Case Study Analysis: Exploring India's COVID-19 Journey

The Case Study Analysis focused on exploring India's COVID-19 Journey through multiple modules. It aimed to gain valuable insights into the pandemic. I actively participated in data preprocessing, SQL analysis, and deriving meaningful insights from the data. This project provided an opportunity to apply data pre-processing techniques, conduct data analysis using SQL, and showcase proficiency in these areas. By demonstrating my involvement in each module on my portfolio, I showcase my ability to contribute to understanding and addressing the impact of the pandemic.

agedistribution_2016_estimates: age-wise distribution

datewisepatients: the number of cases date-wise in each state

death_and_recovery: death and recovery status

hospitalbeds: the number of beds available in the state

icmrtestingdata: data related to the number of samples tested

statewisedata: state-wise data of positive and recovered cases

In this project, the initial step involved preprocessing the available datasets. I performed a range of tasks within this module to clean the datasets and ensure they were in a suitable condition for in-depth analysis. Out of the six datasets, four required cleaning, while the remaining two were already in good shape. Initially, my focus was on using Python to make the necessary changes and clean the four datasets. This step was crucial in ensuring accurate results for subsequent stages of exploration. Following the completion of the cleaning process, all six datasets, including the four that were cleaned, were uploaded to the database. This enabled the project to proceed with SQL tasks and further investigation.

In this module, I leveraged the pre-processed data from Module 1 to perform data analysis using SQL. I formulated queries to address specific problem statements and extract relevant information from the dataset. By utilizing SQL, I gained insights into various aspects of the COVID-19 situation in India, such as age-wise distribution, confirmed cases, recoveries, and daily decreases. Furthermore, I analyzed state-wise data on cases, death and recovery status, available hospital beds, testing data, and population distribution. This module allowed me to uncover valuable insights by querying and manipulating the dataset using SQL.

Similar to Module 2, this module focused on data analysis using SQL. I continued to utilize the pre-processed dataset to extract further insights and address additional problem statements. By formulating queries and conducting analysis, I deepened my understanding of India's COVID-19 journey. I explored various metrics, including age-wise distribution, confirmed cases, recoveries, daily decreases, and state-wise data on cases, death and recovery status, available hospital beds, testing data, and population distribution. This module provided me with a comprehensive understanding of the COVID-19 situation in India and enabled me to contribute to informed decision-making and effective strategies to combat the pandemic.