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87 changes: 87 additions & 0 deletions Data-Visualization/README.md
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# Data Visualization Course Outline

## **Analyze Dozens Of Datasets and Create stunning Visualization**

### 1. What Is Data Visualization and Why Is It Important?

### 2. Why Learn Data Visualization?
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Aren't They Stating the same thing????


### 3. Why do we need to Plot.
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Infact this one also.


### 4. Different types of Plot.


## MATPLOTLIB :
* Intro To Matplotlib
* Our First Matplotlib Plots!
* Do We Need plt.show() ?
* Anatomy of Plots
* Figsize & Plot Dimensions
* Changing Matplotlib Stylesheets
* Line Styles, Colors, Widths, and More!
* Plot Labels & Titles
* Changing X & Y Ticks
* Adding Legends To Plots
* Creating Bar Plots
* Creating Histograms
* Creating Scatter Plots
* Creating Pie Charts
* Working With Subplots
* Putting It All Together

## Pandas Plotting
* A Pandas Plotting Recap
* Changing Pandas Plot Styles
* Adding Labels and Titles to Pandas Plots
* Using rename() When Plotting
* Closer Look at Pandas Bar Plots
* Pandas Histograms
* Box Plots
* Pandas Line Plot
* Pandas Scatter Plots
* Multiple Plots On The Same Axes
* Pandas Automatic Subplots
* Manual Subplots With Pandas

## Seaborn
* Intro to Seaborn
* Why Seaborn?
* The Helpful load_dataset() method
* Seaborn Scatterplots
* Seaborn Lineplots
* The relplot() Method
* Resizing Seaborn Plots: Aspect & Height
* Seaborn Histograms
* Count plot
* Strip And Swarm Plots
* Boxplots
* Boxenplots
* Barplots
* KDE Plots
* Bivariate Distribution Plots
* Rugplots
* The Amazing displot() Method
* HeatMap
* Wrap Up Video

## Plotly
* Introduction To Plotly
* Why Plotly
* scatter plots
* histograms
* cox plots
* Plotly Vs Matplotlib
* Line Plot
* Scatter Plot
* Scattering 3-D
* Box Plots
* Pie Charts
* 3-D Surface Plots
* Wrap Up Video