Jupyter Notebook vs. Microsoft R Open / Revolution R Enterprise

I would rate it 9/10 while recommending Jupyter Notebook as it offers me a wide range of functionality to operate. It is very well suited for someone who is new to python programming as the user interface helps you build code line by line. I personally have written multiple programs in Python using Jupyter Notebook as it helps me organize long code by breaking it in a structure. Also the ability to write comments using '#' helps a lot to a reader understand the code.

Incentivized

Revolution Analytics is a very compelling product for Big Data Analytics. It allows distributed computing over multiple hadoop nodes thus allowing HDFS to do its role cleanly i.e. cheap massive storage and it does good job of running algorithms using R or similar programming language on Hadoop. It would be definitely advantage for the organization who uses either R or SAS as their statistical model development tool as Rev-R support both the platforms. Overall, very positive experience with Rev-R.

• Coding and error correction line by line
• Simple and Effectiveness
• Easy to use for visualisation and presentation of code
• Could be used at any place any time without hassle

Incentivized

• Parallel processing
• Integration with R
• Open-source

Incentivized

• 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.

Incentivized

• Very high learning curve

Incentivized

In general, Revolution Analytics brings a lot of value to the organization. The renewal decision would be based on return on investment in terms of quantified actionable insights that are getting generated against the cost of the product. Additionally, market brand of the tool and reputation risk in terms of possible acquisition and its impact to overall organizational analytic strategy would be considered as well.

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.

Incentivized

It is good, easy to use, improvements are being made to the product and more info being shared in the community. It just needs some more time to become more integrated to other platforms and tools/data out there.

Incentivized

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

Incentivized

Generally support comes through the forums and user generated channels which are helpful, easy to access, quickly turned around and provided by knowledgeable users. However the support channels are not employees and the channels are often used as a way to learn quick difficult elements of R. Better design, users interface and tutorial options would alleviate the need for this sort of interaction.

Incentivized

Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot of needs I have for EDA, modeling, and data export that makes other software products fairly redundant.

Incentivized

R is decent for our needs but in the end didn't quite solve all of our needs so moved on. It is a good tool so far. its been a couple months since we last touched it so with changes continuing and more wide spread use and more info being published this tool will improve. Depending upon your needs this can be very easy for you to setup, use, and maintain when compared to other tools out there. My suggestion is to ensure you fully understand your use cases first with data sources identified to ensure this tool can meet your needs.

Incentivized

• 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

Incentivized

• Better forecasting for resource allocation has saved our organisation hundreds of thousands in conjunction with other strategies.
• Better visualisation options has allowed smoother internal marketing and internal comms strategies when preparing teams for seasonality.

Incentivized