SQL Is Still the Most Underrated Data Science Skill

Every new session is eager to get to machine learning models, and every session is surprised by how much of the actual job is SQL. Most companies don't hand you a clean CSV — they hand you database access and a vague business question, and getting from one to the other is a query problem before it's a modelling problem.

A model trained on the wrong slice of data because of a bad join is worse than no model at all — it's confidently wrong. Window functions, correct joins, and knowing when to aggregate versus filter catch more real-world data errors than any amount of algorithm tuning.

It's also the fastest way to look competent in an interview. A candidate who can write a clean, correct query live reads as more hireable than one who can recite the difference between bagging and boosting from memory.

Every analytics and data track we run — Data Analytics, Data Science, and Data Analyst prep — puts SQL in week one for exactly this reason, not as an afterthought before the 'interesting' modules.