This learning offering will tell a holistic story of Cloud Pak for Data including collaboration across an organization, which is key in this platform. Applicable to all personas. Multiple use cases will provide understanding of how organizations can benefit from Cloud Pak for Data. A variety of features will also be explored, providing students with the insight on how to use the platform. This learning is relevant for Cloud Pak for Data and Cloud Pak for Data System. This WBT contains instructional and interactive content, demonstrations and hands-on simulated exercises. 

• Introduction to IBM Cloud Pak for Data  • Red Hat OpenShift Container Platform: overview • Collaboration and workflows  • Collect data  • Organize data • Prepare data  • Analyze data  • Infuse data

• Data Engineer, Data Steward, Data Scientist, Business Analyst, Application Developer, Administrator

• IBM Demo assets: IBM Cloud Pak for Data  (https://www.ibm.com/demos/collection/Cloud-Pak-for-Data/)

Introduction to IBM Cloud Pak for Data  • Describe IBM Cloud Pak for Data  • Identify how IBM Cloud Pak for Data makes you ready for artificial intelligence (AI)  • Describe, at a high level, the IBM Cloud Pak for Data architecture • Describe how to collaborate within IBM Cloud Pak for Data  • Describe the typical end-to-end data and analytics workflow in IBM Cloud Pak for Data • Identify what you will be doing in this training Red Hat OpenShift Container Platform: overview • Describe how the Red Hat OpenShift Container Platform relates to IBM Cloud Pak for Data • Describe the role of containers, Kubernetes, and Helm • Describe how Red Hat OpenShift is a layered system • Describe, at a high level, the Red Hat OpenShift architecture • Describe, at a high level, how Red Hat OpenShift is secured Collaboration and workflows  • Administer the platform • Describe a typical workflow • Create a project  • Search for data • Request data Collect data  • Identify how you connect to data sources in IBM Cloud Pak for Data  • Identify ways in which you can add data to a project • Identify supported data sources  • Describe how to work with an integrated database  • Create a connection to a data source  Organize data • Describe the Watson Knowledge Catalog service and what you can do with it  • Describe how you can work with catalogs  • Describe how you can govern and curate data using Watson Knowledge Catalog  • Identify how governance artifacts and governance tools work together • Identify how you can govern data to comply with regulations • Perform automated discovery and work with the default catalog Prepare data  • Identify ways in which you can prepare data for use in projects  • Describe how to transform data using the DataStage service • Refine data using the Data Refinery service and virtualize data using the Data Virtualization service • Identify how you can access trusted master data with IBM Master Data Connect • Describe how you can build trust in unstructured data with IBM Watson Knowledge Catalog Instascan • Identify how you can manage test data using Virtual Data Pipeline (VDP) Analyze data  • Identify how you can analyze data in IBM Cloud Pak for Data  • Analyze data using notebooks  • Identify other tools that you can use to analyze data  • Deploy machine learning (ML) models  • Automatically analyze your data using the AutoAI tool Infuse data • Identify how you can perform self-service analytics with Cognos Analytics • Describe how you can extract answers from complex business documents with Watson Discovery • Identify how you can deliver engaging, unified problem-solving experiences with Watson Assistant • Describe you can accurately transcribe the human voice with Watson Speech to Text • Identify how you can convert written text to natural-sounding speech with Watson Text to Speech • Describe how you can automate planning, budgeting, and forecasting with Planning Analytics Assessment