If you’re familiar with Jupyter Notebook, you know it’s an excellent tool for creating and sharing documents that contain live code, equations, visualizations, and narrative text.
However, the world of data science and coding constantly evolves, and a collaborative and interactive notebook environment is a must-have. Some users also look for alternatives to Jupyter because it lacks data versioning and code completion features.
No more sharing and managing multiple versions of Jupyter Notebooks! Choose an interactive tool that facilitates collaboration.
Let’s explore the 10 best Jupyter alternatives and competitors today. Whether you’re a seasoned data scientist or just starting your coding journey, there’s something here for everyone.
10 Jupyter Notebook Alternatives and Competitors in 2024
Summarize this article for me pleaseWhat Should You Look for in Jupyter Notebook Alternatives?
When searching for Jupyter alternatives, ensure you shortlist those that:
- Offer a great user experience: Look for Jupyter alternatives that simplify navigation and optimize the user experience with an intuitive interface
- Enable consistent collaboration: Consider how well your chosen tool can support collaborative work. Look for real-time updates, adaptation control, and record-sharing
- Support data visualization: Choose Jupyter alternatives that let you visualize your data effectively with interactive plots and dashboards
- Provide great onboarding and support: Shortlist solutions that offer comprehensive documentation and access to a supportive data science community or customer support team
- Allow seamless work: Ensure that the tool you choose is compatible with your regular programming languages and file formats
Summarize this article for me pleaseThe 10 Best Jupyter Notebook Alternatives to Use in 2024
Let’s now explore the best Jupyter alternatives for data science in 2024.
1. ClickUp
ClickUp is an all-in-one tool that offers a versatile and collaborative approach to managing projects and documents.
With its user-friendly interface and a wide range of formatting options, you can easily use ClickUp Docs to create and edit documents for any project, roadmap, knowledge base, or task.
And there’s more. ClickUp Docs also allows you to collaborate with your team in real-time. Imagine working on documents together, sharing ideas, and making changes as needed, all in one place.
With version control, tracking changes and reverting to previous versions are a breeze. No more confusion or chaos!
Plus, you can even add HTML files, create test specifications, and generate code snippets with the help of ClickUp Brain, an all-in-one AI assistant that can also help you create templates and tables to store data.
If you’re worried about data analysis, ClickUp has you covered there too. The ClickUp Data Analysis Findings Template is your go-to for organizing and presenting your findings.
With sections for summarizing key findings, visualizing data, and providing recommendations, you can easily create, edit, and share this template with your team.
That’s not all! ClickUp Notepad offers a more versatile approach to note-taking, brainstorming, and organizing ideas than Jupyter Notebook. You can create and organize notes in a hierarchical structure, making it easy to keep track of different topics and subtopics. And with rich formatting options such as bold, italics, and bullet points, your notes will be informative and visually appealing.
ClickUp Notepad integrates seamlessly with other ClickUp features. Link your notes to tasks, documents, or other ClickUp items, making it easy to reference relevant information. Plus, real-time collaboration means you can brainstorm and share ideas with your team members, no matter where they are.
ClickUp best features
- Enhance your data analysis workflows by integrating with Jupyter Notebook, RStudio, or Python
- Get instant, accurate, and contextual answers from any work within and connected to ClickUp with ClickUp Brain, your AI Assistant
- Create, analyze, and share text, code, SOP documents, data policies, and more with ClickUp Docs
- Use various visualization options such as Gantt charts, calendars, and timelines to visualize data related to tasks, projects, and teams
- Integrate with third-party tools such as Google Sheets, Microsoft Excel, and Tableau to analyze and visualize data efficiently
- Automate data-related tasks such as data collection, analysis, and visualization with features such as custom workflows, custom fields, and automation, saving time and effort
- Ensure data security and privacy with encryption, access controls, and audit logs to protect sensitive data
ClickUp limitations
- ClickUp has many features and can be customized for different roles, so it might take a while for new users to learn everything
ClickUp pricing
- Free Forever
- Unlimited: $7/month per user
- Business: $12/month per user
- Enterprise: Contact sales for pricing
- ClickUp Brain is available on all paid plans for $5 per Workspace member per month
ClickUp ratings and reviews
- G2: 4.7/5 (9,380+ reviews)
- Capterra: 4.7/5 (4,000+ reviews)
2. CoCalc
Collaborative Calculation and Data Science (CoCalc) by Sagemath, Inc., is an online platform for collaborative coding. It best suits students, researchers, and educators for collaborative projects and remote teaching.
