JMP Pro vs. Jupyter Notebook

JMP Pro is perfectly suited for statistical analysis but users should have some statistical knowledge before using it since there may be some terms/functions in the software that are not widely used in other fields. No prior coding experience is needed to use JMP Pro. However, most people doing data processing would prefer to code their analysis.

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I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

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• Several types of segmentation models
• Conjoint design
• VERY user-friendly

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• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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• JMP Pro is a really powerful tool for doing statistical analysis. Although the click environment does not require coding experience, new learners will still need to take a long time to know the parameters in the function before performing any analysis.
• The output from JMP Pro analysis (regression analysis) is not always easy to understand, especially when the parameters are programmed differently with the other similar software.

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• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

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JMP Pro is a great tool, but it's not user friendly. JMP Pro is something that requires a lot of trial and error to figure out. Minitab is far more user friendly and their help guides walk new users how to do everything. JMP Pro requires taking the time to do the STIPs course, or brute force figuring it out.

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Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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I haven't had a need to contact support. However, all required help is out there in public forums.

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I use both JMP Pro and Minitab. JMP Pro works great for doing the work that needs live edit functionable, and the ability to filter charts on the go. Minitab is what I use for capability analysis and MSAs as the overall UI is much cleaner, and is far nicer to present to management than JMP Pro.

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With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

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• None. It's cheaper than SPSS. You could probably get by with the basic JMP package.

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• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info