Course description: IBM Netezza Analytics for Data Scientists - Using R and NZSQL

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.

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Roadmaps that contain this course are:

• PureData System for Analytics, powered by Netezza
• Netezza Appliance

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

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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

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• 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

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Agenda

Day 1

• Introductions and course overview
• Unit 1 - Overview of IBM Netezza architecture
• Unit 2 - Getting started with IBM Netezza Analytics
• Unit 3 - Data exploration
• Unit 4 - Data mining with IBM Netezza Analytics: Unsupervised learning methods

Day 2

• Unit 5 - Data mining with IBM Netezza Analytics: Supervised learning methods
• Unit 6 - Manipulating large matrices
• Unit 7 - User-defined analytical processes

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Practical Work:

This course is structured in a lecture/lab format. The hands-on sessions form a vital & integral part of the course.

May 2012

V1.1

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