SQL vs. Python: Which should you learn for data analysis?

William Imoh · Improve this Guide

Start with SQL if you want to access, retrieve, and filter data from databases. Start with Python if your focus is data cleaning, analysis, visualization, or building predictive models.

Most data projects require both. SQL helps you get the data. Python helps you work with it.

In this guide, you’ll learn how SQL and Python differ, when to use each, and how they complement each other in practical workflows. We’ll also break down career paths, key features, and a practical walkthrough of a data analysis project that uses both.

If you’re looking to build your skills, our SQL course is a complete learning path. It takes you from writing your first queries to mastering advanced concepts like joins, subqueries, and performance optimization used in professional data work.

Here’s a quick side-by-side comparison to help you decide:

Feature	SQL	Python
Purpose	Used for querying and managing data stored in relational databases.	Used for a wide range of tasks beyond data querying, such as building web applications, data science, research, and many more.
Learning curve	Easy for beginners to learn.	Also easy for beginners to learn.
Use cases	Data retrieval, database management, data analytics.	Web development, machine learning, data analytics, scientific computing, data manipulation, artificial intelligence.
Libraries and ecosystem	Has different implementations, such as Microsoft SQL Server, PostgreSQL, MySQL, and SQLite.	Has programming libraries such as Numpy, Pandas, and Scikit-learn.
Debugging and execution	You can execute SQL queries separately to debug, but execution occurs without breakpoints.	Debugging is easier with breakpoints, which allow you to pause execution when you encounter bugs.
Career paths	Database administrator, database architect, business intelligence analyst.	Data scientist, data analyst, machine learning engineer, software engineer.

What are SQL and Python?

SQL is a specialized language for handling structured data in relational databases. SQL allows you to extract, modify, and delete records to generate insights that support data-driven decision-making. SQL is beginner-friendly, and you can perform database interactions and queries using understandable commands.

On the other hand, Python is a general-purpose programming language used to build web and desktop applications, perform data analysis, and automate tasks, among other uses. Python has a simple and readable syntax, making it a good language to learn when starting your programming journey.

Key features of SQL

Some key features of SQL include:

• Declarative language: SQL uses a declarative programming approach where you describe what you want to achieve rather than explicitly defining how to achieve it. For example, the SQL statement SELECT * FROM Users means “fetch all the users from a database table.” The database engine then performs its magic behind the scenes to execute the command.

• Easy syntax: SQL is straightforward to learn and understand, even if you have no prior programming knowledge.

• Usage: Almost all Relational Database Management Systems (RDBMS), such as MySQL, PostgreSQL, Oracle, and Microsoft SQL Server, adopt SQL.

• Scalability: You can use SQL to add new tables, edit tables, and delete old tables. This means you can use it to scale up or down to accommodate datasets according to business needs.

• Wide range of commands: SQL supports different types of commands. These commands can be divided into the following:

  • Data Query Language (DQL): The primary focus of DQL is to retrieve data without modifying it. The primary command here is SELECT.

  • Data Definition Language (DDL): You use these commands to define and modify the structure of database objects such as tables, schemas, indexes, and so on. The key commands here are CREATE, ALTER, and DROP.

  • Data Control Language (DCL): You use these commands to control access to data in a database by granting or revoking user permissions. Examples include GRANT, and REVOKE.

  • Data Manipulation Language (DML): This is a subset of SQL you use to insert, update, and delete data from database tables. Examples include INSERT, DELETE, and UPDATE.

  • Transaction Control Language (TCL): You use these commands to manage transactions in a database. They help ensure the data is secure and consistent. Examples include COMMIT and ROLLBACK.

You’ll get to practice using all these command types hands-on in our SQL course. Each lesson focuses on practical query writing and real use cases, which helps you understand when and how to apply what you learn.

Key features of Python

Here are some of the key features of Python:

• Easy to read and understand: Python syntax is clean and readable. Instead of curly braces, it uses indentation for code blocks, making it easier to scan and debug.

