Python vs. SQL in Data Science

More and more companies are discovering Data Science and Analytics for themselves and are looking for corresponding experts. But which programming language is the best one to learn for this? Two of the most popular options are SQL and Python. Although both tools are powerful in their own way, they differ in terms of their capabilities, syntax, and applications. In this article, we will explore the differences between SQL and Python in the context of Data Science.

What is SQL?

Structured Query Language (SQL) is a programming language used to manage and manipulate relational databases. It is commonly used to extract, update, and manipulate data in databases. SQL is great for working with large data sets and performing complex queries. It is widely used in the industry and is an important skill for any Data Analyst or Data Scientist. Also, why more and more Data Scientists are using it for statistics and Machine Learning is because big services and dialects of SQL such as BigQuery, Snowflake or Redshift have integrated it.

What is Python?

Python, on the other hand, is a general-purpose programming language that is also widely used in Data Science. It is an interpreted language, which means you can write and execute the code directly without the need for a compiler. Python has become the standard for Data Science because of its easy usability, flexibility, and extensive ecosystem of libraries and tools. Python is used for a variety of Data Science tasks, including Data Manipulation, Visualization, Machine Learning, and Deep Learning. It is a popular choice for Data Scientists due to its simplicity, readability, and wide range of applications.

SQL vs. Python in Data Science Use Cases

While SQL and Python are both powerful tools for Data Science, they differ in several ways and have their own advantages.

Syntax

SQL has a very specific syntax designed for working with databases. It uses keywords that are similar to human languages like SELECT, FROM, and WHERE to manipulate data. Python, on the other hand, has a more general syntax and many libraries that can be used for a variety of tasks.

Use Cases

SQL is mainly used for working with relational databases and some cloud based Data Warehouses. It is used to extract, update, and manipulate data in databases and sometimes for Machine Learning tasks. Python, on the other hand, is used for a variety of Data Science tasks, including Data Manipulation, Visualization, Machine Learning, and Deep Learning.

Speed

SQL is optimized for working with large data sets and can easily process millions of rows of data. Python, on the other hand, is slower than SQL when working with large data sets. Python has the advantage of being able to use libraries like NumPy and Pandas, which can speed up data manipulation.

Flexibility

SQL was designed to work with databases and beside the few Data Warehouses, SQL dialects like BigQuery, Snowflake & Co. are not very flexible outside of that context. Python, on the other hand, is a general-purpose programming language that can be used for a wide range of tasks.

Summary

In terms of learnability, it can be said that both programming languages have about the same barriers to entry. SQL and Python are both powerful tools for Data Science. SQL is optimized for working with databases and is great for performing complex queries on large datasets. Python, on the other hand, is more flexible and can be used for a variety of Data Science tasks, including Data Manipulation, Visualization, Machine Learning, and Deep Learning. However, both programming languages have their raison d'être and should be mastered by Data Scientists, Data Analysts and Data Engineers alike.