The modern world runs on data. Almost every single business, whether it’s an aspiring new startup or a seasoned multinational enterprise, relies on data for their everyday operations.
The way this data is organized and interacted with comes down to a specific programming language: SQL.
In simple terms, what is SQL?
Structured Query Language, known as SQL or simply “sequel”, is essentially a language that communicates through databases. If you want to pull, add, delete or edit information on a database, the easiest way to do it is through SQL.
Imagine databases are like warehouses, data tables are like filing cabinets and the data itself is like individual files. Now imagine a warehouse worker who knows the data like the back of their hand and can instantly pull out any file you might want access to. The only thing is the worker speaks a special language that you need to learn in order to communicate with them.
This language is SQL.
So, it’s easy to learn, right?
Well, it depends. For those with an understanding of programming and knowledge of other programming languages, learning SQL should take a few weeks. For complete beginners, however, it might be a little more difficult.
Still, it’s definitely one of the easiest programming languages to learn and should certainly be one of the first languages to learn if you’re thinking of pursuing a career in data science.
Who created SQL?
SQL was originally developed by Raymond Boyce and Donald Chamberlain at IBM in 1970. The first version was initially designed to manipulate and retrieve data from the company’s seminal database, System R.
Boyce and Chamberlain honed their model, originally titled SQUARE, to become easier to use. A sequel was soon created (hence the name), which was put into commercial use in 1979. By 1986 it was standardized into the syntax that is used today.
What is SQL used for?
It’s used to interact with relational databases. A regional database organizes data into tables, like an excel spreadsheet.
SQL works by understanding and analyzing data of virtually any size, from small datasets to large stacks. It’s a very powerful tool that enables you to perform many functions at high efficiency and speed.
The way in which it interacts with databases is ‘non-procedural.’ This means SQL’s syntax is very simple and the coder only has to specify “what to do”, not “how to do it.” These interactions are essentially commands, which fall into five categories: data definition, data manipulation, data control, transaction control and data query.
Data definition language (DDL)
Used to update or manipulate a database structure. Commands include CREATE, ALTER, DROP and RENAME.
Data manipulation language (DML)
Enables the modification of a database. Commands include INSERT, UPDATE and DELETE.
Data control language (DCL)
Used to set privilege and permission parameters within the database structure. Commands include GRANT and REVOKE.
Transaction control language (TCL)
Used to manage changes made by DML. It enables these changes to be grouped into logical transactions. Commands include COMMIT, ROLLBACK and SAVEPOINT.
Data query language (DQL)
Used to fetch data from the database. It only uses the SELECT command.
Why should I learn it?
First of all, it’s the cornerstone of pretty much every modern business. As data is one of the most valuable commodities in the world, being able to manipulate, define, control and understand that data is crucial. SQL allows you to do all of these things and more.
And the more data-driven your job is, the more important SQL is to succeeding.
It’s also a universal language that is transferable to other disciplines and languages – learning SQL can help you to understand the workings of other languages such as Python and Java. SQL also makes collaboration easy, as it’s open source and has a large supportive community.
Finally, it’s a prized skill within data science. In fact, SQL is the most in-demand skill among all jobs in the field, appearing in 42.7% of all data science job postings on Indeed.
Which data science jobs require SQL?
SQL is a key skill across every area of data science. Jobs that require SQL knowledge include data analyst, business intelligence developer, data engineer, data architect and software engineer.
- One of the best data science programming languages for beginners
- Used by almost every organization in tech
- Easy to use and open-source
- Supported by a huge and active community
- Still an in-demand skill, almost 50 years after its creation
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