Disclaimer: This post may contain affiliate links, meaning we get a small commission if you make a purchase through our links, at no cost to you. For more information, please visit our Disclaimer Page.
Data science is often viewed as a subject that focuses only on complex machine learning algorithms and sophisticated computer programming languages like R and Python to solve problems. While this is true, some datasets prove difficult to manipulate without using SQL. So, how do data scientists use SQL?
Data scientists use SQL to query tables to extract data for analysis and do data wrangling & preparation. SQL is the standard querying language for relational databases, which store structured data. Therefore, a data scientist needs the knowledge of SQL to query these databases for data analysis.
A data scientists’ work is all about data, and they use different tools and applications to manipulate and extract meaningful information from it for problem-solving. With SQL, you can fetch stored data from relational databases and run it to get the information you need. This article SQL and explains how data scientists use it for data analysis.
Table of Contents
What Is SQL?
Structured Query Language (SQL) is a type of programming language that allows you to access and manage structured data stored in relational databases. It’s registered by the American National Standards Institute (ANSI) as the standard language for relational databases.
Using SQL, you can reorganize and query data by executing commands like UPDATE, CREATE, INSERT, DELETE, SELECT, and DROP. These commands help you modify the structure of the database system as well as control the accessibility of data.
SQL helps you retrieve any information from historical data recorded over years as the databases can handle millions or billions of data cells.
For example, if you want to find information on how many clients spent over $1000 in your business in the first year of business, SQL retrieves that information for you instantly.
It works through a three-step stage to process a query. The first stage is where the SQL query is parsed by checking individual words for syntax errors. It is also checked against the system’s catalog to validate the user and their privilege to execute the query.
After checking for syntax and semantics, the query statement is sent to the database server, where it is optimized and executed.
During the optimization process, the best algorithms for the query are selected, and the query is ready for execution. The relational database management system runs the query and executes the query.
What Is the Connection Between Data Science and SQL?
Most data collected worldwide is held in databases, and SQL helps in accessing the databases to extract the data from them. Databases have been part of data since the very beginning, but there’s newness into what we know about databases in this era of Big Data.
It’s no longer about rows and columns but about how data is structured and the ease of manipulating it.
There are different types of databases. The three most common databases are relational, hierarchical, and network. SQL works with relational databases, which operate independently, and any modifications to its data do affect any connected applications.
In relational databases, complex relationships can be defined between tables, and you can access these relations directly. Because of this independence, relational databases have become the most preferred data storage system, and SQL is the most effective language to communicate with the databases.
SQL relates to data science in the following ways.
- It gives you access to data in the relational database management system.
- It helps you interpret and describe data.
- SQL allows you to create and drop tables and databases, thus modifying the schema of your database.
- It allows you to define data and work on it specifically using conditional commands with the WHERE clause.
- You can easily update data into your database and without the risk of losing your existing data.
Benefits of Learning SQL for Data Science
Data science’s primary goal is to solve real-life problems. Imagine creating a model from the big data collected in the health sector to help coordinate the distribution of vaccines worldwide for the mitigation of the Covid-19 pandemic. That would help save lives even in developing countries that cannot afford it.
Data scientists need to equip themselves with knowledge of SQL because many benefits come with it. Some of these benefits include:
SQL has become a requirement for data science jobs like data analysts, database administrators, business intelligence developers, and database developers. Part of the job is to effectively use SQL to communicate with relational databases and work with data.
SQL will help you understand the dataset you are working with so that you can employ the correct procedures in handling it. You’ll be able to investigate the data, visualize it, develop a structure, spot any missing values, and format it.
Through SQL’s features of slicing, filtering, and sorting, you can familiarize yourself with your data set, checking how values are distributed and organized.
SQL makes managing massive data easy. When working with large data, sheets become challenging to work through. SQL communicates with the database and extracts valuable information from the data no matter how huge it is.
SQL uses declarative statements that are easy to master, unlike other languages requiring you to memorize long strings of code. It’s particularly favorable to new data scientists who are learning coding for the first time.
You can use SQL with other scripting languages to ease data analysis, model evaluation, algorithm creation, and building, and model deployment.
