Strong understanding of SQL and can write complex and optimize queries to extract and analyze data from relational databases such as PostgreSQL, Bigquery, MySQL.
In this project, I used SQL to clean and format the Bengaluru House Prices Dataset. I removed duplicates, handled missing values, and standardized the data. The cleaned dataset is now ready for further analysis and modeling.
This project involved the creation of a snowflake schema and data modeling to improve the services of a Bus Rapid Transport (BRT) system in Peshawar. The aim was to provide insights into the operations of the BRT system and identify areas for improvement.
In this project, I performed EDA on the European Soccer Database using SQL. I analyzed various aspects of the data such as player attributes, match results, team performance, and league standings. I also explored relationships between different variables to gain insights into the data. Through this project, I aimed to provide a comprehensive understanding of the European soccer landscape through the lens of data analysis..
In this project, I used BigQuery to analyze the NYC Citibikes dataset and find insights. I explored different aspects such as the usage patterns of the bikes, the busiest times and locations, the demographics of the users, and more. Through this analysis, I was able to identify trends and patterns that could help stakeholders make informed decisions about the future of the Citibike program in New York City.
This challange includes the solutions to the 8-Week SQL Challenge, a series of real-world SQL problems designed to help develop SQL skills. Each week focuses on a different challenge that requires data manipulation and analysis. The project demonstrates proficiency in SQL and showcases problem-solving skills using various SQL functions and techniques. The solutions are presented in a clear and organized manner for easy understanding and review.
In this project, I analyzed the Google Trends data using Bigquery. I performed various analyses, including identifying the most searched keywords, trend over time, and geographical distribution. From my analysis it turns out the data on bigquery of google trends is not real. Also the data was based on only USA search.
In this project, I conducted a comparative analysis of the Citibike datasets from New York City (NYC) and San Francisco (SF) using BigQuery. I explored various aspects of the datasets such as trip duration, popular routes, user types, and more to identify similarities and differences in the bike-sharing patterns between the two cities. This analysis can provide insights into the bike-sharing market in urban areas and can be useful for future expansion and improvement of bike-sharing systems.