| 1 - Environment setup and Installing Seaborn.mp4 | 11.7 MB | ||
| 1 - How To Use This Section! - Kaggle Datasets.url | 102.4 B | ||
| 1 - How To Use This Section! - Seaborn Datasets.url | 102.4 B | ||
| 1 - How To Use This Section! - StudentsPerformance.csv | 70.3 KB | ||
| 1 - How To Use This Section! - TSLA.csv | 171 KB | ||
| 1 - How To Use This Section! - corona1.csv | 1.1 MB | ||
| 1 - How To Use This Section! - diamonds.csv | 2.6 MB | ||
| 1 - How To Use This Section! - exercise.csv | 2.7 KB | ||
| 1 - How To Use This Section! - flights.csv | 2.3 KB | ||
| 1 - How To Use This Section! - healthexp.csv | 7.1 KB | ||
| 1 - How To Use This Section! - iris.csv | 3.8 KB | ||
| 1 - How To Use This Section! - tips.csv | 9.5 KB | ||
| 1 - How To Use This Section! - titanic.csv | 55.7 KB | ||
| 1 - How To Use This Section!.mp4 | 20.5 MB | ||
| 1 - Scatter Plots - Seaborn Docs.url | 102.4 B | ||
| 1 - Scatter Plots - scatterplots_example.py | 1.2 KB | ||
| 1 - Scatter Plots.mp4 | 59.1 MB | ||
| 1 - Simple Barplots - Seaborn Docs.url | 102.4 B | ||
| 1 - Simple Barplots - simple_barplots.py | 1.3 KB | ||
| 1 - Simple Barplots.mp4 | 90.9 MB | ||
| 1 - Thank You, What's Next.mp4 | 28.3 MB | ||
| 1 - Understanding Distributions.mp4 | 32.1 MB | ||
| 1 - Understanding Linear Regression - Understanding Linear Regression.url | 102.4 B | ||
| 1 - Understanding Linear Regression.mp4 | 72.8 MB | ||
| 1 - Understanding Time Series Data.mp4 | 33.8 MB | ||
| 1 - What is a Graph.mp4 | 71.3 MB | ||
| 2 - Advanced Barplots - Seaborn Docs.url | 102.4 B | ||
| 2 - Advanced Barplots - advanced_barplots.py | 1.4 KB | ||
| 2 - Advanced Barplots.mp4 | 72.8 MB | ||
| 2 - Basic Histograms - Seaborn Docs.url | 102.4 B | ||
| 2 - Basic Histograms - univariate_histograms.py | 1.1 KB | ||
| 2 - Basic Histograms.mp4 | 80 MB | ||
| 2 - Matplotlib & Seaborn - How Do They Work Together.mp4 | 50.6 MB | ||
| 2 - Plotting a single variable - Seaborn Docs.url | 102.4 B | ||
| 2 - Plotting a single variable - time_plot.py | 1.2 KB | ||
| 2 - Plotting a single variable.mp4 | 66.6 MB | ||
| 2 - Regression Plots - Seaborn Docs.url | 102.4 B | ||
| 2 - Regression Plots - regression_plot_example.py | 1.5 KB | ||
| 2 - Regression Plots.mp4 | 63.1 MB | ||
| 2 - Showing Multiple Relationships with Facetgrids - Seaborn Docs.url | 102.4 B | ||
| 2 - Showing Multiple Relationships with Facetgrids - facetgrid_example.py | 1.8 KB | ||
| 2 - Showing Multiple Relationships with Facetgrids.mp4 | 90.5 MB | ||
| 2 - Telling an Effective Story With Your Graph.mp4 | 119.2 MB | ||
| 3 - Bivariate Histograms - Seaborn Docs.url | 102.4 B | ||
| 3 - Bivariate Histograms - bivariate_histograms.py | 1.6 KB | ||
| 3 - Bivariate Histograms.mp4 | 41.2 MB | ||
| 3 - Graph types.mp4 | 89.4 MB | ||
| 3 - Linear Model Plots - Seaborn Docs.url | 102.4 B | ||
| 3 - Linear Model Plots - linear_model_plot_example.py | 1.4 KB | ||
| 3 - Linear Model Plots.mp4 | 53.9 MB | ||
| 3 - Plotting Categorical Data with Pointplots - Seaborn Docs.url | 102.4 B | ||
| 3 - Plotting Categorical Data with Pointplots - basic_pointplots.py | 1.3 KB | ||
| 3 - Plotting Categorical Data with Pointplots.mp4 | 52.9 MB | ||
