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This course is part of Applied Data Analytics Specialization
Instructor: Edureka
Included with
Recommended experience
Intermediate level
Prior experience with Python programming and understanding of charts is recommended but not necessary
Recommended experience
Intermediate level
Prior experience with Python programming and understanding of charts is recommended but not necessary
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.
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December 2024
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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.
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.
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.
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.
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.
1 video1 reading1 assignment1 discussion prompt
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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.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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.