How to Learn SQL Basics for Data Science in 2024?

A Beginners Guide to Learn and Master SQL Basics for Data Science and Machine Learning.

How to Learn SQL Basics for Data Science in 2024?
 |  BY ProjectPro

Data science and artificial intelligence might be the buzzwords of recent times, but they are of no value without the right data backing them. The process of data collection has increased exponentially over the last few years. The companies are churning out massive volumes of data every day for analysis and deriving business insights. All this data is stored in a database that requires SQL-based queries for retrieval and transformations, making it essential for every data professional to learn SQL for data science and machine learning. 


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Why SQL for Data Science?

earn SQL for data science

According to a survey conducted by Terence Shin in early 2021, SQL will be the second most in-demand skill for Data Scientists in 2021 and beyond. 

Fig. 25 Most In-Demand Data Science Skills in 2021 

This conclusion has been made after surveying more than 15,000 Data Scientist job postings from different platforms such as Indeed, Monster, SimplyHired, etc. Some people may argue that tech giants such as Google, Yahoo, etc., are shifting towards NoSQL databases gradually as SQL-based databases are incapable of handling big-data requirements. Industry experts at ProjectPro say that although both have been developed for the same task, i.e., data storage, they vary significantly in terms of the audience they cater to. NoSQL databases are designed to store unstructured data like graphs, documents, etc., whereas SQL databases deal with structured data in tables. So increasing popularity and adoption of NoSQL will not lead to a decline in the demand for SQL. 

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Also, a fun fact? SQL is the standard programming language for many database systems. Even Big data platforms such as Hadoop and Spark have been modeled based on SQL.  Long story short - You want to work in the Data team? You NEED to master SQL for data science the databases. There is no other way out of it. Almost every day, you will find yourself querying the company database to extract a chunk of data based on some conditions and perform analysis on it using Python. Now that we have emphasized enough how important it is for one to learn SQL let us briefly look at what SQL is before moving on to the roadmap on how to learn SQL basics for data science. 

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What is SQL?

SQL (pronounced as “sequel”) stands for Structured Query Language is a domain-specific programming language that has been designed for handling and managing the data stored in RDBMS or Relational Database Management System. This form of data is structured, mainly stored in the table format with linked entities, and it has been built upon the concepts of relational algebra and tuple calculus. SQL was selected as the standard language of ANSI (American National Standard Institute) and ISO (International Organization for Standardization) for its popularity and large-scale adoption. 

Over the years, various organizations have adopted SQL to include a more advanced set of features and cater to some specific needs of different RDBMS. E.g., Microsoft Access, Oracle, Postgres, etc. All these RDBMS-es have proprietary extensions to their databases, but the basic commands of querying and modifying the database are common. 

SQL Basics for Data Science

Here’s the Roadmap of SQL skills -

Roadmap of SQL Skills

Roadmap of SQL Skills (Infographic template from Slidesgo and Freepik)

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As the first step, you should learn about SQL in general. You can start with the categorization of SQL statements into Data Manipulation Language (DML), Data Definition Language (DDL), Data Control Language (DCL), and Transaction Control Language (TCL). 

You should then familiarize yourself with standard SQL commands like SELECT, CREATE, DROP, DELETE and UPDATE. These commands constitute the DML and DDL statements and will equip you with skills to:

  • fetch the data from tables, 

  • create, delete or modify tables/databases.

  • add and update data in tables. 

You should also familiarize yourself with frequently used clauses such as WHERE, BETWEEN, IS NULL, etc. As a part of basic SQL commands, you can also learn about arithmetic operators and logical clauses supported by SQL.

SQL Query      output

Fig. SQL Query and its output (Mysqltutorial)

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As the next step, you can start learning about GROUP BY clauses. This clause is used in combination with the SELECT clause to group the data based on identical entries in different columns and then, using aggregation functions, perform analysis on it. With aggregation functions such as SUM, COUNT, MAX, etc, we can quickly generate insightful reports from the grouped data. 