For example, if you’re working on data and want to package it in a certain way for your web application, you can easily convert it to the format you wish to, like XML or JSON, for excellent data visualization for your web application.
You can also integrate it with other programming languages like Python to incorporate your code packages.
Unlike Python or Java, which execute commands of how data operations should be performed, SQL specifies what data operations to perform, reducing the complexity of coding. That means it’s more time-saving to use SQL because instead of writing long lines of code as in other programming languages, you short and precise commands.
SQL prepares a data scientist to handle constantly moving big data using NoSQL. It’s only by mastering SQL that you’ll understand and appreciate the flexibility of NoSQL and the limitations that come with it.
What SQL Skills Does a Data Scientist Need?
To use SQL effectively in data science, you must master the following skills.
Know the Relational Database Model System (RDBMS) In-depth
The RDBMS is the first concept you learn as a data scientist because this is where structured data is stored. This structured data is what we retrieve and manipulate using SQL. In-depth knowledge of RDBMS is necessary when using SQL.
SQL query statements
These are the most common query statements on SQL for the manipulation of data. They include:
- Data Control Language (DCL)
- Data Manipulation Language (DML)
- Transaction Control Language (TCL)
- Data Definition Language (DDL)
- Data Query Language (DQL)
Mastering these query statements will make it easy for you to choose which one to execute for which purpose.
Creating Tables
You’ll occasionally be required to create and organize data in tables. Therefore, it’s essential to know how to create them in SQL and how to organize data in them.
Null Value
On the relational database, a null value is a missing value. It’s different from a blank space or zero. Knowing how to interpret and manipulate data with null values is a skill that you must master to get accurate results from your data.
Joins
A data scientist should understand the concept of table joins on relational databases and their different types. The inner and outer joins, which are divided into left, right, inner, full, etc., combine columns to form new tables. Understanding these concepts will help in organizing data for easier manipulation.
SQL Indexing
Indexes label the data for easy identification when searching and retrieving data. They increase efficiency and help locate values quickly in a search. When using SQL, mastering indexing will save you time and help you load data quickly into the database.
Where to Learn SQL for Data Science?
There are numerous SQL courses online that offer you value and training relevant to data science. Some of these courses are free and deliver training at both basic and advanced levels. They include:
Introduction to Databases and SQL Querying – Udemy
Udemy offers this introductory course on Databases and SQL Querying to equip beginners with both fundamental skills on databases and SQL. The course covers writing simple queries like string manipulation, dates, aggregation, etc.
Introduction to Structured Data Query (SQL) – Coursera
The course is offered by the University of Michigan on Coursera as a free course. Learners are trained on how to perform CRUD (Create, Read, Update, and Delete) operations, SQL joins, and Stored Procedures.
The course is comprehensive as it covers the basic SQL language syntax, database design, and many-to-many relationships for different representations.
SQL for Data Science – Coursera
This course offers the best SQL learning experience, as it teaches SQL from scratch. It introduces the concepts of a relational database to you and helps you apply the knowledge of SQL in a data science environment.
Advanced Databases and SQL Querying – Udemy
The course is designed for learners who are already familiar with SQL, database concepts, and Microsoft AQL Server. The prerequisite skills for this course are knowledge of writing T-SQL queries like JOINS, SELECT, etc. By joining this course, you’ll dive into advanced topics like dynamic queries, triggers, pivots, views, etc.
Learn SQL – Codecademy
It’s a short and interactive course on Codecademy which teaches learners the basics of SQL and how to use SQL to communicate with relational databases. The course involves hands-on experience as learners have four projects for practice.
It covers the basic SQL manipulations, queries, commands, and aggregate functions like SUM, COUNT, MIN, MAX, etc. The many quizzes keep the learners in practice as they prepare to handle actual data.
Conclusion
SQL skills are a must-have for data scientists because of their huge relevance in data science. Even though there are alternatives to SQL like Pandas, the simplicity and ease of use associated with SQL make it the most used programming language for using relational databases.
In job interviews, hiring managers consider SQL knowledge as an essential skill for one to qualify for the job.