It provides an environment where you can create, edit, and run code in various programming languages, including Python, R, Julia, and SageMath.
CoCalc also supports LaTeX document editing and integrates with Jupyter Notebooks.
CoCalc best features
- Work on the same project simultaneously with real-time collaboration features
- Use data visualization tools to create charts, graphs, and plots within your projects
- Communicate with collaborators through built-in chat and video conferencing features
CoCalc limitations
- The free tier has resource limitations, such as memory and CPU quotas, which might restrict complex computations or large datasets
CoCalc pricing
- Hobbyist: $10.86/month
- Academic Researcher Group: $123.98/month
- Business Working Group: $104.16/month
CoCalc ratings and reviews
- G2: No reviews available
- Capterra: No reviews available
3. Nextjournal
Nextjournal is a web-based platform that integrates computational code with narrative text.
The platform supports various programming languages, including Python, R, Julia, and Clojure, as well as external data sources and libraries.
One of Nextjournal’s key features is its ability to create reproducible research articles that include all the code, data, and visualizations needed to reproduce the results. This ensures transparency and allows others to build upon the work.
Additionally, Nextjournal offers collaboration features that allow multiple users to work on the same notebook simultaneously. This makes it easy for teams to collaborate on projects and share their work with others.
Nextjournal best features
- Write code, run it in real time, and visualize the results through text, images, and interactive plots
- Share your work and collaborate with others
- Track changes quickly with automatic versioning
Nextjournal limitations
- Some users may find that it may not cover the needs of product requirement documents
Nextjournal pricing
- Free
- Team: Starts at $299/month
- Enterprise: Starts at $499/month
Nextjournal ratings and reviews
- G2: No reviews available
- Capterra: No reviews available
4. Deepnote
Another alternative to Jupyter Notebook is Deepnote. It’s an interactive data science platform that aims to make collaboration and reproducibility easier for data scientists and analysts like you.
It provides a code editor and notebook interface similar to Jupyter but with additional features such as real-time collaboration, version control, and the ability to easily share projects with others.
Deepnote integrates with popular data science libraries and tools such as pandas, sci-kit-learn, and TensorFlow, and also supports a wide range of programming languages including Python, R, and SQL.
The platform is designed to be user-friendly, focusing on making it easy for you to get started and work with others on data science projects.
Deepnote best features
- Collaborate with teammates in real-time and manage version control with Git integration
- Choose from a range of appealing graph and chart formats
- Integrate SQL to query databases directly within your notebook, streamlining your data analysis process
- Share your work with ease via links or export as PDF/HTML files, and personalize your experience with custom themes and shortcuts for a more efficient workflow
Deepnote limitations
- Limited support for programming languages
- Dependency on Internet connectivity
Deepnote pricing
- Free
- Team: $39/editor per month
- Enterprise: Custom pricing
Deepnote ratings and reviews
- G2: 4.8/5 (70+ reviews)
- Capterra: No reviews available
5. Google Colab
Google Colab, also known as Google Colaboratory, is a cloud-based service that allows you to write and execute Python code in a browser-based environment. It’s among the popular Jupyter alternatives for data analysis, machine learning, and deep learning tasks.
It provides access to Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) for faster computation, which is especially useful for training deep-learning models. Additionally, Google Colab integrates with Google Drive, making importing datasets and exporting results easy. You can also collaborate with others by sharing your notebooks with them.