| 3 - Plotting multiple variables - Seaborn Docs.url | 102.4 B | ||
| 3 - Plotting multiple variables - time_plot_multi.py | 1.2 KB | ||
| 3 - Plotting multiple variables.mp4 | 58.8 MB | ||
| 3 - Preparing Data for Graphing + Your First Graph! - customization.py | 1.8 KB | ||
| 3 - Preparing Data for Graphing + Your First Graph!.mp4 | 50.9 MB | ||
| 3 - Showing Correlation with Heatmaps - Seaborn Docs.url | 102.4 B | ||
| 3 - Showing Correlation with Heatmaps - heatmap_videocode.py | 1.4 KB | ||
| 3 - Showing Correlation with Heatmaps.mp4 | 62.7 MB | ||
| 4 - Combining the Scatterplot & Histogram - Seaborn Docs.url | 102.4 B | ||
| 4 - Combining the Scatterplot & Histogram - jointplot_example.py | 1.7 KB | ||
| 4 - Combining the Scatterplot & Histogram.mp4 | 51.7 MB | ||
| 4 - Displaying Graphs on the Screen - showing.py | 204.8 B | ||
| 4 - Displaying Graphs on the Screen.mp4 | 37.3 MB | ||
| 4 - Kernel Density Estimations - KDE_plots.py | 1.5 KB | ||
| 4 - Kernel Density Estimations - Seaborn Docs.url | 102.4 B | ||
| 4 - Kernel Density Estimations.mp4 | 59.1 MB | ||
| 4 - Line Plot Customisation - Seaborn Docs.url | 102.4 B | ||
| 4 - Line Plot Customisation - time_plot_customs.py | 1.7 KB | ||
| 4 - Line Plot Customisation.mp4 | 42.9 MB | ||
| 4 - Plotting Categorical data with Boxplots - Seaborn Docs.url | 102.4 B | ||
| 4 - Plotting Categorical data with Boxplots - basic_boxplots.py | 1.8 KB | ||
| 4 - Plotting Categorical data with Boxplots.mp4 | 61.9 MB | ||
| 4 - Residual Plots - Seaborn Docs.url | 102.4 B | ||
| 4 - Residual Plots - residual_plot_example.py | 1.6 KB | ||
| 4 - Residual Plots.mp4 | 42.1 MB | ||
| 5 - Saving Graphs to File - saving.py | 204.8 B | ||
| 5 - Saving Graphs to File.mp4 | 27.1 MB | ||
| 5 - Violin Plots - Seaborn Docs.url | 102.4 B | ||
| 5 - Violin Plots - violinplots.py | 1.7 KB | ||
| 5 - Violin Plots.mp4 | 61.8 MB | ||
| 6 - Funky Ways to Make Graphs!.mp4 | 103.6 MB | ||
| 7 - Seaborn Customisation - Seaborn Aesthetics.url | 102.4 B | ||
| 7 - Seaborn Customisation.mp4 | 61.8 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 88 total files | |||
Data Visualization Made Easy with Seaborn and Python
https://WebToolTip.com
Published 10/2025
Created by James Clare
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 33 Lectures ( 3h 33m) | Size: 1.88 GB
Master Seaborn and Matplotlib to turn raw data into powerful, story-driven visual insights.
What you'll learn
Set up a Python virtual environment and install the libraries needed for Seaborn data visualization.
Learn how Seaborn and Matplotlib work to create, display, and save professional-quality visualizations.
Use real-world datasets to build a range of clear, insightful, and visually appealing charts.
How to plot categorical data with bar plots, box plots & point plots.
Plot univariate and multivariate time series.
How to visualise distributions with uni & bivariate histograms, violin plots and KDE plots.
Show statistical relationships with scatter plots, heat maps, facet grids and joint plots.
How to show linear models, regression plots and residual plots.
Requirements
A basic understanding of Python would be beneficial!
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