A Numerical Real Time Web Tracking and Scrapping Strategy Applied to Analysing COVID-19 Datasets

With the huge amount of data collected from the web, it is hard to manually analyze and extract useful incites from tables, matrices, or rows of data. Therefore, we need a way to represent these data (maps or graphs) to analyze/interpret them. Data visualization makes it easier to identify trends, patterns, and outliers with large datasets. Data Analytics is used to analyze raw data to make conclusions about the information. In this paper, we tackled web scrapping strategies to get COVID-19 data from the web, and then we went through some data analysis and exploration using EDA.