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Writer's pictureJatin Gagwani

Google Data Analysis Capstone | BellaBeat

Google Data Analyst Capstone: Empowering Wellness with Bellabeat Data


Did you know that even a small increase in daily steps can significantly improve health outcomes? Studies show that taking just 1,000 additional steps per day can lead to weight management, reduced risk of chronic diseases, and improved mood. Bellabeat's data analysis can help users identify their current activity levels and set personalised goals to achieve these health benefits.

Google Data Analyst Capstone: Empowering Wellness with Bellabeat Data



Introduction: Unlocking User Insights to Empower Bellabeat's Marketing Strategies


In today's wellness-focused world, Bellabeat stands out as a leader in providing innovative products to help users track their health and activity levels. To further enhance their marketing strategies and empower users to reach their wellness goals, we've embarked on a data exploration journey.


This project delves into a rich dataset containing various health and activity metrics collected by Bellabeat products. By analyzing this data, we aim to gain valuable insights into user behavior and identify factors that influence their health and activity patterns. These insights will be instrumental in crafting targeted marketing strategies that resonate with Bellabeat's user base.



Our Approach:

We'll navigate you through the key stages of this data exploration process. This includes:

  • Data Preparation: We'll explore the data, identify any cleaning or manipulation steps needed, and ensure it's ready for analysis.

  • Key Findings: Through in-depth analysis, we'll uncover hidden trends and patterns within the data, revealing valuable user insights.

  • Actionable Recommendations: Based on our findings, we'll present actionable recommendations that Bellabeat can leverage to optimize their marketing strategies and empower users to lead healthier, more active lives.


Get Ready to Unlock the Potential:

This exploration promises to unlock valuable user insights that will inform Bellabeat's marketing strategies and ultimately empower users to achieve their wellness goals. Let's delve into the data and discover the hidden gems it holds!




Preprocessing: Preparing the Data for Analysis


Before diving into the analysis, we took steps to ensure our data was clean and ready to use. Here's an overview of the preprocessing stages:


1. Data Loading:

  • We loaded data from multiple CSV files containing various health and activity metrics relevant to Bellabeat users (e.g., steps, sleep duration, minute-level activity intensity).


2. Data Cleaning:

  • We addressed missing values, ensured consistent data types (e.g., all dates in the same format), and potentially split columns to separate combined information (e.g., separating date and time).

  • Missing Value Strategies: We employed various techniques to handle missing data points, such as imputation (filling in missing values with estimations) or removal depending on the specific data and the potential impact on analysis.


3. Data Transformation:

  • We transformed the data to improve its usability for analysis. This included tasks like:

  • Data Type Conversion: Converting data types (e.g., date columns to datetime objects) for consistent calculations and visualizations.

  • Column Splitting: Splitting concatenated columns to extract relevant information (e.g., separating multiple data points separated by delimiters in a single column).


4. Data Merging:

  • We merged datasets containing related information from different sources to create a comprehensive view for analysis. For example, we could merge minute-level intensities and METs data to create a dataset for activity analysis.


5. Documentation:

  • We meticulously documented each preprocessing step and any changes made to the data. This documentation ensures transparency and allows for reproducibility of our analysis.




Key Findings and Outcomes: Unveiling Actionable Insights


Our analysis yielded valuable insights into user health and activity patterns:


1. Activity and Calorie Burn:

  • We discovered a very strong positive correlation (correlation coefficient of 0.94) between steps taken and calories burned. This highlights the importance of physical activity for maintaining a healthy weight and overall fitness.


2. Sleep and Activity Levels:

  • We found a significant link between sleep quality (duration and efficiency) and activity levels. Individuals with better sleep tended to be more active, suggesting a potential benefit of good sleep for maintaining physical well-being.


3. Weekly Activity Patterns:

  • Our analysis revealed distinct weekly activity patterns. Weekends generally showed lower activity levels compared to weekdays. This may indicate lifestyle differences or habits that influence activity levels throughout the week.


4. Sleep Trends and Fluctuations:

  • By analyzing sleep data, we observed trends in sleep duration and bedtime patterns. These fluctuations could be influenced by various factors like lifestyle changes, stress levels, or external disruptions.


5. Identifying Unique Patterns:

  • Outlier detection revealed data points that deviated significantly from the norm. These outliers could represent unique user activity patterns or potential errors in data collection. Further investigation is recommended to understand their causes.




Heat Map: Correlation Matrix Depicting Relationships Between User Minute Steps Data from Bellabeat

Heat Map: Correlation Matrix Depicting Relationships Between User Weight Log Data from Bellabeat




Data Visualization and Dashboards: Bringing Insights to Life


This section showcases how we transformed our data analysis findings into clear and compelling visualizations. These visuals will effectively communicate key insights to the Bellabeat team.



Activity Intensity Distribution: Understanding Activity Levels


  • Unveiling Activity Profiles: Analyze activity intensity data (sedentary, lightly active, fairly active, very active minutes) to understand user activity profiles. This distribution reveals time spent at different activity levels, providing a snapshot of overall movement patterns.

