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Originally published in Statology.
When it comes to data science and machine learning, having the right code editor can significantly improve productivity and streamline workflows. Below are some on-premises and cloud-based alternatives to Visual Studio Code designed for the needs of data science.
Note: The reviews of various IDEs are based on my personal opinions and experiences.
1. Cursor
Cursor Cursor has become my favorite integrated development environment (IDE). It has everything that VSCode offers. The entire code editor is designed for developers who want to get things done quickly and accurately with the help of ai. Cursor understands source code and suggests more relevant results. It's better than GitHub Copilot and has a lot of features that you'll fall in love with right away. I've used Cursor for data science, machine learning, Python programming, and writing tutorials. It's my go-to tool for code-related problems.
2. Jupyter Notebook
If you are just starting out with data science or are an expert in the field, you should be using Jupyter Notebook for your daily 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 to 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 complete it automatically.
3. RS Study
If you are using the R language for data science projects, then RS Study It's 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 data and try out various algorithms. RStudio is highly recommended for beginners if they've never touched an IDE in their lives. It's simple and comes with essential tools to make your life easier.
4. Kaggle
He Kaggle The platform includes cloud notebooks that allow you to use datasets, models, and Python packages shared by community members to work on data science projects. It includes free GPUs and TPUs and provides unlimited CPU computing usage. You can save your notebook, share it with others, and even enter a competition to win a cash prize. The main advantage of the Kaggle platform is its free access to Cloud Notebook, making it accessible to anyone with limited resources to get started with data science.
5. Deep note
He Deep note 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 and data-related tasks. It is easy to use and comes with amazing features that will turn you into a super data scientist. I am a huge fan of this platform and would love for you to try it out.
6. Google Co.
If you are looking for a simple IDE for your machine learning and deep learning tasks, then you should take a look at Google Co.It includes free but limited access to GPUs and TPUs and provides free ai generation and completion tools for code generation. It is widely used by data professionals and every new tool in the data space has a tutorial posted on Google Colab. It is simple, fast and comes with enough features to get you building and testing data applications.
7. amazon Sagemaker Studio Lab
If you want to improve your experience on Google Colab, you should take a look at amazon Sagemaker Studio LabIt comes with 8 hours of free CPU and 4 hours of GPU computing per day and provides all the necessary tools that JupyterLab offers. It is fast and designed for all kinds of machine learning and deep learning tasks. You can use it to build the ai application of your dreams.
Conclusion
Choosing the right IDE is important as it will help you learn data science faster and help you tackle various problems 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, which means you don’t have to set anything up, and it comes with thousands of datasets that you can start working on right away. It is completely free and comes with community integration. After you get the hang of the programming language, I would like you to consider trying out other alternatives that work for you. Currently, Cursor works like a charm for me, but in the future, it might change based on my job requirements.
Abid Ali Awan (@1abidaliawan) is a certified data scientist who loves building machine learning models. Currently, he focuses 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 create an ai product using a graph neural network for students struggling with mental illness.
