This course offers a hands-on introduction to data visualization and exploratory data analysis (EDA) using Python's most popular libraries. You'll dive deep into creating stunning visuals with Matplotlib and Seaborn, building interactive charts and dashboards with Plotly, and conducting EDA on complex datasets through advanced graphical methods. Explore how to present data effectively and gain actionable insights from visual representations.
Python for Data Visualization and Analysis
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Python for Data Visualization and Analysis
This course is part of Applied Data Analytics Specialization
Instructor: Edureka
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
9 hours to complete
3 weeks at 3 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
Details to know
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Recently updated!
December 2024
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.
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There are 4 modules in this course
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
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
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
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
Recommended if you're interested in Data Analysis
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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.