I keep seeing mixed advice on whether I should focus on or Python first. If the goal is to get hired quickly in a Data Science domain, which one of these skills pays fastest in America: Python, SQL, Excel, AWS? I want to know what recruiters actually prioritize during the initial screening.
3 answers
From a recruiter's standpoint in the US, SQL is often the "make or break" skill during the initial technical screening. You can be a genius at Python libraries like Pandas, but if you cannot pull the data from a warehouse using complex joins, you won't get past the first round. In my previous firm, we hired junior analysts who were SQL experts much faster than those who only knew Python. It provides a faster ROI because you can contribute to the team on day one by handling data extraction tasks while you continue to refine your Machine Learning skills.
While SQL is definitely vital, don't you think Python offers more long-term growth for someone wanting to move into Deep Learning later? Isn't it harder to pick up Python if you only focus on queries?
Focus on SQL first. It's the universal language of data. Most entry-level roles in America will test your query logic before they even look at your Python code or Excel sheets.
Absolutely, Joseph. I’ve seen so many candidates fail because they ignored SQL. It's the most practical "first skill" to monetize if you are looking for a quick entry into the tech world.
Michael, that’s a valid point about long-term growth. However, the question was about which skill pays "fastest." For immediate employment, SQL is less complex to learn to a professional level compared to Python. Once you have a job as a Data Analyst or Junior Scientist, you have the financial stability to master Python and AWS on the company's time, which is a much smarter strategy for a pivot.