This case study examines Cyclistic bike-share usage patterns, comparing annual members and casual riders. Using 12 months of trip data, I structured the analysis into data preparation, cleaning, and analysis performed using SQL. The data was consolidated, duplicates and incomplete entries were eliminated, and additional columns such as ride length and day of the week were calculated. Key insights show that casual riders tend to take longer trips, particularly on weekends, while annual members consistently ride throughout the week, contributing to higher overall ride counts. Recommendations focus on converting casual riders into members through targeted marketing and optimized bike availability. The data analysis part of this case study has been done by SQL, and you can see the attached Medium blog and the SQL code for better understanding. 

This case study analyzes smart device usage data to deliver actionable insights for Bellabeat, a wellness-focused tech company. Using datasets on activity and sleep patterns, I applied SQL for data cleaning and transformation, ensuring consistency and integrity. Key metrics, such as sleep efficiency and activity-to-sedentary ratios, provided deeper insights. The analysis revealed that many users experience high sedentary minutes and low sleep efficiency, presenting opportunities for Bellabeat to develop tailored product features and targeted marketing campaigns. By aligning insights with Bellabeat's objectives, recommendations aim to enhance customer engagement and drive growth. The data analysis part of this case study has been done by SQL, and you can see the attached Medium blog and the SQL code for better understanding.

This case study explores the Amazon Top 50 Bestselling Books (2009–2019) dataset to uncover trends and actionable business insights. The analysis in SQL examined factors like genre, pricing, user ratings, and author performance, highlighting distinctions between Fiction and Nonfiction, identifying top authors, and tracking yearly sales trends. Recommendations include focusing on Nonfiction's profitability, fostering Fiction's customer loyalty, leveraging author branding, targeting high-rated books with fewer reviews, and utilizing demographic insights for growth. The data analysis part of this case study has been done by SQL, and you can see the attached Medium blog and the SQL code for better understanding. 

This case study leverages "The Movies Dataset" to identify trends and insights about the film industry. Through SQL-based analysis of genres, revenue, ratings, and popularity, patterns driving box office success were uncovered. Key findings show the dominance of Action and Adventure genres, a positive correlation between ratings and revenue, and the influence of popularity on financial outcomes. Recommendations address strategic planning in movie production and marketing. The data analysis part of this case study has been done by SQL, and you can see the attached Medium blog and the SQL code for better understanding. 

This case study investigates historical avocado price and sales data across the United States to derive insights for strategic decision-making. SQL was used to analyze patterns in seasonal demand, regional preferences, and price sensitivity. The findings highlight how seasonality and product type influence pricing and sales, with visualizations illustrating trends. Actionable recommendations include optimizing pricing during peak demand, targeting marketing in high-demand regions, and capitalizing on the premium appeal of organic avocados. This analysis underscores the value of data-driven approaches to market behavior. The data analysis part of this case study has been done by SQL, and you can see the attached Medium blog and the SQL code for better understanding.