Top 7 Alternatives to VSCode for Data Science

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Originally published on Statology.

When it comes to data science and machine learning, having the right code editor can significantly enhance productivity and streamline workflows. Here are some local and cloud-based alternatives to Visual Studio Code tailored for data science needs.

Note: The reviews of various IDEs are based on my personal views and experience.

1. Cursor

 
Cursor has become my favorite Integrated Development Environment (IDE). It has everything that VSCode offers. The entire code editor is built for developers who want to get things done fast and accurately with the help of AI. The Cursor understands your code source and suggests more relevant results. It is better than GitHub Copilot and has many features you will immediately fall in love with. I have used Cursor for data science, machine learning, Python programming, and writing tutorials. It is my primary tool for code-related problems.

2. Jupyter Notebook

 
If you are getting started with data science or are an expert in the field, you must be using Jupyter Notebook for your everyday tasks. It is highly recommended by professionals for writing data reports, experimenting with Python code, building and testing machine learning models, and even deploying the notebook in production. It is simple and has tons of features, making data tasks easy. Now, Jupyter Notebook comes with an AI assistant, which will help you generate code and auto-complete.

3. RStudio

 
If you are using the R language for data science projects, then RStudio is the best tool out there. You can run R notebooks just like Jupyter Notebooks but better, and it comes with amazing features that make it fun and easy to visualize the data and test various algorithms. RStudio is highly recommended for beginners if they have never touched any IDE in their lives. It is simple and comes with essential tools to make your life easy.

4. Kaggle

 
The Kaggle platform comes with cloud notebooks that let you use datasets, models, and Python packages shared by community members to work on the data science projects. It comes with free GPUs and TPUs computes and provides unlimited use of CPU computing. You can save your notebook, share it with others, and even participate in a competition to win a money prize. The main advantage of the Kaggle platform is its free access to Cloud Notebook, which makes it accessible for anyone with limited resources to get started with data science.

5. Deepnote

 
The Deepnote is a free cloud notebook that comes with AI tools and multiple data integration. It is similar to your local IDE where you can do almost anything: build apps, generate data reports, or experiment with multiple machine learning models. It is my second go-to tool for code-related and data-related tasks. It is easy to use and comes with amazing features that will make you a super data scientist. I am a big fan of this platform, and I would love for you to give it a try.

6. Google Colab

 
If you are looking for a simple IDE for your machine learning and deep learning tasks, then you should take a look at Google Colab. It comes with free but limited access to GPUs and TPUs and provides free AI completion and generation tools for code generation. It is widely used by data professionals, and every new tool in the data space has a tutorial published on Google Colab. It is simple, fast, and comes with enough features for you to build and test data applications.

7. Amazon Sagemaker Studio Lab

 
If you are looking to upgrade your Google Colab experience, then you should have a look at Amazon Sagemaker Studio Lab. It comes with 8 hours of free CPU and 4 hours of GPU computing daily and provides all the necessary tools that JupyterLab provides. It is fast and built for all kinds of machine learning deep learning tasks. You can use it to build the AI application that you dream of.

Conclusion

 
Choosing the right IDE is important as it will help you learn data science faster and help you navigate various issues that arise while learning data science and machine learning. If you want my suggestion, I would suggest you start with Kaggle notebooks. It comes with a pre-built environment, meaning you don’t have to set up anything, and it comes with thousands of datasets you can immediately start working on. It is completely free and comes with community integration. After mastering the programming language, I would like you to consider trying out other alternatives that work for you. Currently, Cursor works amazingly for me, but in the future, it might change based on my work requirements.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.