Echo Dot (3rd Gen) - Smart speaker with Alexa - Charcoal

Use your voice to play a song, artist, or genre through Amazon Music, Apple Music, Spotify, Pandora, and others. With compatible Echo devices in different rooms, you can fill your whole home with music.

Buy Now

Wireless Rechargeable Battery Powered WiFi Camera.

Wireless Rechargeable Battery Powered WiFi Camera is home security camera system lets you listen in and talk back through the built in speaker and microphone that work directly through your iPhone or Android Mic.

Buy Now

Analyze credit risk through visualizations – Build Smart. Build Secure. IBM Developer



This developer code pattern showcases the integration between Watson™ Studio and Cognos® Analytics by guiding you through an examination of credit risk-related data.


Cognos Analytics on Cloud and Watson Studio on Cloud now work better together. Cognos Analytics users can now connect to the more powerful data science capabilities in Watson Studio: AutoAI, Jupyter Notebook, and GPUs. With this integration, both data science and business intelligence teams can share a single ecosystem to make the most of their organizations’ data.

The integration between the two offerings serves as a bridge to empower data scientists and business analysts to collaborate on the cloud. Data scientists can easily script against governed Cognos data in Watson Studio and share results back into the Cognos ecosystem.

This code pattern showcases this integration by guiding you through an examination of credit risk-related data. You will refine the data and build a model using Watson Studio and IBM Watson Machine Learning. The model is then used to score new credit applications to determine whether they are a risk. The results are then fed into Cognos Analytics, where you can create visualizations to provide greater insights into the factors that most influence the credit-worthiness of the applicants.


Flow diagram

  1. Credit-risk data is loaded into Cognos Analytics.
  2. Data scientist runs Jupyter Notebook in Watson Studio.
  3. Data from Cognos Analytics is loaded into Jupyter Notebook, where it is prepared and refined for modeling.
  4. Jupyter Notebook uses Watson Machine Learning to create a credit-risk model.
  5. New credit applications are scored against the model, and results are pushed back into Cognos Analytics.
  6. Business analyst runs Cognos Analytics to visualize the results.


Find the detailed steps for this pattern in the README. The steps will show you how to:

  1. Clone the repo.
  2. Upload the data file into Cognos Analytics.
  3. Create a new Watson Studio project.
  4. Create a Cognos Analytics connection in Watson Studio.
  5. Create a data access token.
  6. Create the notebook in Watson Studio.
  7. Add data to the notebook.
  8. Run the notebook.
  9. Refine the data and create a data model.
  10. Write out data using Cognos Analytics connection.
  11. Visualize the data in Cognos Analytics.

Read More


Please enter your comment!
Please enter your name here