Google Colab comes with pre-installed libraries such as NumPy, pandas, matplotlib, and TensorFlow, so you can quickly get started with data analysis and machine learning tasks. It also supports Markdown, allowing you to design documents with embedded code, visualizations, and explanatory text.
Sharing your notebooks with other users is straightforward with Google Colab; you can generate a link or export the notebook as a .ipynb file.
Google Colab’s best features
- Get free access to powerful hardware accelerators, speeding up complex computations
- Import datasets and export results, facilitating collaboration
- Experiment with code and visualize results in an interactive interface
- Teach and learn machine learning and data science with an accessible platform
Google Colab limitations
- The free version has limitations on the maximum execution time (12 hours), maximum notebook size (100 MB), and maximum memory (25 GB). If you need more resources, you may have to upgrade to a paid plan or use other platforms
Google Colab pricing
- Free
- Pay As You Go: Starts at $11.79 for 100 compute units
- Colab Pro: $11.79/month
- Colab Pro+: $58.99/month
- Colab Enterprise: Pay for what you use
Google Colab ratings and reviews
- G2: No reviews available
- Capterra: No reviews available
6. Kaggle
Kaggle is a platform to find and publish data sets, explore and build models in a web-based data science notebook environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
The platform provides data cleaning and exploration tools, a web-based coding interface, and a cloud-based execution environment.
Kaggle also offers educational resources, such as tutorials, courses, and webinars, to help you develop your data science and machine learning skills.
Kaggle best features
- Dive into Kaggle’s vast collection of datasets, exploring a wide range of topics
- Use the Kaggle Notebooks interface to create and share analyses and machine learning models, fostering collaboration
- Allow access to application data via APIs and generate text based on a text prompt
- Engage in Kaggle’s forums, asking questions, sharing ideas, and collaborating with other data scientists and machine learning practitioners
Kaggle limitations
- Difficult for beginners as the competitions and datasets are generally geared toward more experienced data scientists
- Some users may find it challenging to stand out or receive recognition for their work, especially in larger competitions where there are many participants
Kaggle pricing
- Free
Kaggle ratings and reviews
- G2: 4.7/5 (30+ reviews)
- Capterra: No reviews available
7. Zeppelin
Apache Zeppelin is a powerful tool that enables you to explore, analyze, and visualize data using various programming languages such as SQL, Python, Scala, and R.
It counts among the popular Jupyter alternatives for data scientists, analysts, and engineers working with large datasets and performing complex analytics tasks.
With built-in visualization tools and integration with Apache Spark, Zeppelin allows you to create interactive notebooks, share your work, and collaborate with others on data-driven projects.
Whether you’re a beginner or an expert in predictive data analytics, Apache Zeppelin can help you turn raw data into valuable insights.
Zeppelin best features
- Create stunning visualizations and perform complex data analysis
- Enable multi-language support for efficient code debugging purposes
- Use robust big data analytics capabilities
- Conduct thorough data exploration with interactive notebooks
- Tailor your analytics experience and scale effortlessly for large datasets and distributed computing
Zeppelin limitations
- Limited support for data sources compared to other data analysis tools
- For beginners, Apache Zeppelin’s interface and usage may have a steep learning curve
Zeppelin pricing
- Free
Zeppelin ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
8. Saturn Cloud
Saturn Cloud is a leading provider of cloud-based data science and machine learning platforms.
With a focus on accessibility and collaboration, Saturn Cloud enables data scientists and analysts to work smoothly across various machine-learning frameworks and tools. Their platform offers managed environments for popular frameworks such as TensorFlow and PyTorch, facilitating rapid model development and deployment.
Additionally, Saturn Cloud’s support for distributed computing allows you to scale your workloads efficiently.
With features like version control, data management, and deployment capabilities, Saturn Cloud empowers data teams to streamline their machine-learning workflows and bring their models to production faster.