• Object-oriented programming language: Python uses object-oriented programming concepts that solve problems using objects. OOP improves code reusability.

• Free and open-source: Being open-source, Python has a massive ecosystem and community. You’ll find beginner-friendly issues, helpful tutorials, and thousands of open libraries.

• Portability: Python code can run on various operating systems, including Windows, Linux, and macOS, as well as on embedded platforms such as Raspberry Pi, STM32, and ESP32.

How SQL and Python work together in data projects

You use SQL for data management, i.e., retrieving, filtering, organizing, and inserting data. On the other hand, you use Python for more general programming applications, such as data manipulation, statistical analysis, machine learning, and automation.

Let’s walk through a practical scenario demonstrating how to use both for data analysis. Suppose you’re assigned a project to analyze the sales data of an e-commerce company and develop a sales forecast. Here’s how you might approach it:

1. Data collection: This involves gathering all relevant data from the client, which may be in different locations and formats. Your goal is to consolidate the data into a unified structure for analysis. If the data is in the client’s database, you can use SQL to extract and migrate it to a destination database. If the data is accessible via an API, you can use Python to fetch it and store it in your database for further processing.

2. Data cleaning: Before analysis, you should make sure the data is accurate, consistent, and reliable. This step involves identifying and correcting errors, handling missing values, and resolving inconsistencies. You can use SQL basic cleaning tasks, such as:

   • Removing rows with missing data
   • Dropping unwanted columns
   • Setting default values
   • Filtering and grouping data based on specific criteria
   • Combining data from multiple tables

   However, Python is more suitable for complex cleaning operations, such as handling nested structures, working with unstructured data, and data transformation. NumPy and Pandas are popular Python libraries that provide powerful tools and functions for advanced data cleaning and manipulation.

3. Data manipulation: Once the data is cleaned, the next step is to organize it in a way that makes it easier to analyze and interpret. Use Python to analyze and manipulate data. Python’s Numpy and Pandas libraries offer robust tools for reshaping, filtering, and aggregating your cleaned data as well as restructuring data, making it ready for visualization and further analysis.

4. Data visualization: For basic visualizations, you can use some data analytics tools based on SQL. However, Python is more commonly used for creating detailed and customizable visualizations. Python libraries like Matplotlib and Seaborn are widely used in the data science community and support 2D and 3D plots.

5. Making predictions: After cleaning and exploring the data, you can use it to build predictive models. This stage is typically handled entirely in Python, as SQL is not designed for machine learning. Python offers extensive libraries, like Scikit-learn and TensorFlow, which provide a wide range of regression, classification, and clustering algorithms. By training and evaluating models using these tools, you can generate accurate sales forecasts based on the data.

Should you learn SQL or Python first?

Start with SQL if your primary goal is to work with databases, and learn Python next to expand what you can do with that data. I began my career by learning SQL first, and it turned out to be the right decision. It helped me understand how data is stored, structured, and queried, which made it much easier to learn Python later. Once I had the data in hand, Python gave me the flexibility to clean, analyze, and build models on top of it. Learning SQL first gave me a strong foundation that I still rely on today.

If you’re just getting started, check out our SQL course. The course offers interactive query writing with instant feedback, which helps solidify your understanding.

However, deciding which language to learn first depends on your goals and interests. But if you want to succeed in the data field, mastering both SQL and Python is important. They complement each other and make you a more effective data professional.

Is SQL a dying language?

There is an ongoing debate about whether SQL is becoming obsolete and if it’s still worth learning. My answer is no. SQL is not dying. In fact, it remains one of the most widely used languages in data analysis and management.

While technologies like NoSQL databases and other document and graph-based databases have gained popularity, they haven’t replaced SQL. Instead, SQL has continued to evolve and adapt to modern data needs, including large-scale data processing. As long as organizations store data in relational databases, SQL will remain a valuable skill in the data professional’s toolkit.

SQL vs. Python: Key differences that trip people up

Whether you’re a beginner or an experienced developer, it’s normal to get confused when switching between SQL and Python. You may wonder when to use each one, where they overlap, and what sets them apart.

