Data Science Prerequisites - Numpy, Matplotlib, and Pandas in Python - Scatterplot

The video tutorial introduces scatter plots as a key visualization tool, explaining the need for two-dimensional data to create them. It demonstrates how to generate random data and plot it using the PLT.scatter function. The tutorial also covers the application of scatter plots in machine learning for classification and clustering, showing how to generate and label data points. Finally, it explains how to create a scatter plot with colored data points to signify different classes, using the C argument in the plotting function.

Why is it necessary to have two dimensions when creating a scatter plot?

• To specify both X and Y coordinates

• To represent data in a three-dimensional space

• To ensure data points are evenly distributed

• To increase the complexity of the plot

What function is used to create a scatter plot in Python?

• plt.plot

• plt.scatter

• plt.graph

• plt.line

In a scatter plot, what does the first argument of plt.scatter represent?

• The horizontal axis

• The color of the points

• The vertical axis

• The size of the points

How can you center half of the data points at a different location in a scatter plot?

• By using a different random function

• By adding a constant to a subset of the data

• By changing the dimensions of the data

• By using a different plotting library

What does the 'C' argument in plt.scatter function do?

• It specifies the color of the data points

• It changes the size of the data points

• It adjusts the transparency of the plot

• It sets the title of the plot