Applied Analytics Using SAS(R) Enterprise Miner(TM)

Kód kurzu: AAEM51

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This course covers the skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). This course is appropriate for SAS Enterprise Miner 5.3 up to the current release.

Odborní
certifikovaní lektori

Mezinárodne
uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky
servis

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: E-learning

Dĺžka kurzu: 21 hodín

Jazyk: en

Cena bez DPH: 1 080 EUR

Registrovať

Počiatočný dátum: Na vyžiadanie

Forma: Na vyžiadanie

Dĺžka kurzu: 21 hodín

Jazyk: en

Cena bez DPH: 1 800 EUR

Registrovať

Počiatočný
dátum
Miesto
konania
Forma Dĺžka
kurzu
Jazyk Cena bez DPH
Na vyžiadanie E-learning 21 hodín en 1 080 EUR Registrovať
Na vyžiadanie Na vyžiadanie 21 hodín en 1 800 EUR Registrovať
G Garantovaný kurz

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Kontakt

Cieľová skupina

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Data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner

Štruktúra kurzu

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Introduction

  • Introduction to SAS Enterprise Miner.

Accessing and Assaying Prepared Data

  • Creating a SAS Enterprise Miner project, library, and diagram.
  • Defining a data source.
  • Exploring a data source.

Introduction to Predictive Modeling: Predictive Modeling Fundamentals and Decision Trees

  • Introduction.
  • Cultivating decision trees.
  • Optimizing the complexity of decision trees.
  • Understanding additional diagnostic tools (self-study).
  • Autonomous tree growth options (self-study).

Introduction to Predictive Modeling: Regressions

  • Selecting regression inputs.
  • Optimizing regression complexity.
  • Interpreting regression models.
  • Transforming inputs.
  • Categorical inputs.
  • Polynomial regressions (self-study).

Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

  • Input selection.
  • Stopped training.
  • Other modeling tools (self-study).

Model Assessment

  • Model fit statistics.
  • Statistical graphics.
  • Adjusting for separate sampling.
  • Profit matrices.

Model Implementation

  • Internally scored data sets.
  • Score code modules.

Introduction to Pattern Discovery

  • Cluster analysis.
  • Market basket analysis (self-study).

Special Topics

  • Ensemble models.
  • Variable selection.
  • Categorical input consolidation.
  • Surrogate models.
  • SAS Rapid Predictive Modeler.

Case Studies

  • Banking segmentation case study.
  • Website usage associations case study.
  • Credit risk case study.
  • Enrollment management case study.

Predpokladané znalosti

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Before attending this course, you should be acquainted with Microsoft Windows and Windows software. In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling. Previous SAS software experience is helpful but not required.

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