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SQL vs Python: Which Should You Learn?

In this article, we will cover the main features of Python and SQL, their main similarities and differences, and which one you should choose first to start your data science journey.
Dec 2022  · 12 min read

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If you are considering breaking into data science, learning to code is mandatory. Coding is one of the main activities of data professionals. Whether you have to collect, clean, analyze, or visualize data, pretty much everything is done through programming. Hence, you need to start learning to code at the early stage of your data science journey.

So, you’re ready to get started with coding. But what programming language should you go for? This is a very classic question among data science newcomers. There are many programming languages for data science out there, but learning all of them at the same time can be almost impossible and discouraging. It’s better to pick one and, once you master it, progress to another one depending on your needs or interests. 

A very common debate is about what programming language is best to get started. In this regard, Python and SQL are particularly well-suited candidates to begin your coding adventure. Python and SQL are extremely popular programming languages in data science, and you won’t get very far in your career unless you’re fluent in both of them. 

In the following sections, we will explain what Python and SQL are, the main differences between them, and which is the most preferable to learn first. Keep reading! 

Why Choose Python?

Ranked first in several programming languages popularity indices, such as the TIOBE Index and the PYPL Index, Python is today the one-size-fits-all programming language.  

Python is an open-source, general-purpose programming language with broad applicability in many software development domains. Due to its simple and readable syntax (close to the English language), Python is often referred to as one of the easiest programming languages to learn and use for beginner programmers. If you want to have a taste of how coding with Python looks, check out our Introduction to Python Course.

Although it was not conceived for data science when it was developed in the early 1990s’, over the years Python has evolved and today it is extensively used in data science, machine learning, and data engineering. This is mainly thanks to its rich ecosystem of packages. With thousands of powerful libraries backed by its huge community of users, Python can perform all kinds of data-related tasks. 

Below you can find a non-exhaustive list of Python use cases in data science. If you’re curious about other Python applications, check out this guide to Python uses.

  • Data analysis. Python is the most powerful way to analyze data. With world-class libraries like pandas and NumPy, everything is possible with a few lines of code, from data collection and data cleaning to exploratory data analysis and statistical analysis
  • Data visualization. A great way to discover hidden patterns in your datasets and present your results is by visualizing your data with compelling plots and charts. Numerous packages can do the magic, such as matplotlib, seaborn, and plotly.  
  • Machine learning. A subfield of Artificial Intelligence, machine learning uses algorithms to enable machines to learn patterns and trends from historical data to make predictions. A popular and intuitive package to implement powerful machine learning models is  scikit-learn.
  • Deep learning. Deep learning is part of a broader family of machine learning methods concerned with the implementation of artificial neural networks. These powerful algorithms are behind some of the most innovative breakthroughs in data science of the last few years. With powerful libraries and frameworks like Keras and TensorFlow, Python is the go-to language for deep learning.

Why Choose SQL?

Much of companies’ data is stored in databases, namely, relational databases. A relational database is a type of database that provides access to data points that are related to one another across different tables with rows and columns. In other words, relational databases are a more scalable refined alternative to traditional spreadsheets. 

SQL relational database.png

Source: MongoDB

Since its development  in the early 1970s by IBM, SQL (Structured Query Language) has been the standard most popular programming language to communicate with, edit, and extract data from databases. Being fluent in database management and SQL is a must if you want to progress in your data science career. You can find out more about what SQL is used for in our full article. 

A great advantage of SQL is that it’s pretty easy to learn compared to other programming languages. This is due to its declarative, simple syntax, which is specifically designed to manage relational databases using SQL queries. A query is a statement comprising various SQL commands that together perform a specific task in a database, such as access, modify, update, and delete data

Knowing SQL will enable you to work with different relational databases, including popular systems like SQLite, MySQL, and PostgreSQL. Despite the tiny differences between these relational databases, the syntax for basic queries is pretty similar, which makes SQL a very versatile language.

Want to learn SQL? Check out our Introduction to SQL Course, or fully immerse yourself with our SQL Fundamentals Skill Track.

Python Career Paths

Python is the most in-demand skill in data science. As a result, Python is required in nearly every job in the industry. 

There are plenty of career paths to pursue once you have mastered Python. Below you can find some of the most popular ones. For a more detailed list, check out this article on the top 7 data science careers. Also, if you’re looking for a role in the data industry, check out DataCamp Jobs, which can help you find roles tailored to your skills.

Data Scientist

Data scientists are in great demand across sectors. Whether it’s developing machine learning models to optimize routes or dealing with genetic data to advance new treatments for rare diseases, Python is the answer to analyzing vast amounts of data. 

Data scientists need to be able to apply mathematics, statistics, and the scientific method; use multiple tools and techniques for cleaning and preparing data; perform predictive analytics and artificial intelligence, and explain how these results can be used to provide data-driven solutions to business problems. In all these tasks, Python is the most common tool data scientists use.

The average salary for a data scientist in the United States, according to Glassdoor, is $121,276.