Example of GROUP BY clause and aggregation function

Fig. Example of GROUP BY clause and aggregation function (Mysqltutorial)

It will also be helpful to learn about HAVING clauses that will help you filter out the selected groups based on different conditions.

One of the fundamental concepts of SQL is Joins. The JOIN keywords allow you to combine the data from multiple tables together based on a common column(s) between them. The data within an organization is distributed across various tables in the databases. As per the requirements, the data is fetched from these tables and combined with the help of PRIMARY KEY and FOREIGN KEY. SQL gives the option to combine this data using multiple ways such as:

  • CROSS JOIN or FULL OUTER JOIN

  • LEFT JOIN

  • RIGHT JOIN

  • INNER JOIN

Various SQL JOINS

Fig. Various SQL JOINS (dev.to)

Another concept similar to JOINS is UNION. The output of two SELECT statements can be combined using the UNION clause given the number and data type of columns and their order is the same for both SELECT statements. INDEXING is another important concept to speed up data retrieval by creating special lookup tables. There are various kinds of indexes possible in SQL which you can explore.

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A critical concept from Advanced SQL is subqueries. In simple words, a subquery is nothing but a query enclosed within another query which is also known as the main query. A subquery can be included in the main query using various clauses like WHERE, FROM, HAVING, etc. It generates result set by applying multiple conditions to the data. The order of execution of queries is subquery followed by the main query. In other words, the primary query executes the data returned by the subquery. 

Subqueries

Fig. Example of Subqueries (mysqltutorial)

SQL lets you work with  strings, numbers, and date-time data. Various built-in functions are present in the SQL for manipulating values for different data types, and it lets you perform advanced operations and data searches using these functions. You can learn about string functions such as LEN, LTRIM, RTRIM, SUBSTRING, REPLACE, etc. You can also learn about the LIKE operator and WILDCARD characters used for performing string search. Popular date-time functions include CURRENT_TIMESTAMP, DATEDIFF, DATENAME, etc. Mathematical functions include ABS, CEILING, FLOOR, POWER, ROUND, etc.

Usage of SUBSTR function in SQL

Fig. Usage of SUBSTR function in SQL (w3resource)

Apart from this, other advanced functions are important to learn, such as COALESCE, ISNULL, NULLIF, etc. Another very important concept of data analysis is CASE statements. It mimics the if-else block of programming languages. It makes use of special keywords like WHEN & THEN to define the conditions. ELSE keyword is used as a default fallback, in case none of the conditions satisfies, go with ELSE. END keyword is used to mark the end of the if-else block.

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Window functions also operate on a set of rows like GROUP BY. The only difference is that the GROUP BY function generates a single value from multiple rows but Window rows return an aggregate value for every row, i.e., the rows maintain their identities. OVER clause is used to define the data window and is supported by two other clauses: PARTITION BY and ORDER BY. There are multiple types of window operations supported by SQL:

  • Ranking Window Functions: RANK(), DENSE_RANK(), etc.

  • Value Window Functions: FIRST_VALUE(), LAST_VALUE(), LAG(), LEAD(), etc.

  • Aggregate Window Functions: SUM(), MAX(), MIN(), etc.

These functions have become one of the most popularly asked SQL questions in technical data science interviews to test skills and understanding of databases.

Aggregate and Window functions 

Fig. Comparison of Aggregate and Window functions (Medium)

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How to Learn SQL for Data Science?

To learn SQL, we recommend you spend LESS time studying the theory and PRACTICE more. Various online platforms let you create a schema and query it without going through the hassle of installing and setting up a database on your local machine. You can always go to websites like SQL Fiddle, DB Fiddle, Oracle Live SQL, etc. If you are looking for practice projects that leverage SQL for data science from basic to advanced level concepts, check out the data science and machine learning projects on ProjectPro. If you master the SQL as mentioned above, you will be ready to face any technical interview of SQL and nail it. 

 

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