Top SQL Queries for Data Scientists
This article surveys nine essential SQL concepts for data scientists—querying and filtering, handling NULLs, type conversion, aggregation, date manipulation, text processing, ranking, window functions, and subqueries/CTEs—with practical examples of each. It then demonstrates how these concepts combine in real business scenarios, specifically finding best-selling products by month and calculating moving averages, to show that SQL is indispensable for data extraction, manipulation, and reporting even though it's often overshadowed by Python in discussions about data science. The piece emphasizes that SQL proficiency alongside Python and R forms the core toolkit for practical data science work.