Conclusion

Case Study: Data Analysis in Databricks

Elliot Zhu

Data Scientist

Exploring the Airbnb dataset

• Key Achievements:
  • Connected to the Airbnb dataset using Databricks
  • Conducted data quality assessments to ensure accurate and reliable insights
  • Wrote SQL queries to explore room types, pricing, and availability trends
• Outcome: Prepared a clean, structured dataset for further analysis

Uncovering key patterns and relationships

• Key Achievements:
  • Applied window functions to rank listings and find top performers
  • Engineered features such as:
    • Price-to-review ratio
    • Host density
  • Aggregated metrics to analyze:
    • Total earnings
    • Average prices
    • Service fee trends

• Outcome: Uncovered critical insights to guide pricing strategies and operational improvements.

Bringing insights to life

• Key Achievements:
  • Designed interactive dashboards in Databricks
  • Created visualizations:
    • Trends: Average price, review rate, and availability
    • Correlations: Scatter plots for price vs review rate, availability, and service fees
    • Heatmaps: Listing counts by neighborhood and room type

• Leveraged Databricks AI & BI capabilities:
  • Scalable data processing for large datasets
  • Interactive and real-time visualizations for deeper exploration
• Outcome: Enabled stakeholders to explore insights dynamically and drive data-driven decisions

Thank you

Case Study: Data Analysis in Databricks