IBM Watson Studio, previously known as Data Science Experience, is a comprehensive platform that enables data scientists to collaborate on projects using various analytics tools and languages in a secure environment. Launched in 2016, it integrates popular open-source tools and provides access to datasets through the Watson Data Platform, functioning in cloud, on-premises, and desktop forms. The document details the platform's history, creation process, and how to utilize its features for data analysis and visualization.

1. IBM Watson Studio
BY: DEEPAK TYAGI

2. About IBM Watson Studio
 Watson Studio, formerly Data Science Experience or DSX, is IBM’s software
platform for data science. The platform consists of a workspace that includes
multiple collaboration and open-source tools for use in data science.
 In Watson Studio, a data scientist can create a project with a group of
collaborators, all having access to various analytics models and using various
languages (R/Python/Scala). Watson Studio brings together staple open source
tools including RStudio, Spark and Python in an integrated environment, along
with additional tools such as a managed Spark service and data shaping
facilities, in a secure and governed environment.
 Watson Studio provides access to data sets that are available through Watson
Data Platform, on-premises or on the cloud. The platform also has a large
community and embedded resources such as articles on the latest
developments from the data science world and public data sets. The platform
is available in on-premises, cloud, and desktop forms.

3. History
 IBM announced the launch of Data Science Experience at the Spark
Summit 2016 in San Francisco. IBM invested $300 million in efforts to
make Spark the analytics operating system for all of the company's big
data efforts.
 In June 2017, Hortonworks and IBM announced their partnership to
collaborate on IBM's Data Science Experience. Hortonworks previously had
a partnership relationship with Microsoft.
 In 2018, the Data Science Experience platform was renamed IBM Watson
Studio.

4. How to create and use Watson
studio

5. Firstly click on the catalog and search for Watson studio

6. After that, select Watson studio

7. In location select Frankfurt and create service

8. After that, go back to catalog and search for Watson
machine learning

9. Now add location Frankfurt and create service and go
back to Watson Studio and launch IBM Cloud

10. Now click on create project

11. Now click on “create an empty project”

12. Give a name to your project and click on create

13. After creating Watson studio, open a new tab and search
for car dataset in Kaggle and select first link

14. After opening the link click on the download button and
extract the folder from zip file

15. Create a new asset and click on “AutoAI”

16. Define a name to your project and click on associate
machine learning

17. Select the Watson machine learning service you created in
starting and reload and create the project

18. After creating the project click on browse and select any
file from the folder you downloaded earlier from the
kaggle

19. After opening the file click no and select any option and
run experiment

20. After the project runs, scroll down and save the first
pipeline which is marked with a star and just click on the
create button

21. Now click on the view in project and click on “promote to
deployment space”

22. Create a new space and give it a name and click on
“promote”

23. After promoting click on the deployment space

24. After opening deployment space click on the three dots in
the corner and click on deploy and go to space setting

25. Click on associate instance and select the Watson machine
learning you created and save the service

26. Then come back to assests and again click on deploy, after
that give a name to your project and create it and wait for
the initializing

27. And your project is deployed

28. Now you can click on your project name to check the
different language api refereneces

29. How to test different methods in
project in Watson Studio

30. Now go back to the Watson studio service and launch ibm
watson to open the project you created

31. Now click on new asset and click to data refinery

32. Click on data asset and select the file you uploaded

33. Now click on the new step and select the method you
want to use

34. Now add the column, operator and value and apply it

35. Here is your filtered data

36. Now go to visualization and select the column to visualize
and chart type to represent data

37. Here is the data for pie chart, now you can use this data to
check the number of cars types in the market and can
explore other charts also