Experienced Analysis with IBM Watson
January 02, 2021

Experienced Analysis with IBM Watson

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with IBM Watson Studio (formerly IBM Data Science Experience)

We primarily use IBM Watson Studio (formerly IBM Data Science Experience) as training for developing analytical skills in Coursera courses.
  • The platform offers the ability to integrate with other platforms.
  • The interface is intuitive and easy to use.
  • The interface allows you to use different tools as needed like Jupyter notebooks and DB2.
  • It can be difficult to navigate at times.
  • The platform does not offer as much educational material as I would like.
  • IBM Watson Studio (formerly IBM Data Science Experience) has allowed our team to develop skills by offering a platform that allows varying analysis techniques.
We have not utilized the integration with Data Catalog or Data Refinery, but this feature does sound useful. We will likely use this if the need arises.
Coursera introduced us to IBM Watson Studio (formerly IBM Data Science Experience). We never considered using the open-source tools directly because they were already available on the platform.
Paylocity, Microsoft 365 (formerly Office 365), Adobe PhotoShop
IBM Watson Studio (formerly IBM Data Science Experience) is very useful for using different analysis techniques to import and interpret data sets. Converting and cleaning data is easy using IBM Watson Studio (formerly IBM Data Science Experience), so it is very helpful in scenarios where you know what you're trying to achieve. From an educational standpoint, it would be nice if the platform offered more instance-related material, but the platform is very useful for experienced analysis.

IBM Watson Studio Feature Ratings

Connect to Multiple Data Sources
10
Extend Existing Data Sources
8
Automatic Data Format Detection
10
MDM Integration
8
Visualization
9
Interactive Data Analysis
10
Interactive Data Cleaning and Enrichment
10
Data Transformations
10
Data Encryption
10
Built-in Processors
10
Multiple Model Development Languages and Tools
10
Automated Machine Learning
8
Single platform for multiple model development
10
Self-Service Model Delivery
7
Flexible Model Publishing Options
10
Security, Governance, and Cost Controls
10