Bonus point it is very easy to learn.
That's a bit of an exaggeration but I will say: most of data science is not the fun stuff. Everyone goes into the field thinking it's all about doing machine learning to uncover astounding insights that will fundamentally transform the business.
In reality about 5% of most data scientists time is spent doing that. Maybe less. The bulk of the work is getting the data and cleaning it up, doing a tiny bit of the sexy stuff, then writing it up into a report or a presentation to give to people who will either not believe it or scoff because they already knew it.
Think about this - there are a bunch of other software developers who know SQL very well. If your advice was true, then every backend developer would be able to immediately land a data science job and do great at it without having to learn a bunch of math, ML-specific stuff and a whole other tech stack.
For many, the math and "ML-specific stuff" ends up being a very small part of the process. For them, data quality and data cleaning take up the overwhelming majority of hours in a given project, and SQL chops will take you much farther in that kind of an environment.
Plus SQL is not going anywhere anytime soon. So worst case scenario, OP will learn a skill that's not likely to be dated in a few more tics of the hype cycle.
I find it hard to imagine successful data scientists who don't know SQL.
OTOH, I find it hard to imagine (even though I've met some) successful data scientists who only know SQL.
I suppose it's necessary but not sufficient.
I maintain one machine learning model that is very core to our business but doing 'machine learning' is a very portion of my job.
> Think about this - there are a bunch of other software developers who know SQL very well. If your advice was true, then every backend developer would be able to immediately land a data science job and do great at it without having to learn a bunch of math, ML-specific stuff and a whole other tech stack.
In some companies Data scientists are very software development oriented but that is not the case of everywhere. Think about this : software developers who know SQL very well usually don't like cleaning data, they don't necessarily have good interpersonal skills required to solve business problems, they are not necessarily interested in solving business problems, and they may tend to think that more software is the solution to all problems.
I fully disagree. Most backend developers don't know SQL beyond their ORM library or CRUD statements. The business intelligence world has utilized SQL to analyze data and make effective business decisions for 40+ years.
ML is 90% hype to check a box for investors, and the actual business problems could be solved by a semi-competent analyst armed with Excel or SQL, not a bunch of overpaid "scientists" who completed a few Andrew Ng courses.
SQL can become super tricky as well (depending on the context), say you want to get the list of users who are active for 'n' consecutive days from a dataset that has daily user activity for an year. It's not very difficult but needs some effort.
However, for a data science beginner, SQL is the best place to start.
I totally agree with that statement. Being a beginner myself in the DS field, I'm living through this right now in my job. And, as a plus, working with SQL everyday is also helping me a lot to have different perspectives in handling the Python/Pandas DataFrame.
I think previously they'd been used to consuming data from exports and CSVs, scraping websites and plugging into APIs directly. Having to navigate (often messy) database schemas wasn't what they imagined they'd be doing!
Learn Python, it is used universally.
Don't learn R.