IBM SPSS Modeler vs. Posit

Modeler is well suited for understanding consumer data. The ability to create a prediction and then to understand what is driving that prediction is strong in Modeler. Modeler is closely aligned with the CRISP-DM data mining approach meaning it is not just the 'doing' but also the theoretical background behind the development of data mining models.

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

• A very nice and easy to use interface.
• A great variety of analytics, from statistical calculation to data validation and predictive statistics.
• Has a steep learning curve.

Incentivized

• RStudio does an excellent job providing a clean user interface for R or Shiny applications
• RStudio integrates natively with version control software
• Users can program with either R or Python
• RStudio has a command line built in, eliminating the need for a separate program for a REPL

Incentivized

• Some Analyses aren't there out of the box but can be added through open languages like R and Python.
• Graphs could be better.
• Unable to read data stored in OLAP databases

Incentivized

• Ability to scale across the company is limited based on the users license, cannot share a dashboard to the general view of the company.
• Ability to retain session - not simple method to customize view per user (e.g., once session is ended, the users will return next time to the baseline view).
• Ability to enable communication between multiple users - leave notes, tag other users, or share specific view.

Incentivized

There is no other platform that meets our needs. Even if it was terrible we would still use it but fortunately for us it is a very solid project with a great support team. I hope in the future to expand our use and get more licences as well as upgrade to RStudio workbench but for now we are very happy.

Incentivized

The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

Incentivized

For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when

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

The online support board is helpful and the free add ons are incredibly appreciated.

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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

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

We additionally use SAS Data Miner as a toolkit. Compared to SAS Data Miner, the SPSS Modeler is a good competitor. SAS probably is more integrated in the market for a visual-based code for data science activities. However, I don't think it offers anything better than SPSS, and I really like several of the helpful components for usability for SPSS like peaks into nodes.

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

I think that RStudio scales pretty well based on the size of the datasets I'm using. It has multithreading capabilities unlike some other statistical analysis programs which is very useful in cutting down on time. The format of RStudio's syntax also makes it very easy to replicate regardless off the scale of the analysis and data set

Incentivized

• I am able to study and work from home sustainably
• I can help others have a high quality university education experience to graduate confident and competent to meet gaps in the wider community
• Market research for my business
• Help other small businesses to create viable and high quality products and services
• Contribute to research projects: ethical, high quality data analyses and interpretation

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