Ultimate Seaborn: Data Visualization with Python Seaborn

Are you interested in learning how to create beautiful and informative data visualizations using Python? Look no further than this comprehensive course on Seaborn!

With over seven hours of content, this course is designed for beginners who are looking to learn the principles underlying data visualization.

Whether you're a student, a data analyst, or a business professional, this course will give you the skills you need to create stunning visualizations that will bring your data to life.

One of the major benefits of this course is that we use inbuilt datasets, so you don't have to worry about downloading data or running into issues with libraries like pip. This means you can focus on the content and the learning experience, without getting bogged down by technical issues.

In this course, you'll learn:

• The principles of data visualization and the importance of choosing the right chart for your data

• How to work with different types of variables (categorical, quantitative, and temporal) and how to create charts that are appropriate for each type

• The basics of Seaborn and how to use this powerful library to create stunning visualizations in Python by leveraging its opinionated defaults

• How to create different types of charts, including bar plots, line plots, scatter plots, heatmaps, and more

• How to customize your visualizations to make them more informative and engaging

By the end of this course, you'll have the skills you need to create stunning data visualizations that will impress your colleagues and stakeholders. Whether you're working on a research project, building a business dashboard, or simply exploring data for fun, this course will help you take your visualizations to the next level.

Seaborn is the perfect library for a beginner in Data Science.

Generally, new learners start with Matplotlib and spends a time learning syntax. With Seaborn, you can generate publication-quality figures in under two lines of code.

Here are four reasons why.

Reason 1: High-level plotting interface

Seaborn is a high-level plotting interface that simplifies plotting for beginners.

The Python Seaborn library is often learned AFTER a user has studied Matplotlib.

However, learning Seaborn first instead could accelerate picking up an intuition in working with different types of data.

This is because the bulk of constructing a plot has been integrated into Seaborn's high-level plotting interface - so you don't have to construct the plot from scratch and can focus instead on communicating maximum information about the variables in your dataset.

You can then leverage "opinionated defaults" in Seaborn...

Seaborn uses semantic tenets like color, size, and style to communicate information in a functional manner (not just aesthetic).

Seaborn does this by inferring the datatype and then making smart 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 Python Pandas.

Reason 3: Inbuilt datasets

We use Google Colab together with Seaborn's inbuilt datasets.

Sometimes, beginners get frustrated trying to import data, and clean data before being able to explore the dataset.

Seaborn's inbuilt datasets like the Tips dataset, and 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) can ease a complete beginner into rapidly exploring 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.