IBM Cloud Pak for Data - Foundations (V2.1.x) - eLearning (6X136G-WBT)

Overview

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, Cloud Pak for Data System, for the IBM Cloud Private platform.

Audience

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

Prerequisites

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

Objective

  • Container Platform
  • Cloud Pak for Data 
  • Collaboration and workflows 
  • Access data 
  • Organize data 
  • Analyze data 
  • Add-ons and Integrations 
  • Administer the platform
Afficher les détails

Course Outline

Container Platform 
• Describe the IBM Cloud Private platform 
• Explain the IBM Cloud Private technical components 

Cloud Pak for Data 
• Describe the Cloud Pak for Data platform 
• Explain the architecture 
• Explore common use cases and their primary personas 
• Describe the collaboration efforts in Cloud Pak for Data 

Collaboration and workflows 
• Describe the personas, roles and permissions in Cloud Pak for Data 
• Describe a typical Cloud Pak for Data workflow 
• Explore how each persona aligns within the workflow 
• Explain the use case that will be used throughout the course 

Access data 
• Describe the differences between a data source and a data set 
• Understand how to find the supported data sources 
• Add and connect to a data source 
• Add a data set to an analytics project 
• Understand Data Virtualization 

Organize data 
• Search and discover assets within Cloud Pak for Data 
• Request data you need for your project 
• Understand data catalog and how to work with it 
• Create and work with a data dictionary 
• Explore and profile data 
• Transform data with ETL 

Analyze data 
• Manage the projects for analyzing data 
• Explain the usage of notebooks 
• Understand RStudio overview 
• Create machine learning models 
• Apply model management and deployment 

Add-ons and Integrations 
• Describe add-ons and integrations 
• Explore available add-ons and integrations 
• Installing an add-on 

Administer the platform 
• Application administration tasks 
• Cluster administration tasks