What Data Scientists Need to Know About SQL

If you’re an aspiring data scientist or someone already working the field, you’ve probably heard about structured query language or SQL. People typically refer to it as “sequel” rather than by its full name or acronym. It’s the database language used when querying or managing relational database management systems (RDBMS).

So many programming languages exist today that many data scientists may understandably wonder why they should learn SQL in particular. One thing to keep in mind is that SQL is a time-tested language. SQL came into use during the 1970s and still gets relied upon around the world. Also, it’s not a programming language but a query language.

SQL has a unique markup language to represent structured data. Once people learn it, it’s a good idea for them to become familiar with some of the most useful SQL queries or statements. For example, data scientists can learn the queries for retrieving data and ordering it in desired ways. There are also SQL queries based on mathematics, such as those that count the number of customers in a table or produce the average of a given attribute.

Bear in mind that a statement is any text that a database engine recognizes as a known command. Then, queries are commands that return sets of records for users.

Also, when typing an SQL statement and working with a table, the statements are not case sensitive. However, statements that allow more than one command executed during a single server call require semicolons at the ends. The SELECT, DELETE and UPDATE queries are among the most common, and they all relate to manipulating existing information in a database.

Data scientists who know Excel may not realize it, but they already know some concepts associated with SQL. For example, both SQL and Excel store data in tables that have rows and columns. Moreover, each table has smaller sections called fields.

People can also use SQL to check data quality. Even when well-known companies build customized databases for their proprietary information, those entities commonly have their data science teams use SQL when working with the data. That’s because SQL is so diverse in what it can do.