Learning path: Get started with data science using Netezza

Level Topic Type
100 Access and analyze data in Netezza Performance Server Article
101 Load and access data from your cloud data warehouse Code pattern
102 Predict energy prices with in-database analytics Code pattern

This developer data science learning path targets a modern data warehousing use case where analytics and data warehousing are combined in one solution. You will learn how to load and access data from your Netezza data warehouse using methods such as loading from a notebook to a table using Python packages, or directly querying a file on cloud object storage through external tables. We will then create a project to analyze, visualize, and build a machine learning model using the in-built data science and statistical algorithms available on Netezza Performance Server. This can be replicated using a Netezza system on-prem (Netezza on IBM Cloud Pak for Data) or Netezza on Cloud (Netezza on Cloud Pak for Data, on IBM Cloud, AWS, and Azure).

To get started, click on a card below, or see the table above for a complete list of topics covered.

Access and analyze data in Netezza Performance Server

Learn about:

  • Netezza Analytics features
  • Integrating data science tools and languages
  • Determining whether to use an in-database or an integrated solution

Load and access data from your cloud data warehouse

Learn how to:

  • Load data from various sources
  • Access the data to perform research or other business-related activities
  • Analyze and visualize the Netezza Performance Server data

Predict energy prices with in-database analytics

Learn how to:

  • Leverage the development and use of analytic algorithms
  • Analyze data using Netezza in-database analytic functions
  • Create machine learning models using Netezza in-database algorithms


Next: Access and analyze data in Netezza Performance Server