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.

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

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Python for Data Visualization and Analysis

Q: 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.

This course is part of Applied Data Analytics Specialization

Instructor: Edureka

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

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This course is part of the Applied Data Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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!

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.

What's included

17 videos5 readings4 assignments1 discussion prompt

17 videos Total 52 minutes

Course Introduction 3 minutes
Environment Set-Up 3 minutes
Importance of Data Visualization 2 minutes
Line Plot 2 minutes
Bar Chart 2 minutes
Horizontal Bar Chart 2 minutes
Stacked Bar Chart 1 minute
Histogram 2 minutes
Demonstration: Plotting Line and Bar Graph 6 minutes
Demonstration: Plotting Histogram 5 minutes
Scatter Plot 3 minutes
Pie Chart 3 minutes
Box Plot 4 minutes
Customizing Charts 2 minutes
Demonstration: Pie Chart 3 minutes
Demonstration: Scatter Plot and Box Plot 4 minutes
Summary of Visualization with Matplotlib 3 minutes

5 readings Total 95 minutes

Welcome to Python for Data Visualization and Analysis 10 minutes
From Numbers to Narratives 20 minutes
Choosing the Right Chart: Bar Charts, Line Charts and Histogram 25 minutes
Choosing the Right Chart Type 20 minutes
Choosing the Right Chart: Scatter, Pie and Box 20 minutes

4 assignments Total 29 minutes

Practice Quiz : Setting Up Matplotlib 3 minutes
Practice Quiz : Types of Plots and Charts 3 minutes
Practice Quiz : Plotting Different Charts 3 minutes
Knowledge Check : Visualizing Data with Matplotlib 20 minutes

1 discussion prompt Total 10 minutes

Introduce Yourself 10 minutes

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.

What's included

12 videos2 readings3 assignments1 discussion prompt

12 videos Total 41 minutes

What is Seaborn? 2 minutes
Installing and Setting Up Seaborn 2 minutes
Comparing Seaborn with Matplotlib 2 minutes
Relational Plot (Rel plot) 2 minutes
Distribution Plot (Dist Plot) 2 minutes
Categorical Plot (Cat Plot) 3 minutes
Demonstration: Visualizing Charts with Seaborn 5 minutes
Demonstration: Visualizing HeatMap 2 minutes
Demonstration: Category, Relational and Distribution Plots 6 minutes
Demonstration: Personalizing Charts and Visuals 7 minutes
Demonstration: Tailoring Graphs and Visuals 6 minutes
Summary for Data Visualization with Seaborn 3 minutes

2 readings Total 40 minutes

Seaborn with Matplotlib 20 minutes
A Guide to Seaborn 20 minutes

3 assignments Total 26 minutes

Practice Quiz : Seaborn Library 3 minutes
Practice Quiz : Plot Types in Seaborn 3 minutes
Knowledge Check : Visualizing Data with Seaborn 20 minutes

1 discussion prompt Total 10 minutes

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

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.

What's included

24 videos3 readings5 assignments1 discussion prompt

24 videos Total 99 minutes

Plotly 4 minutes
Customizing Basic Plot - Background and Markers 4 minutes
Customizing Basic Plot - Lines, Titles and Labels 3 minutes
Customizing Basic Plot - Interactive 3 minutes
Interactive Plots 3 minutes
Demonstration: Plots with Hover Feature 3 minutes
Demonstration: Customizing Hover Features and Tooltips 4 minutes
Plotly Dash 6 minutes
Demonstration: Defining Layout and Structure 4 minutes
Demonstration: Building Web Apps 4 minutes
Demonstration: Chaining Callbacks 4 minutes
Demonstration: Multiple Inputs and Outputs with Interactions 4 minutes
Demonstration: Importing Airbnb Data 2 minutes
Demonstration: Web App for Airbnb Data 4 minutes
IPyWidgets 5 minutes
Displaying Widgets Layouts and Container Widgets 4 minutes
Interactive Controls Combining Multiple Widgets for Interactivity 5 minutes
Custom Widgets Creating and Registering Custom Widgets 4 minutes
Extending Widget Functionality 5 minutes
What is Streamlit? 2 minutes
Demonstration: Code Details 6 minutes
Demonstration: Executing the App 4 minutes
Demonstration: Data Visualization on Streamlit 6 minutes
Summary for Interactive Data Visualization 3 minutes

3 readings Total 40 minutes

Turning Static Plots Interactive 20 minutes
Plotly dash: Best Practices 10 minutes
Building with Streamlit 10 minutes

5 assignments Total 32 minutes

Practice Quiz : Plotly Library 3 minutes
Practice Quiz : Plotly Dashboard 3 minutes
Practice Quiz : Working of IPyWidgets 3 minutes
Practice Quiz : Streamlit 3 minutes
Knowledge Check : Interactive Visuals with Plotly and IPyWidgets 20 minutes

1 discussion prompt Total 10 minutes

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

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.

What's included

1 video1 reading1 assignment1 discussion prompt

1 video Total 2 minutes

Course Summary of Python for Data Visualization and Analysis 2 minutes

1 reading Total 30 minutes

Project : Sales Data Analysis and Visualization Dashboard 30 minutes

1 assignment Total 30 minutes

End Course Knowledge Check : Python for Data Visualization and Analysis 30 minutes

1 discussion prompt Total 10 minutes

Describe Your Learning Journey 10 minutes

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Instructor

Edureka

157 Courses 147,741 learners

Offered by

Edureka

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Frequently asked questions

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.

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.

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.

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.