Displayr vs. Jupyter Notebook

Displayr is perfectly suited for any insights or data people that understand the type of analysis they want to do, but don't know R code - or just want to get to results more quickly than coding themselves. It's probably not the best learning ground, if you've never done any quantitative analysis before, but then neither are traditional tools like SPSS or Q.

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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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• The intuitive interface and menus make it easy to quickly learn Displayr and find the types of data transformation or analysis that we're looking to do.
• The support level from Displayr's team is FIRST CLASS. Where othe platforms force you to an FAQ or AI chat bot, Displayr's team will jump in first hand, into our data, or on a live call, and help us run a new type of analysis or troubleshoot a problem.
• The ability to work collaboratively, asynchronously and remotely, on the same data set and report is a really huge plus for us.
• The in-built options for multivariate analysis cover 99.9% of anything we have - or will - ever need to run.

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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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• The new "glow-up" on the interface has helped make it a bit easier on the eye, but there are some features of working in the "three pane" browser that are a bit frustrating: especially having to 'rearrange' when resizing the window to look at another app simultaneously.
• Such a small point, but being able to drag and move multiple elements in a table (eg drag two rows to the top) SIMULTANEOUSLY would help a bunch.
• I don't think we take advantage of all the visualisation capabilities in Displayr, and perhaps an AI 'recommendation' engine that sees the data I'm working with and prompts either a specific visualisation, or additional analysis option I might use, would be great.

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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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It's really quite intuitive, but the visual interface could be made a bit more easy to use (window/pane rescaling etc) and I think there could be more 'proactive prompts' to suggest features we're underutilising.

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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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SPSS (the last version I looked at) still requires much more underlying knowledge and coding ability to get where we want to be. That's not where we add value, so the speed and simplicity with which Displayr allows us to get the data analysis done, and move onto developing insight and delivering value is why I chose Displayr.

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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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• I think Displayr is quite expensive, but has the biggest impact on our P&L of any of our subscriptions, because it has unlocked our ability to deliver bigger, more complex analytic projects for clients - and hence grow our topline.
• The ability to scale the license between years has also been a god-send as our team has gone up or down to deliver the level of quant work available to us.
• There's also a bottom line efficiency driven by some of the speed of analysis that Displayr enables.

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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