What is IBM Data Science Experience (DSx)?

We talked about Jupyter Notebook, IDEs for Data Science, Python packages for Data Science etc. It is normal to hear about IBM Data Science Experience. Definitely you noticed that we have not included it in our IDE or other lists. Then, What is IBM Data Science Experience (DSx) Which Can Not Be Listed Among Those Articles? IBM Data Science Experience is a platform for data science for working with RStudio, Spark, Python on Cloud, Server or Desktop. It has the Watson part which makes it unique and the Desktop Application not eligible to be in our other articles. But it definitely needs a mention.

 

What is IBM Data Science Experience (DSx)?

 

It is kind of a cloud based web service. Functionally it is more than those notebooks in the cloud like service as tools like Jupyter, RStudio, Spark, Watson are integrated. It is not exactly a full typical IDE with debugger. The Desktop applications run on client-server model. IBM Data Science Experience (DSx) is assumedly built on the top of their Bluemix cloud technology with multiple services pre-configured and ready to use. There is also option to add an Object Store to the Spark instance as storage space.The desktop applications are powered by Electron framework which gives a look closer to all typical IDEs of these days.

Not to forget, it has Watson to create, train, and deploy self-learning models. These matters do not mean we will not look at the solution and offerings. DSx quite recently launched and IBM rumoured to be invested $300 million in efforts. IBM has targeted this platform as new generation Data Science development and training platform.

The first thing anyone will notice after logging to this cloud platform is Jupyter notebooks and if the user has Jupyter notebooks on Bluemix they’ll be accessible from the home of the control panel. Users can create connections from Data Science Experience to popular services like Amazon S3, Azure, Salesforce.com, external Oracle, Greenplum, Sybase, MySQL databases, Hadoop (Hive, Impala) and others to important things and data.

It has three part – Data Science, Data Hub, and Exchange. Data Science area is intended to create and work with notebooks, and also access various data science related articles, tutorials. In Data Hub area the users will create and work with Projects. In the Exchange part users will get access to data sets, notebooks, and storybooks (Watson Analytics) shared by other people. There is a collaboration part. Supported permissions/roles is Viewer, Editor and Admin. Tolls are Notebooks and RStudio. There are good number of official guides like :

Notebooks from Github can be directly pulled inside the platform. There is option for GitHub integration in settings to publish notebooks on GitHub as well.