Data Understanding and Visualization
Master data visualization techniques using Python libraries like Pandas, Matplotlib, and Seaborn for effective data analysis and communication.
Master data visualization techniques using Python libraries like Pandas, Matplotlib, and Seaborn for effective data analysis and communication.
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 Data Wrangling 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.
Instructors:
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What you'll learn
Analyze datasets using fundamental statistical measures
Create effective visualizations using Pandas functionality
Develop custom plots and charts with Matplotlib
Build advanced visualizations using Seaborn
Choose appropriate visualization types for different data scenarios
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
4 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course combines statistical understanding with practical data visualization skills using Python. Students learn essential statistical concepts including measures of central tendency, variation, and correlation, while mastering visualization techniques using popular Python libraries. The curriculum covers data manipulation with Pandas, creating basic to advanced visualizations with Matplotlib, and developing sophisticated data presentations using Seaborn. Through hands-on exercises and case studies, participants learn to choose appropriate visualization methods and create compelling data stories.
Data Statistics
Module 1 · 6 Hours to complete
Data Visualization with Pandas
Module 2 · 5 Hours to complete
Data Visualization with Matplotlib
Module 3 · 5 Hours to complete
Data Visualization with Seaborn
Module 4 · 7 Hours to complete
Fee Structure
Instructor
Teaching Assistant Professor
Dr. Di Wu is a Teaching Assistant Professor at the University of Colorado Boulder, specializing in data science and computer science. His primary research interests include temporal databases, the semantic web, knowledge representation, and data science, with a focus on extending the Resource Description Framework (RDF) for temporal dimensions. Before joining CU Boulder, he taught various courses such as algorithms and data structures, programming languages, and database management. Dr. Wu aims to develop an inclusive and engaging pedagogy in data science education over the next five years, emphasizing experiential learning in both in-person and online formats. He is involved in teaching courses related to data science and programming, including specializations in Python programming for data scientists.
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