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Jupyter VS jamovi

Compare Jupyter VS jamovi and see what are their differences

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Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

jamovi logo jamovi

jamovi is a free and open statistical platform which is intuitive to use, and can provide the...
  • Jupyter Landing page
    Landing page //
    2023-06-22
  • jamovi Landing page
    Landing page //
    2022-11-03

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

jamovi features and specs

  • User-friendly interface
    jamovi features a clean, intuitive interface that is easy to navigate, making it accessible for users with varying levels of statistical expertise.
  • Free and open-source
    jamovi is completely free and open-source, which allows users to download, use, and modify the software without any cost.
  • Integration with R
    jamovi has built-in support for R, enabling users to run R scripts and use R packages directly within the software, providing additional flexibility and functionality.
  • Regular updates
    The development team frequently releases updates to improve functionality, fix bugs, and add new features, ensuring that the software stays current and reliable.
  • Comprehensive features
    jamovi offers a wide range of statistical analyses and graphical options, catering to both basic and advanced user needs.

Possible disadvantages of jamovi

  • Limited advanced features
    While jamovi covers most basic and intermediate statistical methods, it may lack some of the more advanced statistical techniques found in other specialized software.
  • Performance issues
    Occasionally, users may experience performance issues, such as slow processing times or software crashes, especially with very large datasets.
  • Learning curve for R integration
    Although integration with R is a pro, it can also be a con, as it may require additional learning for users who are not already familiar with R programming.
  • Less established than competitors
    Compared to other statistical software like SPSS or SAS, jamovi is relatively new and may not have as extensive a user base or as many community resources.
  • Limited customer support
    As an open-source project, jamovi relies primarily on community support and forums, which may not be as responsive or comprehensive as dedicated customer support services.

Jupyter videos

+ Add

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

jamovi videos

+ Add

jamovi for Data Analysis - Full Tutorial

More videos:

  • Tutorial - PSYS 241: JAMOVI Tutorial 7 - Review
  • Review - Reliability analysis — jamovi
  • Tutorial - JAMOVI 📊. Un robusto software libre de estadística (🔥 2.3 ya en español)
  • Tutorial - Estadística descriptiva con Jamovi 📊 - Tutorial

Category Popularity

0-100% (relative to Jupyter and jamovi)

Jupyter

jamovi

Data Science And Machine Learning
90 90%
Data Science And Machine Learning
10% 10
Business & Commerce
0 0%
Business & Commerce
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
Technical Computing
0 0%
Technical Computing
100% 100

User comments

Share your experience with using Jupyter and jamovi. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Jupyter and jamovi

Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Source: searchbusinessanalytics.techtarget.com
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.
Source: towardsdatascience.com

jamovi Reviews

  1. Bob Muenchen
    · Retired statistician at University of Tennessee ·
    Beautiful User Interface

    jamovi has one of the most attractive user interfaces. Even the colors used for window-dressing match the default colors for its graphs. Like JASP, its dialogs provide instant results as each item is checked off. That immediate feedback feels great! Corrections to data values are also immediately reflected in each piece of output that would be affected. However, this also means that you can't do one step, restructure the data, then do another since jamovi requires each step to have the same data structure. SPSS, Minitab, BlueSky Statistics, and JMP can all do such common data-wrangling tasks. So, if you restructure your data a lot, you'll need to do that with another tool and read the data in separately for each structure. jamovi's menus start out very sparse and you extend them by downloading needed parts later. This is the opposite of similar tools like SPSS, Minitab, and BlueSky Statistics, which show all their capabilities upon installation. That makes it good for beginners who avoid the others' complex menus. Regarding analytic methods, jamovi has the most popular statistics. The main topics it lacks are quality control and machine learning/AI. Also, it cannot save models for making predictions on a different dataset.

    👍 Pros:    Ui is very attractive|Feedbacks
    👎 Cons:    Limited features
Free statistics software for Macintosh computers (Macs)
Other notes. Developer Jonathon Love pointed us to the Jamovi library of extra procedures. A long, well-illustrated Jamovi blog post also goes over the fine graphics capabilities within Jamovi, which PSPP can only dream of. In our run-throughs, the numbers were identical to SPSS, PSPP, and JASP.
Source: www.macstats.org
10 Best Free and Open Source Statistical Analysis Software
Jamovi is a free and open source statistical software built on ‘R' language. Intuitive interface, quality spreadsheet, optimized analysis are the key reasons for its popularity. It performs all statistical tests with reliability and competence.
Source: www.goodfirms.co

Social recommendations and mentions

Based on our record, Jupyter seems to be more popular. It has been mentiond 216 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / about 1 month ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
View more

jamovi mentions (0)

We have not tracked any mentions of jamovi yet. Tracking of jamovi recommendations started around Mar 2021.

What are some alternatives?

When comparing Jupyter and jamovi, you can also consider the following products

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

JASP - JASP, a low fat alternative to SPSS, a delicious alternative to R.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Statista - The Statistics Portal for Market Data, Market Research and Market Studies

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Montecarlito - MonteCarlito is a free Excel-add-in to do Monte-Carlo-simulations.

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