COVID-19 is a highly infectious respiratory illness caused by the SARS-CoV-2 virus, which can lead to severe respiratory symptoms, hospitalization, and death.The virus spreads primarily through respiratory droplets when an infected person talks, coughs, or sneezes, and it can also spread by touching a contaminated surface and then touching your face.
People who are older or have underlying health conditions, such as diabetes, heart disease, or compromised immune systems, are at higher risk of severe illness or death from COVID-19.
In this project I use SQL Server to explore global COVID 19 data.
BMI (Body Mass Index) is a measure of body fat based on your weight and height. It is calculated by dividing your weight in kilograms by the square of your height in meters. By checking your BMI regularly, it can help you monitor your body weight and identify potential health risks associated with being underweight, overweight, or obese.
In this project, I calculated my BMI using python
As a lover of football and FIFA 21, I decided to take part in this data-cleaning challenge, though with little time. The downloaded dataset includes a large dataset of player attributes, club information, and match statistics. This dataset contains information on over 18,000 players from various football clubs around the world. The data is available in raw format, and it requires cleaning and exploring to extract meaningful insights.
The FIFA 21 dataset includes several data tables, including player data, club data, and match data. The player data table includes information on player attributes such as age, height, weight, and overall rating. The club data table includes information on the clubs, including their name, league, and country. The match data table includes information on matches played, including the date, teams, and scores.
Chi survey data visaulization breakdown of general information of people and their interest using Power BI.
I use SQL for this data cleaning project because:
Data cleaning with SQL can help to improve data accuracy and consistency by removing duplicate or irrelevant information, standardizing formats, and correcting errors.
It can also enhance data quality by identifying and handling missing or incomplete data, and ensuring that data is properly classified and categorized.
By performing these tasks, data cleaning with SQL can help to increase the reliability and usefulness of data for analysis, reporting, and decision-making purposes.
Checkout various projects and dashboards created with Tableau.