Currently, the demand for the skill of SQL is on the rise. Most of the jobs describe their skill requirements, and while doing that, they mention the knowledge in SQL specifically. As the name suggests, ‘Data Science’ is data-driven. Thus, SQL will be an integral part of any data science job. It is also because of the advantage that it offers among the other alternatives. This article tries to elaborate on why SQL and querying are essential for data science and related roles.
If you want to learn SQL for data science, then you can start your journey here!
Structured Query Language, acronymized to SQL, is a computer programming language aimed and designed to manipulate data warehoused in RDBMSs, i.e., Relational Database Management Systems. Different functions such as insertion, deletion, updating, modification of data can be done using SQL. Since most of the structured data is stored in RDBMSs, working with data science will necessarily involve RDBMS and, hence, SQL.
With the advent of big data, data warehousing using relational database management systems has gained more importance, and it is strictly necessary to use them. Moreover, traditionally along with the programming languages Python and R, SQL is used. For instance, a data scientist can write an SQL query to extract data from a database, on which further analyses can be made using Python or R.
If you want to become a data scientist, then you can start your journey here!
Why SQL for Data Science?
Data Science is simply the analysis and study of data to extract meaningful insights. SQL comes into the picture in two of the most critical steps of a data science cycle — Data Extraction, the pre-processing step, as mentioned in the introduction, and Machine Learning. Most of the database platforms are designed using SQL, as it has become a standard for database systems. Also, it is easy to communicate with databases with complex instructions and manipulate data.
Modern systems such as Hadoop, Spark use SQL to maintain relational database systems and to process structured data. Identification of suitable data sources and pre-processing are the key steps in any data analysis work. Since the data is stored in relational databases, querying to extract the data without copying the entire database is necessary as it saves time and is efficient. Hence, a data scientist needs to have comprehensive knowledge in querying language, SQL.
Importance of SQL
SQL is a comprehensive language with several functions, statements, and operators that pave the way to seamless data extraction. SQL has multiple reasons to assert its importance and relevance in data science. First of all, even though SQL has a wide range of tools available, learning them is not an arduous task, as the commands and queries in SQL are comparable to simple English. For example, consider the SQL query ‘select name, nationality from employee’, which can be comprehended by any person of its function with its simplicity of language. Thus, a data science novice can quickly learn SQL, unlike the other programming languages that require more conceptual understanding.
#data-analysis #data #data-science #sql #data-visualization #sql
