Strava Fitness Data Analytics Project | Python, SQL & Power BI Case Study

In this video, I present an end-to-end Fitness Data Analytics case study using Strava fitness tracker data. This project demonstrates how raw fitness tracking data can be cleaned, analyzed, and visualized to uncover meaningful insights about user activity patterns, sleep behavior, and heart rate trends. The project covers the complete data analytics workflow including data preprocessing, feature engineering, exploratory data analysis, SQL querying, and dashboard development. Technologies Used: • Python (Pandas, NumPy, Matplotlib, Seaborn) • SQL (MySQL) • Power BI • VS Code Key Insights: • Average Daily Steps ≈ 7,638 • Average Sleep Duration ≈ 6.98 hours • Average Heart Rate ≈ 77 BPM • Peak Activity Hour ≈ 6 PM Project Workflow: 1. Data Cleaning and Preprocessing using Python 2. Feature Engineering (SleepHours, Activity Level Classification) 3. Exploratory Data Analysis and Visualizations 4. SQL Queries for Data Insights 5. Power BI Dashboard for Interactive Analytics This project is ideal for students and aspiring data analysts who want to learn how to build real-world analytics projects for their portfolios. If you found this project helpful, please like the video and subscribe for more data analytics and data science projects. Project link: https://github.com/AchintyaKrishna/strava-fitness-data-analysis #DataAnalytics #PythonProject #SQLProject #PowerBI #DataAnalystPortfolio