The data analysis part of this case study has been done by Google Sheets, and you can see the attached Medium blog and the Google Sheets workbook for better understanding. This case study explores the differences in usage patterns between annual members and casual riders of the Cyclistic bike-share program, based on 12 months of trip data. The structured analysis process involved data preparation, cleaning, and unification, removing duplicates and incomplete entries, and calculating additional columns such as ride length and day of the week. The analysis showed that casual riders tend to take longer trips, particularly on weekends, while annual members ride consistently throughout the week, leading to higher overall rides. These findings provided valuable insights for developing strategies to convert casual riders into members through targeted marketing and better bike availability during peak times.

The data analysis part of this case study has been done by Google Sheets, and you can see the attached Medium blog and the Google Sheets workbook for better understanding. This case study examines smart device usage data to generate insights for Bellabeat, a wellness-focused tech company. The analysis involved cleaning and transforming data from activity and sleep patterns, ensuring its consistency and integrity. Metrics like sleep efficiency and activity-to-sedentary ratios were derived to uncover deeper insights. The analysis identified that many users have high sedentary minutes and poor sleep efficiency, providing Bellabeat with an opportunity to tailor product features and marketing campaigns to these segments. These insights were aligned with Bellabeat's goals to enhance customer engagement and drive growth through data-driven strategies.

The data analysis part of this case study has been done by Google Sheets, and you can see the attached Medium blog and the Google Sheets workbook for better understanding. This case study analyzes the Amazon Top 50 Bestselling Books dataset (2009–2019) to uncover trends and provide actionable business insights. By examining factors such as genre, pricing, user ratings, and author performance, the study highlights differences between Fiction and Nonfiction, identifies top authors, and reveals annual book sales trends. The insights lead to strategies for inventory management, marketing, and pricing optimization, focusing on Nonfiction’s profitability and Fiction’s customer loyalty. Recommendations include leveraging author branding, targeting highly-rated but less-reviewed books, and exploring demographic insights for future growth.

The data analysis part of this case study has been done by Google Sheets, and you can see the attached Medium blog and the Google Sheets workbook for better understanding. This case study examines "The Movies Dataset" to uncover insights about the film industry. Through analysis of genres, revenue, ratings, and popularity, key patterns related to box office success were identified. Findings show that Action and Adventure genres dominate, there is a positive correlation between ratings and revenue, and popularity significantly impacts financial performance. The analysis offers actionable recommendations for strategic planning in movie production and marketing.

The data analysis part of this case study has been done by Google Sheets, and you can see the attached Medium blog and the Google Sheets workbook for better understanding. This case study analyzes historical avocado price and sales data across the U.S. to uncover trends essential for strategic business decision-making. By examining seasonal demand, regional preferences, and price sensitivity, the study identifies key dynamics in the avocado market. The data cleaning, transformation, and visualization led to six insightful charts, highlighting how seasonality and product types affect pricing and sales. The findings suggest opportunities such as optimizing pricing strategies during peak demand, targeting marketing in high-demand regions, and leveraging the premium appeal of organic avocados. The analysis emphasizes the importance of data-driven approaches in understanding market behavior and maximizing profitability.