Learning Objectives: Apply everything you've learned in a comprehensive data analysis project—from loading raw data to presenting actionable insights using NumPy, pandas, matplotlib, seaborn, and statistical analysis.
Project Overview
In this capstone, you'll analyze a dataset simulating customer data for an e-commerce company. You'll go through the complete data science workflow:
Explore this interactive dashboard to see the final analysis results. Click on bars, pie segments, or data points to see details:
Step 1: Data Loading and Initial Inspection
Step 2: Data Cleaning
Step 3: Exploratory Data Analysis
3.1 Univariate Analysis
3.2 Bivariate Analysis
Step 4: Statistical Analysis
Step 5: Key Findings and Visualizations
Spending by Membership Tier
Churn Rate by Membership
Customer Satisfaction Distribution
Step 6: Your Turn - Extended Analysis
Project Completion Checklist
Course Summary
Key Takeaways
✅ Complete workflow: Load → Clean → Explore → Analyze → Visualize → Recommend
✅ Data quality first: Always assess and clean data before analysis
✅ Multiple perspectives: Use both statistics and visualizations
✅ Tell a story: Connect findings to actionable insights
✅ Iterate: Analysis is rarely linear—discoveries lead to new questions
✅ Document: Clear documentation makes your work reproducible and shareable
Congratulations!
You've completed the Python for Data Science course! You now have the skills to:
- Manipulate data efficiently with NumPy and pandas
- Create compelling visualizations with matplotlib and seaborn
- Perform exploratory data analysis systematically
- Apply statistical concepts to make data-driven decisions
- Complete end-to-end data analysis projects
Next recommended course: ML Fundamentals to apply your data skills to machine learning!
Ready to build ML models? See you in the Machine Learning course!