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Case Study: Data Analysis in Databricks
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Course Description
Data Analysis in Databricks
This case study offers a hands-on approach to upskilling data analysis in Databricks, focusing on the use of SQL. Participants will start with the Airbnb dataset, containing data on listings, bookings, and host details. The initial phase of the case study involves exploring this dataset to understand its structure and content, followed by the necessary data-cleaning steps and loading the data into Databricks. This foundational step ensures the analysis is based on accurate and relevant data.
Developing Visualizations and Dashboards
After cleaning the data, the case study shifts to a critical aspect of data analysis—building visualizations and dashboards. You will learn how to use Databricks’ powerful visualization tools to create insightful graphs and dashboards. These visualizations will help you identify trends, patterns, and outliers in the data. By transforming raw data into easily understandable visual formats, you'll gain the ability to convey complex information succinctly, making it accessible to stakeholders at all levels.
Strategic Analysis and Decision Making
The final part of the case study focuses on applying the cleaned data and visual insights to identify top-performing neighborhoods and room types within the Airbnb dataset. You will use SQL queries in Databricks to analyze performance metrics and derive actionable strategies that can help hosts boost their visibility and performance. By the end of this case study, you will be equipped with the skills to use data-driven insights to influence business decisions and improve operational effectiveness.
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Introduction to Case Study
FreeIn this chapter, you’ll set up your Databricks environment and explore the Airbnb Open Dataset. You’ll learn essential SQL skills for querying, filtering, and summarizing data within the Databricks Lakehouse. Through exercises, you’ll practice simplifying complex queries with subqueries and optimizing performance for large datasets using the
OPTIMIZEcommand. This chapter provides the foundational skills needed to analyze and manage data efficiently in Databricks. - 2
Analytics Applications
In this chapter, you will explore advanced analytical techniques using the Airbnb dataset in Databricks SQL Notebooks. Through hands-on exercises, you will calculate descriptive statistics and leverage window functions for complex computations. You’ll also perform feature engineering to create meaningful metrics that drive actionable insights. By the end of this chapter, you’ll be equipped to confidently apply advanced analytics in real-world consulting scenarios for actionable insights, aligning with Data X’s data-driven strategy.
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Data Visualization
Data visualization is a powerful tool for enhancing the understanding of complex datasets and communicating key insights effectively.In this chapter, you will explore the art and science of designing interactive visualizations and building comprehensive dashboards using Databricks.By leveraging various chart types, such as bar charts, heatmaps, and maps, you’ll uncover data distributions, correlations, and geographical trends. Interactive filters will enable dynamic exploration of neighborhoods, review scores, and price ranges.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.datasets
Metadata sheetcollaborators
Elliot ZhuAI Engineer
Elliot is a data architect with extensive experience in applying advanced analytics, machine learning, and multimodal modeling across diverse industries, including healthcare, public sector, and industrial AI solutions. He is an expert in developing and deploying AI‑driven solutions that translate complex data into actionable insights, while designing advanced models to enhance decision‑making and mitigate risk in enterprise strategy and operation.
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