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SAS® In-Memory Statistics
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SAS® In-Memory Statistics
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Get answers almost instantly. SAS In-Memory Statistics provides a single interactive programming environment for analytical data preparation, variable transformations, exploratory analysis, statistical modeling and machine-learning techniques, integrated modeling comparison, and scoring.
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The most recent release is SAS In-Memory Statistics 2.6.

What’s New

Support for working with the third maintenance release of SAS 9.4 and SAS LASR Analytic Server 2.7.

Other features include:

  • SAS In-Memory Statistics enables interactive programming so that multiple users can analyze data extremely quickly.
  • SAS In-Memory Statistics utilizes in-memory analytical processing, so you can create, evaluate, and compare multiple statistical models quickly.
  • You can use statistical algorithms and machine-learning techniques to uncover patterns and trends in the data stored in Hadoop or Teradata.
  • You can analyze unstructured (and structured) data using a wide range of text analysis techniques.
  • You can also generate personalized, meaningful recommendations in real time with a high level of customization.
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Training Courses

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Learn SAS technology quickly and efficiently by taking a course from the analytics experts.
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Getting Started with SAS® In-Memory Statistics

Learn how to access data on the SAS LASR Analytic Server and perform exploratory analysis and preparation. Topics include starting the SAS LASR Analytic Server, loading data onto it and manipulating that data 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.

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. Learn about methods such as decision trees and random forests using the DECISIONTREE and RANDOMWOODS statements, as well as modeling a binary response using the LOGISTIC and NEURAL statements.

Learning path for advanced analytics

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Documentation

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Find user's guides and other technical documentation for SAS In-Memory Statistics.
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SAS In-Memory Statistics 2.6

Installation

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