IBM Watson Studio on Cloud Pak for Data vs. Posit

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

Incentivized

In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.

Incentivized

• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

Incentivized

• The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
• The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
• Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

Incentivized

• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

Incentivized

• Python integration is newer and still can be rough, especially with when using virtual environments.
• RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
• Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.

Incentivized

because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

Incentivized

There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.

Incentivized

The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

Incentivized

I think it's a quick and easy to use tool. The IDE is very intuitive and easy to adapt to. You do not need to learn a lot of things to use this tool. Any programmer and a person with knowledge or R can quick use this tool without issues.

Incentivized

From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

Incentivized

RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses

Incentivized

Never had slow response even on our very busy network

Incentivized

I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

Incentivized

Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.

Incentivized

The trainers on the job are very smart with solutions and very able in teaching

Incentivized

The Platform is very handy and suggests further steps according my previous interests

Incentivized

It surprised us with unpredictable case of use and brand new points of view

Incentivized

We did it at the individual level: anyone willing to code in R can use it. No real deployment involved.

The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

Incentivized

RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.

Incentivized

It helped us in getting from 0 to DSX without getting lost

Incentivized

RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.

Incentivized

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

Incentivized

• Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
• Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
• What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).

Incentivized