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Ultimate Seaborn: Data Visualization with Python Seaborn

Data visualization using Python Seaborn in under two lines of code

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Seaborn is the perfect library for a beginner in Data Science

Reason 1: High-level plotting interface

Seaborn is a high-level plotting interface which simplifies plotting capabilities for beginners to data visualization greatly. The Python Seaborn library is often learn AFTER a user has studied Matplotlib. However, learning Seaborn first instead could greatly accelerate the development of an intuition in working with different types of data since the bulk of constructing a plot has been integrated into Seaborn's high-level plotting interface.

Also Seaborn has opinionated defaults which uses semantic tenets like color, size and style to communicate information - functional as opposed to purely aesthetic. Seaborn does this by inferring the datatype and then making choices: such as choosing the right color palette to display numerical information or categorical information.

Reason 2: Wide and long-form dataframes

Seaborn can be easily used for both wide and long form dataframes. The course contains a portion on transforming data from wide to long-form data to better leverage Seaborn's plotting functionalities using Pandas.

Reason 3: Inbuilt datasets

Seaborn's inbuilt datasets like the Tips dataset, the Iris and Penguins datasets contain a mix of categorical and continuous numerical variables allowing for an exploration of the distribution, categorical, regression and relational plots, together with the plotting of multiples and facet plots. A level of familiarity with the datasets (and a commitment to explore and practice with different datasets) will accelerate the development of an intuition on how best to navigate a previously unseen dataset.

Reason 4: Aesthetically pleasing production quality plots

Seaborn's plots are built to be aesthetically pleasing through the use of its color palettes, themes, styles etc. Seaborn is the library where a complete beginner can begin producing production-ready plots almost immediately after completion of the course.

The course contains a combination of code walkthroughs which show the user how to enhance a plot  + high-level thinking and an intuition to convey relevant information, depending on the decision-maker and stakeholders and the purpose of the visualization.

The course is delivered on Google Colab and uses a range of inbuilt datasets from Seaborn. The course also includes a presentation on Autoviz, an automated data visualization library to introduce the learner to the process through which visualization can be entirely automated.

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