In this blog, we will explore the step by step process in Data Analytics projects to archive the business requirements. We will be using python libraries Pandas, Numpy, Matplotlib, Seaborn to clean the data and apply analytics on top of data and visualize the data insights in more detailed way. If we understand these steps we can easily takeup any data analytics projects confidently.
Please refer the below Jupyter Notebook link from my Github repository for detailed explanation for Seaborn Package.
Below is Jupyter Notebook link from my Github repository
If you face any issue while opening the notebook like below
Sorry, something went wrong. Reload? then please click on Reload link. It will be loaded properly.
Reason is when we open any Jupyter notebooks in Github, sometimes above message will be shown due to rich text available in that notebook. Try clicking Reload link multiple times if the issue comes again.
