There are so many IDEs and Code Editors for data science available today. Most of them are free for a student and as well as a professional. Although choosing a code editor is a personal choice but choosing the best code editor for data science can improve your speed and productivity as a Data Scientist. So in the article, I will take you through the best code editor that you should use for data science.
Why do You Need to Use a Code Editor for Data Science?
As a Data Scientist, we explore structured and unstructured data to:
- find patterns and relationships between different features in a dataset
- training machine learning models
- building end to end applications for our trained model
We perform these tasks using a programming language, most likely Python. To write Python code, we use a code editor that is nothing more than an application designed to simplify the process of writing code efficiently by speeding up your typing speed, automatically completing your code, and highlighting the syntax of your code.
As a Data Scientist, we mainly use the Python programming language which is one of the most popular programming languages today for data science. So in the section below, I will introduce you to the code editors that I use to improve my productivity as a Data Scientist so that you can decide which is the best code editor for data science.
Best Code Editor for Data Science
When I first started learning Python for Data Science, PyCharm was the only code editor I used. It’s not just a code editor because it’s an IDE. Then once I switched to Visual Studio Code which is the official code editor developed by Microsoft. I felt that VS Code is better than PyCharm and all other data science IDEs and code editors. So today I am using VS Code to train machine learning models and develop end-to-end applications for my trained models.
So I prefer VS Code for training machine learning models, developing end-to-end applications, basic Python projects, and even for C++. Now let’s come to the other side of data science which is exploring data to gather information. I think when you want to collect information from a dataset, you should prefer a code editor that makes the cell to cell execution of your code. So that you can explore different kinds of visualizations and information in one place in different cells. This gives you a great idea of the features in your dataset and even how to report as a Data Scientist.
So, to collect information from a dataset, you should prefer a code editor that makes the cell to cell execution of your code. You can use Jupyter Notebooks or Google Colaboratory for this purpose. Both have the same features, but I still prefer Google Colab over Jupyter Notebooks due to the user interface of Google Colab.
So I think Visual Studio Code and Google Colaboratory are the best code editors for data science. You can use VS code for training machine learning models and developing end to end applications for your trained model and you can use Google Colab for exploring a dataset. I hope you liked this article on the best code editor for data science. Feel free to ask your valuable questions in the comments section below.