March 21, 2018


Anonymous | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User

Overall Satisfaction with IBM Data Science Experience (DSx)

IBM Data Science Experience was used by my organization for projects which required the use of Machine learning. Mainly the R Jupyter notebook was used as well as the SPSS Modeler. It was used by just a department. The main business problem was predicting Pavement condition index for highways.
  • DSx provides an excellent support for machine learning modeling
  • DSx provides a good environment for collaboration between colleagues
  • DSx also provides support for sharing datasets, models, notebooks, and articles to start projects
  • They should involve the drag and drop functionality more into DSx for data analysts who are not so much into coding
  • Also, scripting nodes should be integrated into the drag and drop(SPSS MODELER)
  • Also, more nodes should be added to the SPSS Modeler. For example, remove duplicates node, edit metadata node etc.
  • IBM DSx helped us meet some needs of our business clients
  • Not all business users could fully utilize DSx as they don't have much experience in coding
  • Most Business requirements for a problem were solved
I wanted an environment that can support multiple users without any restrictions. Also, R-Studio does not provide a collaborative environment for multiple users. The Auto feature selection in the SPSS modeler is a good node in DSx which helps make statistical decisions on evaluating what features are relevant. Other products do not have this feature.
IBM DSx is best suited for creating cloud-based machine learning modeling. Its support for open-source software such as Python and R is a plus. For Data Analyst who prefers writing codes for all their algorithms, DSx is a better place to do this. The latest packages for the software can be added and installed easily too.