Data from travel in Brazil was analyzed using SQL to explore and prepare user data, flight information, and hotel information.
By utilizing SQL queries such as joins, subqueries, and aggregate functions, crucial information was extracted for interested parties.
This project showcases my ability to handle complex data, perform detailed exploratory analysis, and extract meaningful insights, demonstrating proficiency in SQL and data analysis techniques.
This SQL project focuses on cleaning a Nashville housing dataset, involving tasks such as standardizing date formats, populating missing property addresses, splitting address and owner address fields, identifying and removing duplicates, and dropping unnecessary columns.
The project demonstrates proficiency in data cleaning techniques, SQL queries, and string manipulation, preparing the dataset for in-depth analysis, reporting, model building, and integration with other datasets in real estate-related domains.
In this Walmart sales data analysis project, I explored key aspects of the dataset using SQL queries.
From understanding store distributions to uncovering sales trends and their correlation with external factors like temperature, fuel prices, CPI, and unemployment rates, the project aimed to unveil insights into Walmart's sales performance.
By segmenting data based on store types, seasonal trends, and economic indicators, I gained valuable insights into market dynamics. This project highlights my proficiency in SQL query formulation and data analysis, providing businesses with actionable insights to optimize strategies and enhance competitiveness in the retail sector.
Beginning with the removal of duplicates, standardizing data formats, handling null and blank values, and optimizing the dataset by removing unnecessary columns, the project aimed to enhance data quality for further analysis.
Through exploratory data analysis (EDA), I delved into various aspects of the dataset, such as identifying companies with significant layoffs, analyzing trends over time, exploring layoffs by industry, and assessing the impact of economic factors like funds raised and company stage on layoff occurrences.
By leveraging SQL queries and techniques, this project showcased my proficiency in data cleaning, manipulation, and analysis, providing businesses with actionable insights to understand layoff trends globally and make informed decisions.
In addition to exploring and preparing the data from the Brazil travel dataset using SQL queries, I utilized the insights gained to create three comprehensive dashboards highlighting key metrics and trends for users, flights, and hotels.
These dashboards showcase statistics and trends over time, offering valuable insights into user behavior, flight patterns, and hotel bookings.
By visualizing the data in these dashboards, I provided a comprehensive overview of the travel landscape in Brazil, enabling stakeholders to make informed decisions based on the presented metrics and trends.
This project demonstrates my proficiency and showcases my ability to translate insights into actionable visualizations for effective decision-making.
