Learn By Example: Seaborn

As organization collect huge amounts of data, it has become increasingly important for data analysts and scientists to explore and visualize this data before performing further analysis. Seaborn is a visualization library especially built for statistical analysis which has higher level APIs allows you to visualize the relationships in your data. Seaborn is an integral part of the PyData stack and is closely integrated with other Python libraries such as Pandas and NumPy.

Here is what this course covers:

Histograms and Kernel Density Estimation: Use high-level APIs to display regression plots and KDE curves

Univariate and bi-variate relationships: Find linear relationships between multiple variables

Pairwise relationships: Use the FacetGrid and PairGrid to find relationships between pairs of features

Themes, styles and color palettes: Customize your visualizations using different colors, themes and figure styles

This course is built around hands on demos, built to explicitly explain concepts underpinning each topic. Real-world datasets are used where possible.

Software used: Python 3, Seaborn