Insight analytics

Case Study: Data Analysis in Databricks

Elliot Zhu

Data Scientist

From data to insights

  • Extracting insights with descriptive statistics

    • Creating derived metrics to support strategic decisions

    • Utilizing advanced SQL techniques for deeper analysis

    • Implementing feature engineering to add dataset value

Transform from raw data to insights.

Case Study: Data Analysis in Databricks

Summary statistics

Key techniques:

  • Mean, median, mode

  • Standard deviation, variance

  • Distribution analysis

Applications:

  • Identify trends in Airbnb listings

  • Understand pricing patterns across neighborhoods

Illustrations for statistical distribution.

Case Study: Data Analysis in Databricks

Feature engineering

Key techniques:

  • Transforming categorical data

  • Creating interaction terms

  • Generating time-based features

Benefits:

  • Enhance pricing models with nuanced features

  • Pinpoint neighborhood-level demand patterns

Illustration of the process from descriptive statistics calculation to feature engineering.

Case Study: Data Analysis in Databricks

Derived metrics for insights

Example:

  • Revenue per listing
  • Average length of stay
  • Booking frequency

Use cases:

  • Support pricing strategy development
  • Identify high-demand areas

Illustration of the process from descriptive statistics calculation to insight generation.

Case Study: Data Analysis in Databricks

Let's practice!

Case Study: Data Analysis in Databricks

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