Software Alternatives, Accelerators & Startups

JASP VS Jupyter

Compare JASP VS Jupyter and see what are their differences

JASP logo JASP

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

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.
  • JASP Landing page
    Landing page //
    2023-05-08
  • Jupyter Landing page
    Landing page //
    2023-06-22

JASP features and specs

  • User-Friendly Interface
    JASP offers an intuitive and visually appealing interface that is easy for users to navigate, making statistical analysis accessible even to those who are not heavily experienced in statistics.
  • Open Source
    Being open-source, JASP is available for free, enabling anyone to use it without financial barriers and allowing for community-driven improvements and customizations.
  • Bayesian Methods
    JASP includes a wide array of Bayesian statistical tools, providing advanced options for users interested in Bayesian inference, which is often not as well-supported in other statistical software.
  • Integration with R
    JASP allows for integration with R, providing flexibility for users who wish to perform more customized or complex analyses by incorporating R scripts within the user-friendly JASP environment.
  • Dynamic Reports
    The software enables users to generate dynamic reports that update in real-time as data changes, streamlining the reporting process and making it easier to share findings.

Possible disadvantages of JASP

  • Limited Customization
    While JASP provides a great user interface and many built-in options, it offers less customization and fewer advanced features compared to more flexible software like R or Python.
  • Performance Issues with Large Data Sets
    JASP may struggle with performance issues when handling extremely large datasets, potentially causing delays or crashes during analysis.
  • Dependence on Internet Connection for Some Features
    Some of JASP's functionalities rely on an active internet connection, which can be limiting in situations where such a connection is unreliable or unavailable.
  • Limited Support for Complex Data Manipulation
    JASP is not designed for extensive data manipulation or cleaning tasks, requiring users to preprocess their data using other tools before importing it into JASP for analysis.
  • Relatively New Software
    As a newer entrant in the field of statistical software, JASP lacks the extensive user base and comprehensive third-party resources available for more established software platforms.

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.

JASP videos

Introducing JASP

More videos:

  • Review - Berkenalan dengan JASP: Software Analisis Data Gratis dan Lengkap
  • Review - Gusion Legend Skin Cosmic Gleam Review | Jasp GamIng

Jupyter videos

What is Jupyter Notebook?

More videos:

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

Category Popularity

0-100% (relative to JASP and Jupyter)
Business & Commerce
100 100%
0% 0
Data Science And Machine Learning
Technical Computing
100 100%
0% 0
Data Dashboard
0 0%
100% 100

User comments

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Reviews

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

JASP Reviews

  1. Bob Muenchen
    · Retired statistician at University of Tennessee ·
    Good choice for teaching stats

    JASP works very similarly to jamovi. That's not a coincidence, as some JASP developers split off to create jamovi. You can open a single dataset and use the most popular statistics and machine learning methods. But if you have multiple datasets to merge, you must do that in another tool. Also, the dataset must maintain a single structure throughout your analyses. Restructuring or transposing is not allowed. It is commonly said that data scientists spend 80% of their time wrangling data like that, so that's a significant limitation for general use. However, those simplifications make JASP a good choice for teaching. Another advantage for teaching is that the menus are very sparse, but you can add to them easily by downloading additional modules. That's the opposite of similar software such as BlueSky Statistics, SPSS, or Minitab, which install all features at once. If you're looking for free and open-source software, JASP and jamovi are best for teaching while BlueSky Statistics is best for general-purpose analysis.

    🏁 Competitors: BlueSky Statistics
    👍 Pros:    Easy user interface
    👎 Cons:    Limited features

Free statistics software for Macintosh computers (Macs)
JASP and Jamovi share lightning-fast speed; a wide range of statistics, with extra plugins on Jamovi; and easy installation on Macs, Windows, and Linux. Their basic interface has an Office 365-style open/save/print/export tab; options on the left, output on the right layout; instant changes to the output if you change the input; and export of both data and output, as...
10 Best Free and Open Source Statistical Analysis Software
Jeffreys’s Amazing Statistics Program (JASP) came into existence as a free and open source alternative to SPSS with powerful Bayesian analyses as its core feature. It has a user-friendly interface. Results are annotated with descriptive text to make analysis easy.
25 Best Statistical Analysis Software
This versatile, free, and open-source statistical software is specifically designed to cater to the needs of researchers and students. With its user-friendly interface, JASP makes data analysis and visualization more accessible and efficient.

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.
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.

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than JASP. While we know about 216 links to Jupyter, we've tracked only 15 mentions of JASP. 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.

JASP mentions (15)

  • Bayesian Epistemology
    For anyone looking for a quick and hands-on dive into the world of Bayesian modelling and inference, I can't recommend JASP enough, made freely available by the University of Amsterdam[0]. I've recommended it before, and it's just a breeze to work with, seeing frequentist and Bayesian analyses side-by-side. [0]: https://jasp-stats.org/. - Source: Hacker News / 3 months ago
  • Introduction to Modern Statistics
    Anyone looking to apply and compare frequentist and bayesian methods within a unified GUI (which is essentially an elegant wrapper to R and selected/custom statistical packages), should check out JASP developed by the University of Amsterdam [0]. It's free to use, and the graphs + captions generated on each step are of publication quality out of the box. Using it truly feels like a 'fresh way' to do... - Source: Hacker News / over 1 year ago
  • Can anyone share spss for macOS?
    Https://jasp-stats.org fully free. Its advisible to learn python, R or matlab for graduate school. Source: almost 2 years ago
  • Help with my analysis in spss. I have 5 independent (ordinal) variables. 1 Moderator and 1 dependent variable. How do I run a multiple regression in SPSS?
    Also for alternative software that are much easier to use take a look at JASP or jamovi (both are very similar); and as a bonus, neither of these two will require you to manually add product variables to your dataset. Source: almost 2 years ago
  • [D] Discussion: R, Python, or Excel best way to go?
    If you have no access to SPSS (or SAS, or JMP), then look into JASP (https://jasp-stats.org/). I've only just touched that. One thing I believe is that JASP (as well as JMP) will allow/block off tests and analyses depending on the nature of each column. This means that, for example, if you have groups A, ..., Z, the software will treat those as non-numbers, which can only be used as inputs for variables which... Source: about 2 years ago
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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
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What are some alternatives?

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

jamovi - jamovi is a free and open statistical platform which is intuitive to use, and can provide the...

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.

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

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

BlueSky Statistics - BlueSky Statistics is a fully featured statistics application and development framework built on...

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