Learn to Master Data Visualization and perform Exploratory Data Analysis (EDA) using Python, Matplotlib and Seaborn
InstructorIdan GabrieliLessons are organized by chart purpose, covering ranking, proportion, trend, distribution, and correlation.
The curriculum includes article-based exercises distributed across the different visualization sections to reinforce learning.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This course slide introduces Level 3 on Data Visualization, displaying graphical representations like pie, bar, and donut charts to illustrate concepts in Exploratory Data Analysis.
This course slide introduces the data visualization module for Level 3, highlighting the use of Matplotlib and Seaborn libraries as key tools for creating visual representations.
This visual aid outlines the course structure as a sequential learning path, displaying distinct levels from L1 through L4 to illustrate the step-by-step progression of the curriculum.
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The curriculum focuses on charting and visualizations rather than machine learning theory or model training. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Some code may require manual updates via documentation due to library version changes. Note that a signal regarding deprecated syntax was provided before the displayed update date, which does not prove a correction has occurred.
Selected from the course's public promotional preview. These images document visible presentation material only; they do not represent the complete paid curriculum.
The content is highly relevant for visualization but may not align with the 'Machine Learning' title. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.