Python Seaborn for Course

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... In this Python coding tutorial, we take a look at how you can use Python Seaborn to build out quick graphs for data. Seaborn is a library many Data Analysts and Scientists use on a daily basis. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg In this comprehensive Python Seaborn tutorial, I walk you through everything you need to know about the Seaborn library—a powerful data visualization tool built on top of Matplotlib. We start with installation and basic setup in a Jupyter Notebook, then dive into importing Seaborn datasets like MPG and Tips to demonstrate real-world examples. Throughout this video, you'll learn how to create over a dozen different chart types, including scatter plots, line plots, histograms, KDE plots, box plots, violin plots, heatmaps, and more. I cover essential customization techniques like setting themes, adjusting figure sizes, changing color palettes, and using hue to separate categorical data. You'll also discover advanced visualizations like joint plots, pair plots, and facet grids that let you analyze multiple variables simultaneously. I explain how to build correlation heatmaps for data exploration, create distribution plots with fill options, and combine different plot types for richer insights. Each example uses simple, one-line code when possible, but I also show you how to layer complexity when needed. By the end of this Python Seaborn tutorial, you'll be able to confidently visualize data, customize your graphs for presentations, and decide which chart type best tells your data's story. TIMESTAMPS 00:00 Introduction to Seaborn Library 00:27 Installing and Importing Seaborn 01:20 Loading Data from Seaborn 02:40 Exploring the MPG Dataset 03:00 Setting Themes and Styles 05:17 Context and Font Scaling 06:53 Creating a Correlation Heatmap 07:51 Building a Scatter Plot 09:19 Adding Hue for Categories 10:05 Customizing Figure Size and Palettes 12:25 Line Plots and Point Plots 14:19 KDE Plots and Histograms 17:19 Understanding Histogram Variations 19:05 ECDF and Rug Plots 21:13 Count Plots and Bar Plots 22:30 Box Plots and Violin Plots 24:22 Strip Plots 25:11 Joint Plots 27:23 Pair Plots 30:20 Loading Tips Dataset 30:51 Creating Relational Plots 32:25 Facet Grids 35:00 Mapping Data to Facet Grids OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.