- Understand the need for Pandas in Data Science
- Matplotlib basics: Figure, SubPlots, Labels, Plots
- Various types of plots: Bar, Scatter, Box, etc.
- Configuring Matplotlib plots
This project challenges you to understand and analyze data through visualization. One principal truth in the science of data is that breakthroughs are difficult if you don't intuitively "understand" the data. Visualization is important because it helps us to gain insights before making any assumptions or taking on false biases. We will see in the future how this also helps us in feature-selection.
In IPL teams representing Indian cities contend each year. Chris Gayle is the highest run scorer in IPL. Do you know who is the second highest run scorer (without using ‘for’ loop)? This module can help you determine the second highest run scorer by manipulating large data sets to extract business insights.
This project challenges you to manipulate large datasets without using conventional programming techniques to extract business insights.
Note :- include the line plt.switch_backend('agg') in every build.py