Saturn Cloud’s best features
- Easily configure and scale your machine learning frameworks
- Collaborate with Jupyter Notebooks to work interactively and share insights with your team
- Distribute computing and scale your workloads across multiple machines for faster processing
- Track changes and collaborate effectively with version control features
- Access, manipulate, and visualize data within the platform
- Streamline process mapping for your machine-learning models
Saturn Cloud limitations
- There might be constraints on the amount of computing resources, storage, or bandwidth that can be used
- Certain features or integrations may only be available with specific subscription plans
Saturn Cloud pricing
- Hosted: Free
- Hosted Organizations: Custom pricing
- Enterprise: Custom pricing
Saturn Cloud ratings and reviews
- G2: 5/5 (250+ reviews)
- Capterra: Not enough reviews
9. Microsoft Azure Notebooks
Microsoft Azure Notebooks provides a cloud-based environment that allows you to create and run notebooks without installing any software on your local machine.
This open-source platform makes it easier for teams to collaborate on projects, as they can all access the same notebooks from any device with an internet connection.
In addition to providing a collaborative environment, Azure Notebooks makes managing and sharing notebooks easier. For example, you can create projects to organize your notebooks and use version control to track changes over time. You can also share your notebooks with others by generating a link that opens the notebook in their web browser.
Azure Notebooks is integrated with other Azure services, such as Azure Machine Learning, so you can easily deploy machine learning models created in your notebooks to the cloud. This makes developing and deploying machine learning solutions easier.
Microsoft Azure Notebooks’ best features
- Create and share Notebooks to collaborate with colleagues and stakeholders on projects
- Integrate with Azure services, deploy machine learning models, and scale your workloads as needed
- Create interactive data visualizations, analyze your data, and learn from your findings using a variety of programming languages
- Use markdown cells to format your notebooks for professional presentations and ensure data security and regulatory compliance using Azure’s robust features
- Run notebooks locally or in the cloud, track changes, iterate on your work, and explore your data
Microsoft Azure Notebook limitations
- The free tier has resource limitations, including limited CPU and memory resources
- Limited customization options for notebooks, especially custom themes and extensions
Microsoft Azure Notebooks pricing
- Free
Microsoft Azure Notebooks ratings and reviews
- G2: No reviews available
- Capterra: No reviews available
10. IBM Watson Studio
The final tool in our list of Jupyter alternatives is IBM Watson Studio. This is a comprehensive data science and machine learning platform designed to help businesses build, train, and deploy AI models.
It provides tools for data preparation, model development, and deployment and collaboration features to facilitate teamwork among data scientists and business analysts.
Watson Studio supports popular programming languages such as Python and R, integrating with various data sources and cloud environments.
The platform also includes advanced capabilities such as AutoAI, which automates process documentation, and Watson Machine Learning, which enables easy deployment and management of models.
IBM Watson Studio best features
- Use AutoAI to accelerate the process of creating and optimizing machine learning models
- Connect and integrate data from various sources to ensure comprehensive and efficient data analysis
- Simplify the deployment of machine learning models into production environments for real-world use
IBM Watson Studio limitations
- Premium features and services may result in high operating costs
IBM Watson Studio pricing
- On IBM Cloud Pak for Data: Custom pricing
- On IBM Cloud Pak® for Data as a Service: Custom pricing
IBM Watson Studio ratings and reviews
- G2: 4.2/5 (160+ reviews)
- Capterra: Not enough reviews
Summarize this article for me pleasePower Your Data Science Projects with ClickUp
There’s a range of Jupyter alternatives to consider if you only want to create and share documents. But with ClickUp, you can do much more. The platform is not just about documents and data analysis. It’s a versatile tool that offers project management, documentation, collaboration, and automation features, making it a great all-in-one solution.
So, if you’re looking to try something new to manage your data science projects efficiently, give ClickUp a try and see the difference it can make to your workflow.
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