DW540G - 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 leverage all the data while still achieving high performance throughout the entire modeling cycle - from data prep through exploratory data analysis through to model scoring and deployment. By leveraging 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/score a model in a single environment while leveraging increasing massive data sets and shrinking the time from model concept to deployment.
This course will focus on how to effectively leverage the IBM Netezza Analytics platform to build, test and score analytic models in-database. You will learn how to leverage:
- In-Database Analytics fully scalable and parallelized in-database analytics package,
- Enterprise R, the statistical language, that runs on Netezza.
- Matrix Engine, a parallelized, linear algebra package
- Understand how the 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
- Invoke Netezza Analytics data mining methods and statistical functions using the R client and/or Netezza SQL (NZSQL)
- Convert existing R statistical modules/functions to leverage the Netezza platform
This advanced course is for analytic modelers including: data miners, quantitative analysts and statisticians.
- Overview IBM Netezza Architecture
- Getting Started with IBM Netezza Analytics
- Data Exploration
- Data Mining with IBM Netezza Analytics using unsupervised learning
- Data Mining with IBM Netezza Analytics using supervised learning
- Manipulating large matrices in database
- Working with User Defined Analytical Processes
You should have:
- Basic understanding of using advanced analytics (statistics, data mining, etc.) in business problem solutions
- Working knowledge of R or SQL