- Overview
- Data
- Technologies
- Tools
- Analysis
- Results
- Conclusion
- Acknowledgements
- Wanna Contribute?
- Authors
This is a case study conducted as part of the Google Data Analytics Professional Certificate capstone course. The goal of the study was to identify growth opportunities for Bellabeat, a wellness technology company focused on smart products for women.
The data used for this analysis comes from the FitBit Fitness Tracker Data (CC0: Public Domain) dataset which is made available through Mobius. The dataset consists of personal fitness tracker data from thirty-three Fitbit users who consented to the submission of their personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring.
The dataset provides information about daily activity, steps, and heart rate, which will be used to explore users’ habits. The data is stored in 18 CSV files, which are included in the "Fitabase Data 4.12.16-5.12.16" folder.
- Python
- Pandas
- SQLite
- R Markdown
- RStudio
- neovim(NVchad.tweaked)
- Git and GitHub
- downcute(theme)
- Linux terminal
The analysis performed on the data involved cleaning, transforming, and combining the data from the 18 CSV files into a single dataset. Data exploration techniques such as data visualization and summary statistics were used to gain insights into users’ habits.Python and SQL queries were used to analyze the data and extract specific information.
Our analysis of the FitBit Fitness Tracker Data revealed several key insights:
- There is no clear distinction in user activity across different days of the week, suggesting that any motivation or gamification features should be consistent throughout the week.
- The average daily step count is around 7670, and higher daily step counts are associated with lower mortality risk from all causes. Our product could motivate users to reach a certain number of steps daily.
- There is a linear relation between steps taken and calories burned, and our step monitor could use user data to fit a model and predict how many steps a user should take to burn a certain amount of calories.
- Sedentary minutes decrease as the number of minutes asleep increases, implying that the product could also motivate users to keep a consistent and sufficient sleeping schedule.
Based on these insights, we believe that our product could be developed to help users achieve their fitness and wellness goals by motivating consistent physical activity and sufficient sleep. By encouraging users to reach a certain number of steps daily, predicting calorie burn based on steps taken, and promoting a healthy sleeping schedule, our product could help reduce the risk of obesity, heart disease, type 2 diabetes, and some cancers, and improve daily well-being. We’re excited to continue developing this tool and see how it can benefit our users.
- Google Data Analytics Professional Certificate capstone course instructors
- FitBit for providing the dataset
- Mobius for making the dataset available
- The creators of Python, Pandas, SQLite, R Markdown, and RStudio for providing the necessary tools for the analysis.
Contributions are always welcome! If you have any suggestions or bug fixes, feel free to submit a pull request.
