Netezza designs and markets high-performance data warehouse appliances and advanced analytics applications for uses including enterprise data warehousing, business intelligence, predictive analytics and business continuity planning.

Netezza is widely credited for either inventing or bringing renewed attention to the data warehouse appliance category, depending upon whether one regards long-time data warehouse technology vendor Teradata as having been in the data warehouse appliance category all along.

Introduction

IBM Netezza Analytics provides a game-changing experience for Data Scientists by allowing data miners and quantitative analysts to use all the data while still achieving high performance throughout the entire modeling cycle: from data preparation through exploratory data analysis through to model scoring and deployment.

By taking advantage of the massively parallel processing architecture of an IBM Netezza Appliance, analytics can be performed in-database so that there is no superfluous data movement. This harnesses the full power of IBM Netezza Appliance and allows data miners and quantitative analysts to greatly reduce the time to build and deploy or score a model in a single environment while using increasing massive data sets and shrinking the time from model concept to deployment

This course focuses on how to effectively exploit the IBM Netezza Analytics platform to build, test, and score analytic models in-database. Participants in this course learn how to use:

• In-Database Analytics fully scalable and parallelized
• In-database analytics package
• R, the open source statistical language, that runs on Netezza
• Matrix Engine, a parallelized, linear algebra package.

AUDIENCE

This advanced course is for analytic modelers, including data miners, quantitative analysts, and statisticians.

PREREQUISITES

You should have the following background:

• Basic understanding of using advanced analytics (statistics, data mining, and so on) in business problem solutions
• Working knowledge of R, SQL, or both
• Working knowledge of Unix or Linux
• Experience with Shell Scripting
• Experience with VI Editing

OBJECTIVES

• Understand how the IBM Netezza architecture and parallel processing capabilities supports modeling and analysis paradigms on large-scale data sets
• Understand data mining methods in the context of use cases to solve common business problems
• Apply new approaches to modeling and analysis made possible by IBM Netezza Analytics
• Use Netezza Analytics data mining methods and statistical functions using the R client, or IBM Netezza
• SQL (NZSQL), or both