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Bellabeat Wellness Case Study Data
Data Science and Analytics
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About
This collection offers detailed daily fitness metrics intended for wellness analysis, particularly as an external data source supporting the Bellabeat case study. It enables deep dives into user activity patterns, calorie expenditure, and distance covered. The resource focuses on activity logs derived from consumer fitness tracking, providing essential inputs for predictive modelling and user segmentation. This offering uses a specific subset of four key files from a much larger original data pool.
Columns
- TotalDistance_km: This metric represents the total distance covered by an individual, measured in kilometres. The mean value is 5.49 km.
- Calories: This metric represents the total quantity of calories burned by the individual, with a mean burn rate of 2.3k.
- VeryActiveHours: The duration, in hours, that an individual was engaged in high-intensity active movement, averaging 0.35 hours.
- FairlyActiveHours: The duration, in hours, that an individual was engaged in moderately active movement, averaging 0.23 hours.
- LightlyActiveHours: The duration, in hours, that an individual was engaged in low-intensity activity, averaging 3.21 hours.
- SedentaryHours: The duration, in hours, that an individual was inactive or sedentary, averaging 16.5 hours daily.
Distribution
Data is usually supplied in CSV format. The sample file, Activity_Clean.csv, is 26.39 kB in size and contains 6 columns. Across the measured metrics, 940 valid records are available. There are no noted missing or mismatched records within this sample file structure. The expected update frequency for this type of data is annually.
Usage
This material is ideal for data analytics projects focused on quantifying user behaviour and fitness levels. Suitable applications include performing data visualization to illustrate trends in activity, building analytical models to predict calorie expenditure based on distance, and analysing the relationships between sedentary time and active hours. It is particularly relevant for projects requiring statistical exploration using methods like Tidyverse or ggplot2.
Coverage
Specific geographical coverage, time range, and detailed demographic scope are not detailed within the available documentation. This dataset focuses on individual activity and calorie metrics derived from general fitness tracking users.
License
CC0: Public Domain
Who Can Use It
- Data Analysts: To segment users based on their activity levels and calculate average fitness metrics for population groups.
- Health Researchers: To study relationships between different types of daily activity (e.g., very active time versus sedentary time).
- Data Visualisation Experts: To create compelling charts and dashboards illustrating daily fitness and wellness patterns.
Dataset Name Suggestions
- FitBit Daily Activity and Calorie Metrics
- Bellabeat Wellness Case Study Data
- Consumer Fitness Metrics: Activity and Calories
- Individual Daily Exercise Analytics
Attributes
Original Data Source: Bellabeat Wellness Case Study Data
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