Applied Plotting, Charting & Data Representation in Python
Master data visualization in Python using matplotlib. Learn to create effective charts and plots for data science.
Master data visualization in Python using matplotlib. Learn to create effective charts and plots for data science.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Data Science with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Create effective data visualizations using matplotlib
Implement best practices for basic and advanced charting
Design informative and truthful visual representations
Develop custom visualizations for specific research questions
Master various chart types including scatter plots, line charts, and heatmaps
Apply animation and interactive features to data visualizations
Skills you'll gain
This course includes:
3.2 Hours PreRecorded video
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on data visualization using Python's matplotlib library. Students learn fundamental principles of information visualization, best practices for creating basic charts, and advanced visualization techniques. The curriculum covers everything from design principles and graphical heuristics to practical implementation of various chart types. Through hands-on projects using real-world data, learners develop skills in creating effective visualizations, from basic plots to complex composite charts and geographic visualizations.
Principles of Information Visualization
Module 1 · 3 Hours to complete
Basic Charting
Module 2 · 7 Hours to complete
Charting Fundamentals
Module 3 · 8 Hours to complete
Applied Visualizations
Module 4 · 4 Hours to complete
Fee Structure
Instructor
Associate Professor at the University of Michigan
Christopher Brooks is an Associate Professor in the School of Information at the University of Michigan, where he specializes in designing tools to enhance teaching and learning experiences in higher education. His research focuses on the application of learning analytics within human-computer interaction, utilizing methods from educational data mining, machine learning, and information visualization. Brooks has published extensively in these areas and is actively involved in directing the Educational Technology Collective, which includes postdoctoral scholars and students collaborating on innovative projects. He teaches various courses related to applied data science and has contributed to online education platforms such as Coursera. His work aims to leverage data to improve educational outcomes and foster better learning environments.
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4.5 course rating
6,246 ratings
Frequently asked questions
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