10 SQL Queries You Should Know as a Data Scientist

SQL Queries You Should Know as a Data Scientist


Many relational database management systems (RDBMS) handle data stored in tabular form using the computer language SQL (i.e., tables).

An essential competency for a data scientist is SQL. You may counter that it falls within the purview of a data engineer, but Full-Stack Data Scientist positions are more common. In addition, you would want to rely on something other than just a Data Engineer to get data from a database as a Data Scientist.

We are all aware of the importance of data in the modern world. The most typical SQL queries are covered in this article.

Describe a SQL query

While writing within SQL language, you may generate statements and queries using various SQL keywords. A statement is made up of a string of characters and SQL keywords that adhere to the language’s formatting and syntax requirements and can manage processes related to your data or alter data. In the SQL language, a statement is a “whole phrase” that may be effectively sent to a database management system.

A query is a statement used to obtain data based on predetermined standards. A collection of data that satisfies the criteria you specified in your query will be the outcome of creating a SQL query. SQL keywords will often be used to specify the conditions in your query.

A query will provide results. By enrolling in one of the free SQL courses offered online, you can learn more about SQL.

Top 10 SQL queries

Data Pivoting using Case When

You’ll probably encounter many problems that call for CASE WHEN statements since it’s such a versatile concept. It helps you to construct intricate conditional statements if you want to assign a certain value or class dependent on other variables.

It also enables you to pivot data, which is less well-known. Use CASE WHEN statements can pivot the data, for instance, when you have a monthly column and want to create a separate column for each month.


The operations of NOT IN and EXCEPT are almost identical. Both of them are used to compare the rows between two queries or tables. You need to be aware of a few minor variations between the two.

EXCEPT, as opposed to NOT IN, eliminates redundant entries and returns distinct rows.

Additionally, EXCEPT predicts the same amount of rows in both searches and tables, while NOT IN merely compares one column from each query or table.

Read Also:  Research Target Audience on Instagram

Self Signs Up

 A SQL self-join is used to connect a table to itself. You could think it serves no use, yet you’d be surprised at how often this happens. In real-world circumstances, data is often saved in a single large table rather than multiple smaller ones. In a number of circumstances, self-joins may be required to handle specific problems.


A subquery is a query contained inside another query and the WHERE clause. Another name for it is an inner and nested query. This is a great method to deal with unique problems that require asking several questions to arrive at a precise answer. Even using AS statements, you should ensure you are comfortable utilizing subqueries as they are both quite useful when querying.

Formatting strings

Using string operations is essential, particularly when dealing with messy data. Companies may assess your understanding of strings formatting or manipulation to make sure you can modify data.

Time and Date Manipulation

Undoubtedly, some SQL queries will make use of date-and-time data. For instance, you could be requested to modify a variable’s format from DD-MM-YYYY to only the month.

The following are a few crucial ones to comprehend:


Window Features

Window functions let you do an aggregate computation over all rows rather than delivering just one row, which is what a GROUP BY statement does. It’s helpful if you need to sort rows, calculate cumulative sums, and more.


Union is number ten! Despite the rarity, it’s great to be fully aware since you never know when it could question you about everything. Combine two tables with the same columns using UNION.

Dense vs. Rank Row Number vs. Rank

Ranking rows and values is a pretty common practice. Here are a few scenarios where companies often use ranking:

Ranking the best customers based on income, profits, etc.

Ranking the top countries by sales and the best-selling products based on the number of units sold.

Determining which videos have the most views based on metrics like minutes watched and unique viewers.


Assigning a “rank” to a record in SQL may be done in several different methods, which we’ll explore with an example. Consider the following search and its outcomes:

  • For each row, beginning at number 1, ROW NUMBER() produces a unique number. If a second criterion is not specified, ROW NUMBER() randomly allocates a number in cases of ties (such as Bob vs. Carrie). 
  • Except for ties, where RANK() will assign the same number to all rows beginning at 1, this function produces a different number for each row starting at 1. A gap will also come after the same rank. 
  • In contrast to RANK(), DENSE RANK() does not leave gaps after the same ranks. Daniel is rated third with DENSE RANK() as compared to fourth with RANK().

Repeating CTEs

Similar to a recursive function in Python, a recurrent CTE is a CTE that refers to itself. When it comes to accessing hierarchical data like organization charts, file systems, a network of relationships between websites, etc., recursive CTEs are very helpful.

A recursive CTE consists of three components:

  • The anchor participant a first search that yields the CTE’s fundamental outcome
  • The member that recurs: a CTE-referencing recursive query 
  • UNION ALL’ed with an anchor member is a circumstance that ends the recursive member


Both simple and hard questions have been handled. With the queries, we can do certain database-level computations and filtering. As a result, we can collect the information we need rather than obtain all the information and then perform filters and computations.

Using SQL to query the needed data is crucial since real-world databases include a great deal more data and several relational tables.

You can also learn more from free SQL courses, which will help you understand its concepts better. You can also earn free online courses with certificates for completing the programs.

Hello 👋
Can we help you?