On top of that, it’s safe to say that SQL has also been embraced by newer technologies, such as Hive, a SQL-like query language interface to query and manage large datasets, or Spark SQL, which you can use to execute SQL queries. Knowing the standard SQL is thus a requirement for you to find your way around in the (data science) industry. SQL is the lingua franca of data: it gives you the ability to interact with almost any database or even to build your own locally!Īs if this wasn’t enough yet, keep in mind that there are quite a few SQL implementations that are incompatible between vendors and do not necessarily follow standards. There are several reasons: one of the first reasons would be that companies mostly store data in Relational Database Management Systems (RDBMS) or in Relational Data Stream Management Systems (RDSMS) and you need SQL to access that data. Not bad for a language that was developed in the early 1970s, right?īut why exactly is it that it is so frequently used? And why isn’t it dead even though it has been around for such a long time? You get the picture: SQL is a must-have skill when you’re working towards getting a job in the data science industry. What’s more, in this survey, SQL stands out way above the R (57%) and Python (54%) programming languages.
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This is confirmed by 70% of the respondents of the 2016 O’Reilly Data Science Salary Survey, who indicate that they use SQL in their professional context. SQL is far from dead: it’s one of the most in-demand skills that you find in job descriptions from the data science industry, whether you’re applying for a data analyst, a data engineer, a data scientist or any other roles. You'll briefly get some pointers on how you can tune your query further.Īre you interested in an SQL course? Take DataCamp’s Intro to SQL for Data Science course! Why Should I Learn SQL For Data Science?.
Structured Query Language (SQL) is an indispensable skill in the data science industry and generally speaking, learning this skill is relatively straightforward.