Fitbit Data Analysis for User Engagement by Sukhmandeep SinghFitbit Data Analysis for User Engagement by Sukhmandeep Singh

Fitbit Data Analysis for User Engagement

Sukhmandeep Singh

Data Analyst

Microsoft Power BI

Fitness & Wellness

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Posted Jul 7, 2025

Analyzed Fitbit data to uncover trends in activity, weight tracking, and sleep patterns using Power BI and Power Query.

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Oct 20, 2024 - Oct 21, 2024

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Fitbit Data Analysis for User Engagement

Sukhmandeep Singh

Data Analyst

Microsoft Power BI

Fitness & Wellness

This project analyzed Fitbit data from 32 users over 31 days to uncover trends in activity levels, weight tracking adoption, and sleep patterns. Using Power BI and Power Query, the data was cleaned, transformed, and visualized to extract actionable insights.

Key objectives included:

Identifying users utilizing weight tracking.

Assessing activity levels (steps, distance, calories burned, sleep).

Validating daily minute tracking accuracy.

Creating interactive Power BI dashboards for insights.

Challenges like missing data, inconsistencies, and outliers were addressed, ensuring a reliable analysis. The findings provided valuable insights to enhance user engagement and data accuracy.

Like this project

Posted Jul 7, 2025

Analyzed Fitbit data to uncover trends in activity, weight tracking, and sleep patterns using Power BI and Power Query.

Likes

Views

Timeline

Oct 20, 2024 - Oct 21, 2024

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For companies Manage team projects Share hiring workspace Post jobs Discover contractors Sign contracts Approve invoices Global payments & tax compliance

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Resources

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Explore all Top independents Design Engineering Marketing Music & audio Social media Video & animation Product Business consulting & HR Other

Contact

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Company

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Key objectives included:

Identifying users utilizing weight tracking.

Assessing activity levels (steps, distance, calories burned, sleep).

Validating daily minute tracking accuracy.

Creating interactive Power BI dashboards for insights.

Challenges like missing data, inconsistencies, and outliers were addressed, ensuring a reliable analysis. The findings provided valuable insights to enhance user engagement and data accuracy.