SQL or Structured Query language is the standard language used to query and manage relational database systems. In its initial days, it was called as “SEQUEL” but later changed to “SQL” due to trademark issues. As there are a lot of database systems are available based on SQL and they all have different extensions, different structure, function or commands to interact with them but the basic and standard SQL commands like “Select”, “Insert”, “Update”, “Delete”, “Create”, and “Drop” which are supported by almost all the databases systems so that you can accomplish your task easily.
Here in this post, I tried to cover the above-mentioned basics related to SQL which helps you to get a quick start for interacting with the relational database. Some common relational database management systems that use SQL are:
- Microsoft SQL Server
- IBM DB2 Oracle
- Apache Open Office Base
- Sybase ASE
Basic SQL Syntax
Now for Data Analysis, Python and R are widely used but with the help of SQL queries, we can easily perform all pre-processing steps like importing data from other file formats till exporting it in required format within between steps like finding missing values, performing aggregation, merging data using Join and Union, Pivoting, unpivoting, adding constraints, deleting unnecessary columns or data etc. I personally use Microsoft SQL Server Management Studio for my day to day work and here’s how a basic template looks like when you interact with SQL:
SELECT <Columns> FROM <Tables> WHERE <Conditions> GROUP BY <Columns> HAVING <Conditions> ORDER BY <Columns>;
Importance of SQL in Data Science
Now the question comes – Is it required for a Data Analyst or Data Scientist to have a knowledge of SQL? So the answer is YES!!. Today most of the structured data reside in the form of rows and columns in databases. Also, still most of the business giants have their data stored in databases irrespective of which relational database management systems they are using. In a role where most of your time you spend with data in correcting, getting it in shape and finally analyzing it, SQL will surely make your job easy with its nature of being simple and declarative.
Also, as new technologies are coming where the majority of the data is stored in non-traditional databases, Hadoop, weblogs, flat files etc., developers are trying to find or integrate a way which helps you to use SQL like commands to interact with them. Like what we saw in case of Hadoop, where initially it was limited to professionals having knowledge of Java but later the developers started finding the solutions to make this technology available to the majority of the developers for data analysis. Then a lot of studies happened and ultimately it was found that SQL is the query language used widely in industry for data analysis purpose. Thus, Hive was developed at Facebook to help people with SQL knowledge to query the data against Hadoop.
So having knowledge of SQL will surely benefit you in your project. If you want to start learning SQL for Data Science and Data Analysis, you can take courses provided by Microsoft Virtual Academy and Microsoft SQL Server training