WORLD LAYOFFS DATA CLEANING AND  EXPLORATION PROJECT-MySQL (Click to view full SQL script here)

There is a wide gap between raw data and successful data analysis. Data Cleaning bridges this gap, hence the reason why it is an important pre-requisite for a successful data analysis.

Data Cleaning (or Data Cleansing) refers to the process of identifying incomplete, incorrect, inconsistent, inaccurate or irrelevant parts of the data and then replacing, modifying or deleting the dirty / coarse data. Insights are only as good as the data that informs them, as a result, clean data is more likely to inform good insights.

This project is a step-by-step walkthrough of the process used in Cleaning a data about world layoffs in different companies and industries for year 2020 to 2023. Using the table data import wizard, the dataset was imported to MySQL workbench and found to contain 2361 rows of data and 9 field columns.

                                                                         A LOOK AT DATASET

Relevant columns for the analysis were identified and the steps were taken to improve their usability and ensure they are error free.

Click to view full SQL script here

After all the steps the dataset is now ready for further analysis and can be used to derive meaningful insights about the world layoffs. This project demonstrates the importance of data cleaning in ensuring data quality and reliability.

WORLD LAYOFFS DATA EXPLORATION

After cleaning data various data exploration regarding the dataset was made. This includes seeing total layoffs, company wise layoffs, industry wise layoffs, year wise layoffs etc. through various groupings and analysis queries.

Then  the rank was given to the companies with maximum layoffs in each year using CTEs.

Click to view full SQL Data exploration Script here