What Role does SQL Play in Data Science – Must have Skill for Data Scientists
You must have heard about the top skills required for Data Science. Do you know where you should start? The easier and most important skill that you can acquire is SQL. Before developing this skill, you must know the role of SQL in data science and why every Data Science expert mark SQL as an important one for data scientists. So, let’s explore how exactly SQL is crucial for data science.
SQL is the standard querying language for all the relational databases. It is also the standard for the current big data platforms that use SQL as their key API for their relational databases. We will walk through some of the key aspects of SQL and its validity in the current scenario that is defined by Data Science. Then, we will proceed to learn the key elements of the SQL required for Data Science.
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Importance of SQL in Data Science
Data Science is the study and analysis of data. In order to analyze the data, we need to extract it from the database. This is where SQL comes into the picture. Relational Database Management is an important part of Data Science. While many modern industries have geared their product management with NoSQL, SQL remains the ideal choice for many CRM, business intelligence tools and in office operations.
Many database platforms are modelled after SQL. This is because it has become a standard for many database systems. As a matter of fact, modern big data systems like Hadoop, Spark make use of SQL for maintaining relational database systems and processing structured data. While Hadoop provides features for batch SQL, Impala and Apache Drill provide interactive query capabilities.
Do you know how important Hadoop is for Data Science?
On the other hand, Apache Spark uses the powerful in-memory SQL system to accelerate the processing of queries.
Furthermore, in order to become a data scientist, knowledge of SQL is a must. Many interview questions of Data Science start with SQL queries. Therefore, SQL is essential for Data Science. Therefore, from the above description, we conclude that:
- A Data Scientist needs SQL in order to handle structured data. This structured data is stored in relational databases. Therefore, in order to query these databases, a data scientist must have a sound knowledge of SQL.
- As a matter of fact, Big Data Platforms like Hadoop provides an extension for querying SQL commands for manipulating data through HiveQL.
- In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool.
- In order to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL.
- SQL is also essential for carrying out data wrangling and preparation. Therefore, when dealing with various Big Data tools, you will make use of SQL.
What SQL Skills are required for Data Science?
The aspiring Data Scientists must have the following necessary SQL skills:
1. Knowledge of Relational Database Model
A Relational Database Model System (RDBMS) is the primary and foremost necessary concept for an aspiring Data Scientist. In order to store structured data, you must know RDBMS in-depth. You can then access, retrieve and manipulate the data through SQL. An RDBMS is a standard for every data platform. Even the advanced big data platforms consist of an RDBMS section for processing structured information.
2. Knowledge of the SQL commands
A Data Scientist must know these following SQL commands –
- Data Query Language
- Data Manipulation Language
- Data Definition Language
- Data Control Language
3. Null Value
Null is used to represent a missing value. A field that contains Null value is blank in a table. However, a Null value is different than a zero value or a field that contains blank spaces.
With the help of special lookup tables, a database search engine can locate values in a row easily. With SQL indexing, we can quickly load the data into the database.
Table joins are the most important concepts of relational databases that a data scientist must know. There are two types of joins – Inner Join and Outer Join. They are then further divided into Inner, Left, Right, Full etc.
6. Primary & Foreign Key
A primary key represents unique values in a database. With the help of a primary key, we are able to distinguish each line and record from the database. A Foreign Key, on the other hand, is used to connect two tables together.
A subquery is the nested query that is embedded in another query. There are four important subqueries in SQL – SELECT, INSERT, UPDATE and DELETE. It will return the information to the primary query.
8. Creating Tables
Data Science makes use of organized relational tables, and therefore, it is necessary to know how to create tables in SQL.
All these tools of SQL are required to become proficient in Data Science. You can learn more about them with our SQL DataFlair Series.
In the end, we conclude that SQL plays an important role in Data Science. As a matter of fact, the modern big data platforms are emulating SQL to process organized data that is generated alongside the unstructured one. We also understood various necessary skills of SQL required for Data Science.
So, you have checked the role of SQL in Data Science. Now it is the time to master SQL skills for Data Science.
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