  • Potential for Increased Activity: The analysis might indicate a significant portion of users are sedentary for most of the day. This highlights an opportunity for Bellabeat to promote the importance of increasing physical activity levels.

Pie Chart: Distribution of User Activity Types Tracked by Bellabeat

Combination Chart: Analysis of User Activity Levels Including Sedentary, Lightly Active, Fairly Active, and Very Active Minutes Over Time as Tracked by Bellabeat



Steps vs. Calories Burned: Visualizing the Correlation


  • Steps and Calorie Burn Correlation: Analyze the relationship between daily steps and calories burned. This unveils the correlation between physical activity (steps) and energy expenditure (calories burned). Focuses on the user insight, not the chart type.

  • Physical Activity and Weight Management: The analysis highlights the link between increased physical activity (steps) and higher calorie burn. This reinforces the value of physical activity for weight management. Connects the insight to a specific user benefit.

Line Chart: Relationship Between Total Steps and Calories Burned for Bellabeat Users

Weekly Trends: Steps vs. Calories Burned for Bellabeat Users



Hourly Steps Analysis: Unveiling Activity Patterns


  • Activity Patterns Throughout the Day: Analyze hourly step data to identify peak activity times and variations throughout the day. This unveils insights into user behavior, such as morning commutes, workday dips, and potential evening activity.

  • Tailoring Marketing Messages: Understanding activity patterns allows for targeted marketing messages. Bellabeat can reach users during peak activity times or times with lower activity to encourage movement.



Bar Chart: Average Steps Taken by Bellabeat Users Throughout the Day



Sleep Analysis: Exploring Sleep Patterns


  • Sleep Analysis Dashboard: Uncovering Sleep Habits: Develop a sleep analysis dashboard to explore sleep duration, quality (sleep efficiency), and bedtime patterns over time. This comprehensive view reveals user sleep habits and their potential impact on overall health and activity levels.

  • Sleep and Activity Levels: Analyze correlations between sleep quality and daily activity levels. Understanding this connection can inform strategies to promote healthy sleep habits, potentially leading to increased activity levels.



Line Chart: Analysis of Sleep Trends for Bellabeat Users - Duration, Quality, and Bedtime



Weekly Trends : Monitoring Weekly Patterns


  • Unveiling Weekly Activity Trends: Develop a weekly trends dashboard to visualize patterns in steps, calories burned, and other activities. This allows for identifying recurring weekly patterns in user behavior.

  • Targeted Interventions Based on Weekly Patterns: Understanding weekly activity trends empowers Bellabeat to tailor interventions or promotions based on user behavior patterns observed throughout the week.



Bar Chart: Distribution of Total Steps Taken by Day of the Week for Bellabeat Users

These data visualizations effectively translate complex data into easily digestible insights, empowering the Bellabeat team to make data-driven decisions for their marketing strategies.



Implications and Recommendations: Translating Insights into Action


Our analysis yielded valuable insights that can empower you to take charge of your health and well-being:


1. Optimize Your Sleep:

Understanding your sleep patterns (duration, quality, bedtime habits) can help you optimize your sleep routine for better rest and improved well-being. Consider establishing a consistent sleep schedule, creating a relaxing bedtime environment, and limiting screen time before bed. Bellabeat's sleep trackers can provide valuable insights into your sleep quality and help you identify areas for improvement.


2. Embrace Physical Activity:

The analysis highlighted the importance of regular physical activity for overall health. Aim to incorporate both moderate and vigorous activities into your daily routine to achieve recommended activity levels. Break up long periods of sitting with short walking breaks or consider using a fitness tracker to monitor your daily steps and activity levels.


3. Leverage Daily and Weekly Trends:

By examining daily and weekly trends in activity and calorie expenditure, you can identify patterns and adjust your routines accordingly. For example, understanding peak activity times during the day and week can help you schedule workouts or active breaks more effectively. Bellabeat apps can visualize these trends, allowing you to make data-driven decisions about your activity levels.


4. Set SMART Goals for Success:

Setting specific, measurable, achievable, relevant, and time-bound (SMART) goals based on the insights from this analysis can help you track your progress and stay motivated. Whether it's increasing your daily step count, improving sleep quality, or achieving specific fitness targets, goal setting can lead to positive behavioral changes.


5. Embrace Healthy Lifestyle Habits:

Our analysis underscores the importance of adopting healthy lifestyle habits for long-term well-being. This includes regular exercise, adequate sleep, balanced nutrition, stress management, and hydration. Making small but sustainable lifestyle modifications can lead to significant improvements in overall health over time.


6. Track Your Progress and Stay Motivated:

Utilizing health and activity tracking tools and apps like Bellabeat's offerings can facilitate the monitoring and tracking of key metrics over time. Regularly reviewing and analyzing these metrics can help you gain insights into your habits, identify areas for improvement, and track progress towards your health and fitness goals.



Thank you! This analysis empowers Bellabeat to craft data-driven marketing strategies for a healthier, more active community. We welcome your questions and feedback. Want to learn more? Check out the project code and details on BellaBeat-EDA.


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