diff --git a/contrib/plotting-visualization/matplotlib-scatter-plot.md b/contrib/plotting-visualization/matplotlib-scatter-plot.md
new file mode 100644
index 0000000..a9fa500
--- /dev/null
+++ b/contrib/plotting-visualization/matplotlib-scatter-plot.md
@@ -0,0 +1,155 @@
+# Scatter() plot in matplotlib
+* A scatter plot is a type of data visualization that uses dots to show values for two variables, with one variable on the x-axis and the other on the y-axis. It's useful for identifying relationships, trends, and correlations, as well as spotting clusters and outliers.
+* The dots on the plot shows how the variables are related. A scatter plot is made with the matplotlib library's `scatter() method`.
+## Syntax
+**Here's how to write code for the scatter() method:**
+```
+matplotlib.pyplot.scatter (x_axis_value, y_axis_value, s = None, c = None, vmin = None, vmax = None, marker = None, cmap = None, alpha = None, linewidths = None, edgecolors = None)
+
+```
+## Prerequisites
+Scatter plots can be created in Python with Matplotlib's pyplot library. To build a Scatter plot, first import matplotlib. It is a standard convention to import Matplotlib's pyplot library as plt.
+```
+import matplotlib.pyplot as plt
+
+```
+## Creating a simple Scatter Plot
+With Pyplot, you can use the `scatter()` function to draw a scatter plot.
+
+The `scatter()` function plots one dot for each observation. It needs two arrays of the same length, one for the values of the x-axis, and one for values on the y-axis:
+```
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
+y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
+
+plt.scatter(x, y)
+plt.show()
+```
+
+When executed, this will show the following Scatter plot:
+
+
+
+## Compare Plots
+
+In a scatter plot, comparing plots involves examining multiple sets of points to identify differences or similarities in patterns, trends, or correlations between the data sets.
+
+```
+import matplotlib.pyplot as plt
+import numpy as np
+
+#day one, the age and speed of 13 cars:
+x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
+y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
+plt.scatter(x, y)
+
+#day two, the age and speed of 15 cars:
+x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
+y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
+plt.scatter(x, y)
+
+plt.show()
+```
+
+When executed, this will show the following Compare Scatter plot:
+
+## Colors in Scatter plot
+You can set your own color for each scatter plot with the `color` or the `c` argument:
+
+```
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
+y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
+plt.scatter(x, y, color = 'hotpink')
+
+x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
+y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
+plt.scatter(x, y, color = '#88c999')
+
+plt.show()
+```
+
+When executed, this will show the following Colors Scatter plot:
+
+## Color Each Dot
+You can even set a specific color for each dot by using an array of colors as value for the `c` argument:
+
+``**Note:** You cannot use the `color` argument for this, only the `c` argument.``
+
+```
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
+y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
+colors = np.array(["red","green","blue","yellow","pink","black","orange","purple","beige","brown","gray","cyan","magenta"])
+
+plt.scatter(x, y, c=colors)
+
+plt.show()
+```
+
+When executed, this will show the following Color Each Dot:
+
+## ColorMap
+The Matplotlib module has a number of available colormaps.
+
+A colormap is like a list of colors, where each color has a value that ranges from 0 to 100.
+
+Here is an example of a colormap:
+
+
+
+
+This colormap is called 'viridis' and as you can see it ranges from 0, which is a purple color, up to 100, which is a yellow color.
+
+## How to Use the ColorMap
+You can specify the colormap with the keyword argument `cmap` with the value of the colormap, in this case `'viridis'` which is one of the built-in colormaps available in Matplotlib.
+
+In addition you have to create an array with values (from 0 to 100), one value for each point in the scatter plot:
+
+```
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
+y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
+colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])
+
+plt.scatter(x, y, c=colors, cmap='viridis')
+
+plt.show()
+```
+
+When executed, this will show the following Scatter ColorMap:
+
+
+
+You can include the colormap in the drawing by including the `plt.colorbar()` statement:
+
+```
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
+y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
+colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])
+
+plt.scatter(x, y, c=colors, cmap='viridis')
+
+plt.colorbar()
+
+plt.show()
+```
+
+When executed, this will show the following Scatter ColorMap using `plt.colorbar()`:
+
+
+
+
+
+