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Insight analytics

1. Insight analytics

Welcome to Chapter 2 - Insight Analytics

2. From data to insights

In Chapter 2, we'll move from foundational analysis to advanced techniques, enabling Airbnb to uncover actionable insights and make data-driven decisions. We'll extract meaningful insights, develop derived metrics, and apply advanced SQL functions like window functions to uncover deeper patterns in the dataset. Additionally, we’ll explore feature engineering to enrich the dataset, enabling us to deliver more actionable insights to Airbnb. Let's cover each of these in more detail.

3. Summary statistics

First, let's talk about summary statistics They are used to provide the foundation for understanding dataset patterns. Some key measures include mean, median, and standard deviation that are often used to summarize data and uncover key trends. For example, analysing the average pricing in Brooklyn versus Manhattan might reveal distinct demand trends, helping Airbnb refine its marketing and pricing strategies for each borough. Additionally, understanding the variation in pricing can help identify outliers or seasonal fluctuations, enabling Airbnb to tailor pricing strategies for different regions effectively. By incorporating these insights into their platform, Airbnb can better serve both hosts and guests, ensuring competitive pricing and improved customer satisfaction.

4. Feature engineering

Once we've established a strong foundation using summary statistics, we'll build on this with feature engineering to uncover even deeper patterns. Feature engineering allows us to uncover trends that raw data might not reveal, such as identifying peak booking times or determining which property types are most in demand. Techniques like transforming categorical data or generating time-based features help uncover specific patterns in the dataset. For example, combining property type and location might show that luxury apartments in Manhattan command a premium compared to similar properties in other boroughs. Additionally, incorporating interaction terms like the combination of property type and location can help identify neighborhood-specific preferences or premium offerings. Through this module, you'll learn how to apply these techniques effectively to draw actionable insights from Airbnb data.

5. Derived metrics for insights

Lastly, we will derive new metrics to transform raw data and engineered features into actionable insights. For example, calculating revenue per listing helps Airbnb identify high-performing properties and regions, allowing hosts to maximize earnings and the company to focus on growth areas. In the upcoming exercises, you'll explore how to create and analyze derived metrics.

6. Let's practice!

By the end of this module, you'll have the skills to uncover patterns, create new metrics, and provide actionable recommendations for Airbnb. Now let's connect to our Databricks workspace and start our exploration.