the course, you’ll be able to:Design professional and interactive data visualizations.Perform EDA to uncover patterns and trends in data.Deploy data visualization applications using Streamlit.

What are the prerequisites for this course?

A basic understanding of Python programming and familiarity with data manipulation libraries such as Pandas or NumPy is recommended.

How can I apply the skills learned in this course?

The skills you acquire will be valuable for roles in data analytics, business intelligence, and data science. You can use these skills to create impactful visualizations, analyze complex datasets, and communicate insights effectively.

Which Python libraries will I use for visualization and interactive dashboards?

You’ll work with Matplotlib, Seaborn, Plotly, and Streamlit to create both static and interactive data visualizations.

Can I build interactive dashboards and deploy them with Streamlit?

Yes, you’ll learn how to design interactive dashboards in Streamlit and deploy them to share with others.

Do I need prior experience with data manipulation libraries like Pandas or NumPy?

No prior experience is required. The course introduces Pandas and NumPy from scratch and uses them in practical examples.

What career opportunities can data visualization skills open up?

Data visualization skills qualify you for roles like Data Analyst, Business Intelligence Developer, and Data Scientist.

Will I earn a certificate after completing the course?

Yes, you’ll receive a Coursera certificate to showcase your skills to employers and share on LinkedIn.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Python for Data Visualization and Analysis

本课程是 Applied Data Analytics 专项课程的一部分

位教师：Edureka

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Create impactful visualizations using Matplotlib and Seaborn to represent complex datasets effectively.
Build dynamic, interactive charts and dashboards with Plotly and IPyWidgets for enhanced data exploration.
Build dynamic, interactive charts and dashboards with Plotly and IPyWidgets for enhanced data exploration.
Deploy interactive data visualization applications seamlessly with Streamlit to share analysis results.

您将获得的技能

您将学习的工具

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

13 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

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本课程是 Applied Data Analytics 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。

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获得对主题或工具的基础理解
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获得可共享的职业证书

该课程共有4个模块

This Python for Data Visualization Analysis course provides a practical introduction to data visualization and exploratory data analysis (EDA) using Python. You will work with Matplotlib and Seaborn to create clear and effective visualizations, use Plotly to build interactive charts and dashboards, and apply advanced graphical techniques for EDA on complex datasets. Learn to present data clearly and extract meaningful insights through visual analysis.

By the end of this course, you’ll be able to: - Understand the importance of various visualization techniques. - Select appropriate chart types for visualizing diverse datasets. - Create professional-quality visuals with Matplotlib, Seaborn, and Plotly. - Develop interactive dashboards and visuals with Plotly and IPyWidgets. - Perform EDA on complex datasets and deploy the results using Streamlit. This course is ideal for learners with foundational knowledge of Python programming and a basic understanding of data manipulation. Familiarity with libraries such as Pandas or NumPy is recommended. Whether you're a data analyst, aspiring data scientist, or Python programmer looking to sharpen your data visualization skills, this course equips you with the tools to transform raw data into meaningful stories. Elevate your data analysis journey—enroll in Data Visualization and Exploratory Data Analysis with Python today!

单元详情

In this module, learners will explore how to create various types of visualizations using Matplotlib. They will learn to apply these visuals to complex datasets, uncovering hidden insights that facilitate informed decision-making.

涵盖的内容

17个视频5篇阅读材料4个作业1个讨论话题

17个视频总计52分钟

Course Introduction3分钟
Environment Set-Up3分钟
Importance of Data Visualization2分钟
Line Plot2分钟
Bar Chart2分钟
Horizontal Bar Chart2分钟
Stacked Bar Chart1分钟
Histogram2分钟
Demonstration: Plotting Line and Bar Graph6分钟
Demonstration: Plotting Histogram5分钟
Scatter Plot3分钟
Pie Chart3分钟
Box Plot4分钟
Customizing Charts2分钟
Demonstration: Pie Chart3分钟
Demonstration: Scatter Plot and Box Plot4分钟
Summary of Visualization with Matplotlib3分钟

