Python Data Visualization with IBM
Master data visualization techniques using Python libraries like Matplotlib, Seaborn, and Folium to create compelling visual representations of data.
Master data visualization techniques using Python libraries like Matplotlib, Seaborn, and Folium to create compelling visual representations of data.
This comprehensive course teaches the essential skills of data visualization using Python. Students learn to leverage powerful libraries including Matplotlib, Seaborn, and Folium to create impactful visual representations of data. The curriculum covers basic to advanced visualization techniques, from simple line plots to complex geospatial visualizations. Participants gain hands-on experience with various chart types, including histograms, scatter plots, and choropleth maps. The course emphasizes practical applications and storytelling through data, enabling learners to create compelling visualizations for effective communication of insights. Access to IBM Watson Studio provides additional opportunities for real-world practice and collaboration.
4.6
(108 ratings)
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Instructors:
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What you'll learn
Use Matplotlib, Seaborn, and Folium libraries for creating impactful data visualizations
Create basic visualizations including area plots, histograms, and bar charts
Develop specialized charts such as pie charts, box plots, and scatter plots
Implement advanced visualization techniques including waffle charts and word clouds
Design interactive maps and geospatial visualizations
Apply data visualization best practices for effective communication
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 5 modules in this course
This course provides a comprehensive introduction to data visualization using Python. Students learn to create various types of visualizations using popular libraries like Matplotlib, Seaborn, and Folium. The curriculum progresses from basic plotting techniques to advanced visualization tools, including geospatial data representation. Each module builds upon previous knowledge, covering essential chart types and their applications in data analysis. The course emphasizes practical skills through hands-on exercises and real-world examples, enabling learners to effectively communicate data insights through visual storytelling.
Introduction to Visualization Tools
Module 1
Basic Visualization Tools
Module 2
Specialized Visualization Tools
Module 3
Advanced Visualization Tools
Module 4
Creating Maps and Visualizing Geospatial Data
Module 5
Fee Structure
Instructors
Dr. Alex Aklson: Crafting Data-Driven Solutions and Innovating Smart Health Systems at IBM
Dr. Alex Aklson is a data scientist in IBM Canada’s Digital Business Group, where he has contributed to innovative projects, including the development of a smart system to detect early signs of dementia by analyzing walking speed and home activity patterns in older adults. Prior to IBM, Alex worked at Datascope Analytics in Chicago, where he crafted data-driven solutions using a human-centered approach. He holds a Ph.D. in Biomedical Engineering from the University of Toronto.
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
Testimonials
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4.6 course rating
108 ratings
Frequently asked questions
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