To help clear things up, here are some of the key differences between SQL and Python:

SQL	Python
A declarative language, which means you just state what you want. For example, SELECT * FROM Users WHERE age > 18	An imperative language; you specify how to achieve what you want. For example, result = [users for user in users if users.age > 18]
Works on tables (rows and columns).	Uses different data structures such as lists, dictionaries, sets, tuples, dataframes, etc.
Used within tools like MySQL, PostgreSQL, and Microsoft SQL Server.	It is a full-fledged programming language, not embedded in another tool.
Queries are stateless and are self contained.	You can mutate the state of variables and data structures across the code base.
Runs in a database engine.	Runs on a machine or server.
It is strongly typed per column, e.g., VARCHAR, INT, DATE	Can be strongly or dynamically typed.

SQL vs. Python: Career paths

SQL and Python offer you a wide range of career opportunities across various software domains. Mastering both can broaden your career prospects.

SQL is widely used in data science and analytics. Proficiency in SQL can qualify you for the following roles:

• Database architect: As a database architect, you are responsible for designing the most suitable database systems to support applications. Your role also involves developing strategies that make databases reliable and secure. This helps to ensure data integrity. According to the United States Bureau of Labor Statistics, the average salary of a database architect is $135,980.

• Database administrator: As a database administrator, you are responsible for making sure that databases operate efficiently, securely, and reliably. Your duties include managing user access, assigning user permissions to access or modify database objects, and maintaining up-to-date backups to prevent data loss. The average salary of a database administrator is $104,620.

• Business intelligence analyst: With your knowledge of SQL, you can pursue a career as a business intelligence analyst. In this role, you use data to generate insights that help to make strategic business decisions. Your responsibilities include extracting data from databases, analyzing trends, and creating visualizations and dashboards to present findings to stakeholders.

• Software developer: Your SQL skills are valuable in software development. As a software developer, you’ll build applications that rely on data in databases. Knowing how to write SQL queries allows you to efficiently retrieve, manipulate, and manage this data as needed. Many software developers use SQL regularly as part of their daily work. The average salary of a software developer is $133,080.

Python is in high demand in the data field, and it is a core requirement for many roles in data science, data engineering, analytics, and software development. With your Python skills, you can pursue the following career paths:

• Data scientist: As a data scientist, you’ll use Python for data cleaning, exploration, visualization, and predictive modeling. Strong knowledge of mathematics and statistics is also essential for this role. The average salary of a data scientist in the U.S. is $112,590.

• Data analyst: In this role, you’ll use Python and other tools like Power BI or Tableau to analyze data and address business problems. While data analysts focus on solving business problems by interpreting data, data scientists often apply machine learning techniques to predict trends. The average salary of a data analyst is $82,841.

• Machine learning engineer: As a machine learning engineer, you’ll develop algorithms that use input data and leverage statistical models to make predictions. Python and libraries like TensorFlow and Scikit-learn are central to this role. The average salary of a machine learning engineer is $166,574.

• Software engineer: Python is used widely in software engineering, especially for building full-stack applications. Python frameworks like Django and Flask enable you to build robust and scalable backend systems. The average salary of a software engineer is $166,512.

Next steps

As outlined in this guide, SQL and Python are popular programming languages that data professionals use. Using both languages together may provide more benefits, but you do not have to know both languages to succeed in your data science career.

Mastering SQL is your entry ticket into the data field. By starting with SQL, you’ll build a solid foundation that makes learning other languages, such as Python, easier. You should dedicate focused time each day to practicing SQL, and this will improve how you work with data.

Our SQL course gives you everything you need to go from beginner to a confident data professional. It’s fully interactive, beginner-friendly, and built around the types of queries and data tasks you’ll face on the job. You can also explore our SQL roadmap for a step-by-step guide on learning SQL.

Once you have gained confidence with SQL, you can start learning Python. Python enables you to do more complex analysis, automation, and build applications. Check out the Python roadmap to follow a clear path from beginner to expert.