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Originally published on Statology.
When it comes to data science and machine learning, having the right code editor can significantly boost productivity and streamline workflows. Here are some local and cloud-based alternatives to Visual Studio Code that cater to the needs of data science professionals.
Note: Reviews of various IDEs are based on my personal opinions and experiences.
1. Cursor
Cursor has become my favorite integrated development environment (IDE). It offers everything that VSCode does. The entire code editor is designed for developers who want to get things done quickly and accurately using AI. Cursor understands your code source and suggests more relevant outcomes. It’s better than GitHub Copilot and has many features you’ll fall in love with instantly. I’ve used Cursor for data science, machine learning, Python programming, and writing tutorials. It’s my go-to tool for code-related issues.
2. Jupyter Notebook
Whether you’re new to data science or an expert, you should use Jupyter Notebook for your daily tasks. It’s highly recommended by professionals for writing data reports, experimenting with Python code, creating and testing machine learning models, and even deploying the notebook in production. It’s simple and has many features that make data-related tasks easier. Now, Jupyter Notebook comes with an AI assistant to help you generate and auto-complete code.
3. RStudio
If you use the R language for data science projects, then RStudio is the best tool available. You can run R notebooks just like Jupyter notebooks, but better, with amazing features that make data visualization and testing various algorithms fun and easy. RStudio is highly recommended for beginners if they’ve never touched an IDE before. It’s simple and comes with essential tools to make your life easier.
4. Kaggle
The Kaggle platform comes with cloud notebooks that allow you to use datasets, models, and Python packages shared by community members to work on data science projects. It offers free GPU and TPU computations and unlimited CPU usage. You can save your notebook, share it with others, and even participate in competitions to win cash prizes. The main advantage of the Kaggle platform is its free access to Cloud Notebook, making it accessible to anyone with limited resources to start data science.
5. Deepnote
Deepnote is a free cloud notebook equipped with AI tools and multiple data integrations. It’s similar to your local IDE where you can do almost everything: create applications, generate data reports, or experiment with various machine learning models. It’s my second go-to tool for code and data-related tasks. It’s easy to use and comes with amazing features that will make you a super data scientist. I’m a big fan of this platform and would love for you to try it.
6. Google Colab
If you’re looking for a simple IDE for your machine learning and deep learning tasks, you should check out 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’s widely used by data professionals, and every new tool in the data space has a tutorial published on Google Colab. It’s simple, fast, and has enough features to help you create and test data applications.
7. Amazon SageMaker Studio Lab
If you want to enhance your Google Colab experience, you should check out Amazon SageMaker Studio Lab. It offers 8 hours of free CPU and 4 hours of GPU computation per day and provides all the necessary tools offered by JupyterLab. It’s fast and designed for all kinds of machine learning and deep learning tasks. You can use it to create the AI application of your dreams.
Conclusion
Choosing the right IDE is crucial as it will help you learn data science faster and solve various problems that arise during the learning process of data science and machine learning. If you want my suggestion, I recommend starting with Kaggle notebooks. It comes with a predefined environment, meaning you don’t have to set up anything, and it comes with thousands of datasets you can start working on immediately. It’s completely free and comes with community integration. After mastering the programming language, I suggest trying other alternatives that work for you. Currently, Cursor works wonders for me, but in the future, it might change based on my professional requirements.
Abid Ali Awan (@1abidaliawan) is a certified data scientist who loves creating machine learning models. Currently, he focuses on content creation and writes technical blogs on machine learning and data science technologies. Abid holds a master’s degree in technology management and a bachelor’s degree in telecommunications engineering. His vision is to create an AI product using a graphical neural network for students struggling with mental illness.
