Welcome to the next session on our journey through data visualization! Today, we will dip our toes into the Seaborn library, focusing on aesthetics and styling in our plots. In our previous session, we used Matplotlib to create some simple bar plots. Today, we'll see how Seaborn can help us create visually appealing plots effortlessly.

Defining an aesthetic style before creating your plot is an important aspect of any data visualization. The right choice of colors, sizes, and other aesthetic factors can make your plots more engaging, easy to interpret, and effective at conveying your intended insights.

We'll be looking at three essential elements of styling in Seaborn: figure style, color palette, and plot size. Think of it this way: you're about to paint a masterpiece, and Seaborn provides us with a studio full of tools. Are you ready to create beautiful plots with the Titanic dataset?

Seaborn is a data visualization library built on top of the Matplotlib library in Python. It offers a high-level and easier-to-use interface, as well as attractive and informative statistical graphics.

Let's start by importing Seaborn and setting the aesthetics for all the plots to .