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.
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.
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
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
Https://jasp-stats.org fully free. Its advisible to learn python, R or matlab for graduate school. Source: almost 2 years ago
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
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
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 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
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
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
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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