Python for Data Visualization and Analysis
This course is part of Applied Data Analytics.
This hands-on course provides comprehensive training in data visualization and exploratory data analysis using Python's most powerful libraries. Students will learn to create stunning static visualizations with Matplotlib and Seaborn, develop interactive charts and dashboards with Plotly and IPyWidgets, and deploy shareable web applications using Streamlit. The curriculum emphasizes practical skills for selecting appropriate chart types, customizing visuals for maximum impact, and extracting meaningful insights from complex datasets. Through guided demonstrations and hands-on projects, participants will master techniques for transforming raw data into compelling visual narratives that facilitate better decision-making. By course completion, learners will possess the technical proficiency to create professional-quality visualizations and interactive dashboards that effectively communicate data stories.
Instructors:
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What you'll learn
Create impactful static visualizations using Matplotlib for different data types and relationships
Develop sophisticated statistical visualizations efficiently with Seaborn
Build interactive charts and dashboards with Plotly for enhanced data exploration
Create dynamic data applications with customizable controls using IPyWidgets
Deploy interactive visualization applications with Streamlit to share results
Select appropriate chart types for different data scenarios and analysis goals
Skills you'll gain
This course includes:
3.2 Hours PreRecorded video
13 assignments
Access on Mobile, Tablet, Desktop
Batch access
Shareable certificate
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There are 4 modules in this course
This course provides a comprehensive exploration of data visualization and exploratory data analysis using Python. The curriculum is structured around three main visualization libraries: Matplotlib, Seaborn, and Plotly. Beginning with fundamental visualization concepts, students learn to create various charts and plots using Matplotlib, including line graphs, bar charts, histograms, scatter plots, pie charts, and box plots. The course then advances to Seaborn for creating more sophisticated statistical visualizations with less code. The final portion focuses on interactive visualization techniques using Plotly and IPyWidgets, culminating in building deployable web applications with Streamlit. Throughout the course, students apply these tools to real-world datasets, developing practical skills in selecting appropriate visualization methods and customizing visuals for maximum impact and clarity.
Visualization with Matplotlib
Module 1 · 3 Hours to complete
Data Visualization with Seaborn
Module 2 · 1 Hours to complete
Interactive Data Visualization
Module 3 · 3 Hours to complete
Course Wrap-Up and Assessment
Module 4 · 1 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Applied Data Analytics
Instructor
Inspiring the Next Generation of Tech Professionals
Edureka is dedicated to providing high-quality, instructor-led online training, empowering professionals to enhance their skills in various domains. The platform features a diverse team of experienced instructors who are passionate about teaching and possess extensive industry knowledge. These instructors facilitate a wide range of courses covering topics such as data science, artificial intelligence, machine learning, and cloud computing. Edureka's commitment to education is reflected in its innovative approach to learning, which includes interactive sessions, real-world projects, and 24/7 support for students. By fostering a collaborative learning environment, Edureka ensures that learners not only acquire technical skills but also develop critical thinking and problem-solving abilities essential for success in today's fast-paced job market.
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Frequently asked questions
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