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Ultimate Seaborn: Data Visualization with Python's Seaborn

Ultimate Seaborn: Data Visualization with Python's Seaborn

Seaborn is an amazing visualization library for statistical graphics plotting in Python. It is built on the top of matplotlib library and also closely integrated into the data structures from pandas.

What you'll learn

  • Ask the right questions about the data using summary statistics and Visual Exploratory Data analysis to gain accelerated insight into the data
  • Generate distribution, categorical, relational and regression plots to learn more about the variables in the dataset
  • Display maximum information using not only color, size and shape, but the power of multiples
  • Leverage the power of multiples, and apply aesthetic abilities functionally for effective data storytelling
  • Develop an intuition behind some automated visualization libraries like Autoviz to replicate the workflow for your own dataset

Requirements

  • Basics of Python - an introduction to the Pandas library is included. However Seaborn is easy to learn because of its high level interface.

Curiosity and an interest in exploratory data analysis

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Description

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.

Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes.

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