Data Analyst

Data scientists and data analysts are close relatives. While data scientists focus on machine learning techniques to predict the future and deal with uncertainties, data analysts are specifically trained to deal with business problems, such as developing KPIs, creating solutions for stakeholders, and reducing business costs. Python is the go-to language for data analysts to analyze data, although other tools, including business Intelligence software, like Power BI or Tableau, and SQL, are equally important.

Data analysts are already in huge demand, and it seems that demand will only increase with time. Glassdoor estimates an average salary of $72,337 for these professionals.

Machine Learning Engineer

Machine learning engineers focus on researching, building, and designing artificial intelligence and machine learning applications to automate predictive models and make them scalable. In essence, they develop algorithms that use input data and leverage statistical models to predict an output while continuously updating outputs as new data becomes available. 

While machine learning engineers have a large toolkit to do their job, Python is still an indispensable tool.

The mean annual salary of machine learning engineers is $136,454.

SQL Career Paths

Despite being around for quite some time now, SQL is still an indispensable tool for developers and data professionals worldwide. SQL is everywhere, being the go-to language for data management across industries and top-class companies such as Google, Meta, and Amazon. 

As an extremely popular language, the opportunities are wide and diverse. Below you can find a list of some of the most popular SQL jobs.

Database Architect

A database architect is responsible for designing the most suitable and reliable database for a given application. A database architect develops modeling strategies to ensure that the database is secure, scalable, and performs reliably. This entails knowing all the different kinds of databases –relational, NoSQL database, graph-based, distributed, etc–, and having the expertise to identify what kind of situation needs what type of database.

Glassdoor estimates the average annual salary for a database architect at $111,365.

Software Developer

Software developers create computer software and applications. They are the ones who program software, including new programs and features. 

These applications often require data to work properly. Can you guess where the data is stored? Yes, relational database. That makes SQL one of the most basic skills for developers. 

The average annual salary for a Software Engineer is $101,739.

Database Administrator

Database administrators are responsible for ensuring that a database runs efficiently and securely. They maintain users' information, assign them the proper access rights according to their needs, and monitor usage. Database administrators also do the task of backing stored data on a routine basis. 

The average annual salary for this profession, according to Glassdoor, is $89,806.

Python vs SQL: Which Language Should You Learn First?

Which language should you learn first? While this question is particularly relevant for newcomers in data science, it’s important to note that, in the long run, you will need to become fluent in both Python and SQL if you want to progress in your career.

Having said this, the answer to the question will depend on your goals, priorities, and the previous programming knowledge you may have. 

SQL is certainly an easier language to learn than Python. It has a very basic syntax that has the sole purpose of communicating with relational databases. Since a great amount of data is stored in relational databases, retrieving data using SQL queries is often the first step in any data analysis project. Learning SQL is also a great choice because it will help you interiorize basic concepts of programming in a user-friendly way, paving your way to move to more complex programming languages.

However, as a general-purpose programming language, learning Python will allow you to do much more cool stuff. For example, with Python, you will be able to perform an end-to-end data science project, from data collection and data cleaning to data analysis and data visualization.

Python is much more versatile than SQL, but it takes more time to get fluent. Notwithstanding this, Python is widely regarded as a beginner-friendly language because of its English-like syntax and its focus on readability.

The type of work you’re looking for is also worth considering. For example, if you’re interested in the field of business intelligence, learning SQL is probably a better option, as most analytics tasks are done with BI tools, such as Tableau or PowerBI. By contrast, if you want to pursue a pure data science career, you’d better learn Python first.

SQL vs Python: A Comparison

Below, you can find a table of differences between Python and SQL:

 

Python

SQL

Purpose

Used for data science, web development, game development, and other software domains.

Communicate with and manage relational database

Type of Language

General-purpose programming language

Domain-specific programming language

Open Source?

Yes

Some dialects are proprietary. 

Versions

Python 3

Different dialects, such as MySQL, SQLite, PostgreSQL

Ecosystem

+200,000 available packages

No packages available

Ease of Learning

Python is a beginner-friendly language with English-like syntax. 

SQL is a very easy-to-learn language.

Career paths

  • Data scientist
  • Data analyst
  • Machine learning engineer
  • Database architect 
  • Software developer
  • Database administrator 

Advantages

  • Readability
  • Versatility
  • Huge community of users
  • Extremely easy to learn
  • Similar syntax among different SQL dialects

Disadvantages

  • Weak performance with huge amounts of data
  • Poor memory efficiency
  • Applications restricted to database management
  • Some dialects are costly.

Popularity

1th in TIOBE (November 2022) 

9th in TIOBE (November 2022) 

SQL vs Python: Better Together

We hope you found this article insightful. Python and SQL are both indispensable tools for data professionals, hence, while it’s better to pick one to learn at the beginning of your data science journey, in the long run, you will need to become a master of both of them.

Willing to learn Python and SQL? We have you covered. Check out the following resources and get started today.

Introduction to Python

Beginner
4 hours
4,678,356
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
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Start Course

Introduction to Spark SQL in Python

Beginner
4 hours
12,811
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.

Introduction to SQL

Beginner
2 hours
156,898
Learn how to create and query relational databases using SQL in just two hours.
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