📌 Topics Covered:

• Introduction to SQL & BigQuery: Understanding Structured Query Language (SQL) for database management and Google BigQuery as a serverless, highly scalable data warehouse.
• Core Querying: Mastering SELECT, FROM, and JOIN to retrieve data, along with WHERE for Filtering and GROUP BY for Summarizing results.
• Data Quality Checks: Techniques to identify NULL values, duplicates, and data type inconsistencies to ensure the reliability of your analysis.
• Common Table Expressions (CTEs): Using the WITH clause to create temporary result sets that make complex queries more readable and modular.
• Window Functions: Performing advanced calculations across a set of table rows that are related to the current row (e.g., RANK(), ROW_NUMBER(), and moving averages).
• Dimensions vs. Metrics: Distinguishing between qualitative attributes (Dimensions like “Product Name” or “Region”) and quantitative measurements (Metrics like “Total Sales” or “Quantity Sold”).

📝 Class Summary: This module focuses on “Data Extraction at Scale,” teaching students how to move from basic data retrieval to writing high-performance, readable SQL code that can process terabytes of information in seconds.

✅ What You Will Learn:

• How to utilize BigQuery to query massive datasets without the need for managing local database infrastructure.
• The methodology for using CTEs to break down “Spaghetti Code” into organized, logical steps.
• How to apply Window Functions to compare a specific row’s value against a partition (e.g., comparing a salesperson’s performance to their regional average).
• Techniques for Data Quality audits to prevent “Garbage In, Garbage Out” in your machine learning models.

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