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Python for Data Visualization and Analysis

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Python for Data Visualization and Analysis

Instructor: Edureka

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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
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.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

Build your subject-matter expertise

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
  • Knowledge Check : Visualizing Data with Matplotlib•20 minutes
  • Practice Quiz : Setting Up Matplotlib•3 minutes
  • Practice Quiz : Types of Plots and Charts•3 minutes
  • Practice Quiz : Plotting Different Charts•3 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
  • Knowledge Check : Visualizing Data with Seaborn•20 minutes
  • Practice Quiz : Seaborn Library•3 minutes
  • Practice Quiz : Plot Types in Seaborn•3 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
  • Knowledge Check : Interactive Visuals with Plotly and IPyWidgets•20 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
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
203 Courses•185,285 learners

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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.

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

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.

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

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

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

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

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

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.

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.

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.

Financial aid available,