Ultimate Seaborn: Data Visualization with Python Seaborn

Seaborn is the perfect library for a beginner in Data Science. Everyone starts with Matplotlib and spends a lot of 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.

Who this course is for:

• For anyone looking to elevate their data visualization abilities with just a few lines of code to produce attractive visualizations
• Python developers looking to gain fluency with a visualization library
• Anyone looking to learn the basics of statistical data visualization
• Data storytellers looking to expand to using Seaborn for its high-level interface allowing one to plot attractive, information-rich plots with just a few lines of code
• Data analysts looking to learn and apply visual exploratory data analysis
• For rapid prototyping and exploration