Editorial Summary :

If you are beginning with the data science domain, you must learn SQL . This article will give you an idea about what you can do using SQL and what are the commonly used SQL commands . I have listed all the basic commands and their usage, making you understand their purpose more easily . To get the data from the table of your choice, all you need to do is specify the table name next to the FROM keyword . The WHERE clause filters the records, i.e., rows of the dataset . The name of this dataset is Dummy_Sales_Data_v1 . Data analysis is not just about data extraction, but there are commands, clauses, and functions in SQL which help you with that . You need to specify a condition in the WHERE clause, and only the rows which satisfy this condition will be retrieved . In SQL, this clause is used to sort the retrieved records in ascending or descending order . To arrange the rows in order, all you need to do is write a keyword ORDER BY followed by a column name by which you want to order the result set . In SQL, GROUP BY groups the rows or records with the same value in the column specified in the GROUP BY clause . It is used when you want to group the rows and perform aggregate mathematical operations, e.g., addition, counting, or taking the average . Within GROUP BY, you can use aggregate functions such as SUM() and COUNT() . Many more functions and commands enable you to do entire data analysis tasks using SQL . You can find them in Advanced SQL concepts and other SQL-related articles on my Medium page .

Key Highlights :

  • This article is a look at what you can do using the structured query language, which is a must-know tool for everyone working with datasets .
  • Data analysis is not just about data extraction, but there are commands, clauses, and functions in SQL which help you with that .
  • In SQL, GROUP BY groups the rows or records with the same value in the column specified in the GROUP BY clause .
  • It is used when you want to group the rows and perform aggregate mathematical operations .

The editorial is based on the content sourced from www.analyticsvidhya.com

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