I’m a student looking to break into the industry in 2025. I see some jobs emphasizing SQL for data extraction and others wanting heavy Python for machine learning. If I have limited time, which one should I master first to be "job-ready"? Is it true that many data scientists spend more time in SQL than in actual AI frameworks?
3 answers
If you want to get hired quickly, master SQL first. Most entry-level data roles (like Data Analyst) are 90% SQL. You can’t build a model if you can’t get the data out of the warehouse. Companies value someone who can write complex window functions and CTEs because it shows you understand the business logic. Python is the "sexy" part of the job where you do the modeling, but in a production environment, you’ll spend your mornings writing SQL queries to pull the latest training data. Think of SQL as your foundation and Python as the advanced toolkit you build on top of it
Deborah, I totally agree, but do you think the rise of "Text-to-SQL" AI tools will make deep SQL knowledge less valuable for new data scientists in the next few years?
Learn SQL for the job, Python for the career. SQL gets you through the door, but Python allows you to scale your impact through automation and AI.
Perfectly put, Kelly. I’ve never met a successful data scientist who wasn't proficient in both, but SQL definitely comes first in the daily grind.
Actually, Lawrence, it makes deep SQL knowledge MORE valuable! AI can write basic SELECT statements, but it often messes up complex joins or specific business logic. You need to be the "expert in the room" who can audit the AI-generated code. If you can’t spot a logic error in a 50-line SQL query, you’ll end up feeding bad data into your models. The role is shifting from "writing from scratch" to "expert reviewer," and you can't review what you don't deeply understand.