I'm finishing my Data Science bootcamp and I've spent 90% of my time on Python and Scikit-Learn. However, all the job interviews I've had so far are heavily focused on complex SQL joins and window functions. Should I pivot my focus? How much SQL do I actually need to know for a "real" job?
3 answers
Here is the hard truth: in the industry, your data doesn't live in a CSV file; it lives in a massive SQL database. You can be the best Python programmer in the world, but if you can't extract the data yourself, you are a bottleneck for the team. You don't need to be a Database Administrator, but you absolutely must master Window Functions (like RANK, LEAD, LAG), Common Table Expressions (CTEs), and complex joins. Most Data Scientists spend more time "wrangling" data in SQL than they do building models in Python. Python is for the last 20% of the work; SQL is for the first 80%.
Do you think the rise of "Natural Language to SQL" tools will eventually make these technical SQL interviews obsolete for Data Scientists?
I would suggest practicing on platforms like LeetCode or Stratascratch. Focus on the "Medium" difficulty SQL questions to be safe for interviews.
Great advice, Megan. The Stratascratch SQL problems are specifically tailored to the kind of data manipulation we do every day in this field.
I don't think so, Craig. Those tools still make mistakes on complex business logic. You need to know enough SQL to "audit" what the AI produces. If you blindly trust a generated query for a multi-million dollar report, you're asking for trouble. Companies hire Data Scientists for their precision, and that includes being certain that your data extraction logic is 100% correct.