SAS In-Memory Statistics

Kurstitel / Course title Stufe / Level Kursformat / Course Format
Predictive Modeling Using SAS® In-Memory Statistics
This course focuses on the statistical and machine learning methods for predictive modeling available in the IMSTAT procedure. Topics include building candidate predictive models and assessing predictive models on training and holdout data for honest assessment using the IMSTAT procedure. You learn about methods such as decision trees and random forests using the DECISIONTREE and RANDOMWOODS statements. Modeling a binary response using the LOGISTIC and NEURAL statements is also covered, as is analyzing an interval target with generalized linear models using the GLM and GENMODEL statements. Generating and using Base SAS score code is demonstrated as well. Features of ODS Statistical Graphics are described for visualizing IMSTAT results.

3 Für Fortgeschrittene Classroom Live Web Classroom
Getting Started with SAS® In-Memory Statistics
This course focuses on accessing data on the SAS LASR Analytic Server and performing exploratory analysis and preparation. Topics include starting the SAS LASR Analytic Server, loading data onto the LASR Analytic Server, and manipulating data on the LASR Analytic Server using the IMSTAT procedure. IMSTAT topics include deriving new temporary and permanent tables and columns as well as calculating summary statistics such as means, frequency, and percentiles. Creating filters and joins on in-memory data are also discussed.

3 Für Fortgeschrittene Classroom Live Web Classroom
Data Mining Techniques: Predictive Analytics on Big Data
This course introduces applications and techniques for assaying and modeling large data. The course also presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models, and mixture distribution models. Students perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics, and SAS In-Memory Statistics.

3 Für Fortgeschrittene e-Learning