5篇阅读材料总计95分钟

Welcome to Python for Data Visualization and Analysis10分钟
From Numbers to Narratives20分钟
Choosing the Right Chart: Bar Charts, Line Charts and Histogram25分钟
Choosing the Right Chart Type20分钟
Choosing the Right Chart: Scatter, Pie and Box20分钟

4个作业总计29分钟

Knowledge Check : Visualizing Data with Matplotlib20分钟
Practice Quiz : Setting Up Matplotlib3分钟
Practice Quiz : Types of Plots and Charts3分钟
Practice Quiz : Plotting Different Charts3分钟

1个讨论话题总计10分钟

Introduce Yourself10分钟

In this module, learners will delve into data visualization with Seaborn, mastering the creation of diverse plots while developing skills to customize and refine visuals for improved presentation and interactivity.

涵盖的内容

12个视频2篇阅读材料3个作业1个讨论话题

12个视频总计41分钟

What is Seaborn?2分钟
Installing and Setting Up Seaborn2分钟
Comparing Seaborn with Matplotlib2分钟
Relational Plot (Rel plot)2分钟
Distribution Plot (Dist Plot)2分钟
Categorical Plot (Cat Plot)3分钟
Demonstration: Visualizing Charts with Seaborn5分钟
Demonstration: Visualizing HeatMap2分钟
Demonstration: Category, Relational and Distribution Plots 6分钟
Demonstration: Personalizing Charts and Visuals7分钟
Demonstration: Tailoring Graphs and Visuals6分钟
Summary for Data Visualization with Seaborn 3分钟

2篇阅读材料总计40分钟

Seaborn with Matplotlib20分钟
A Guide to Seaborn20分钟

3个作业总计26分钟

Knowledge Check : Visualizing Data with Seaborn20分钟
Practice Quiz : Seaborn Library3分钟
Practice Quiz : Plot Types in Seaborn3分钟

1个讨论话题总计10分钟

Which of the following is easier to use, Seaborn or Matplotlib?10分钟

In this module, learners will explore how to create interactive plots using Plotly, enhance exploratory data analysis (EDA) with IPyWidgets, and build shareable web applications with Streamlit. They will also gain the skills to develop dynamic dashboards and interactive reports for effective data presentation.

涵盖的内容

24个视频3篇阅读材料5个作业1个讨论话题

24个视频总计99分钟

Plotly4分钟
Customizing Basic Plot - Background and Markers4分钟
Customizing Basic Plot - Lines, Titles and Labels 3分钟
Customizing Basic Plot - Interactive3分钟
Interactive Plots3分钟
Demonstration: Plots with Hover Feature3分钟
Demonstration: Customizing Hover Features and Tooltips4分钟
Plotly Dash6分钟
Demonstration: Defining Layout and Structure4分钟
Demonstration: Building Web Apps 4分钟
Demonstration: Chaining Callbacks4分钟
Demonstration: Multiple Inputs and Outputs with Interactions4分钟
Demonstration: Importing Airbnb Data2分钟
Demonstration: Web App for Airbnb Data4分钟
IPyWidgets5分钟
Displaying Widgets Layouts and Container Widgets4分钟
Interactive Controls Combining Multiple Widgets for Interactivity5分钟
Custom Widgets Creating and Registering Custom Widgets4分钟
Extending Widget Functionality5分钟
What is Streamlit?2分钟
Demonstration: Code Details 6分钟
Demonstration: Executing the App4分钟
Demonstration: Data Visualization on Streamlit6分钟
Summary for Interactive Data Visualization3分钟

3篇阅读材料总计40分钟

Turning Static Plots Interactive20分钟
Plotly dash: Best Practices10分钟
Building with Streamlit10分钟

5个作业总计32分钟

Knowledge Check : Interactive Visuals with Plotly and IPyWidgets20分钟
Practice Quiz : Plotly Library3分钟
Practice Quiz : Plotly Dashboard3分钟
Practice Quiz : Working of IPyWidgets3分钟
Practice Quiz : Streamlit3分钟

1个讨论话题总计10分钟

How do IPyWidgets enhance the interactivity of Jupyter Notebook projects?10分钟

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on data visualization concepts, Matploltlib, Seaborn, Plotly and Association rule mining.