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Showing posts with label SAS. Show all posts
Showing posts with label SAS. Show all posts
DataFlux(R) Data Management Studio: Creating a New Data Type in the Quality Knowledge Base Training In Hyderabad India

DataFlux(R) Data Management Studio: Creating a New Data Type in the Quality Knowledge Base Training In Hyderabad India


DataFlux(R) Data Management Studio: Creating a New Data Type in the Quality Knowledge Base



This course is designed for data quality stewards who need to learn more about how to use the DataFlux Data Management Studio to create new data types in the Quality Knowledge Base.


Duration: 10-15hrs
Course Content:

Introduction
  • Creating and registering a backup copy of the course QKB.
  • Opening a QKB and investigating data types.
  • Creating a new data type.
Creating a Parse Definition
  • Creating a parse definition for clothing data.
  • Adding the necessary components.
  • Using the Parse Definition Quick Editor (PDQE).
Creating a Case Definition
  • Introduction to case definitions.
  • Creating a case definition.
Creating a Standardization Definition
  • Introduction to standardization definitions.
  • Creating a standardization definition.
Creating a Match Definition
  • Introduction to match definitions.
  • Creating a match definition.
DataFlux(R) Data Management Studio: Understanding the Quality Knowledge Base Training In Hyderabad India

DataFlux(R) Data Management Studio: Understanding the Quality Knowledge Base Training In Hyderabad India


DataFlux Data Management Studio: Understanding the Quality Knowledge Base


This course is designed for data quality stewards who need to learn more about the Quality Knowledge Base (QKB), as well as how to use DataFlux Data Management Studio to investigate and work with the QKB and its components.


Duration: 20-25hrs
Course Content:

  • Introduction to the SAS Quality Knowledge Base (QKB)
  • Working with QKB Component Files
  • Working with QKB Definitions
  • Parse Definitions
  • Case Definitions
  • Standardization Definitions
  • Match Definitions
  • Identification Analysis Definitions and Right Fielding
  • Gender Analysis Definitions
  • Extraction Definitions
SAS(R) Grid Manager Administration Training In Hyderabad India

SAS(R) Grid Manager Administration Training In Hyderabad India


SAS(R) Grid Manager Administration


In this course, you learn how to administer a SAS Grid Manager environment, including the users, applications, and resources in that environment. After you complete this course, you will understand SAS Grid Manager concepts and terminology and have a firm grasp of the fundamentals of the SAS Grid Manager architecture.


Duration: 20-25hrs
Course Content:

  • Introduction to SAS Grid Computing
  • Grid Manager Architecture
  • Clients and the Grid
  • SAS Grid Manager Administration
  • SAS Grid Manager Administration: Advanced Topics
  • Troubleshooting
SAS(R) Real-Time Decision Manager: Creating and Managing Campaigns Training In Hyderabad India

SAS(R) Real-Time Decision Manager: Creating and Managing Campaigns Training In Hyderabad India


SAS(R) Real-Time Decision Manager: Creating and Managing Campaigns


This course provides an overview of SAS Real-Time Decision Manager 6.5 and prepares you to construct inbound marketing decisions using the solution.


Duration: 30-35hrs
Course Content:

  • Introduction to SAS Real-Time Decision Manager
  • Campaign Fundamentals and Basic Nodes
  • Advanced Nodes
  • Creating Treatments
  • Treatment Eligibility and Arbitration
  • Decision Treatment Campaigns and Campaign Sets
  • Finalizing a Campaign
  • History Campaigns
SAS Certified Statistical Business Analyst Using SAS 9 Training in Hyderabad India

SAS Certified Statistical Business Analyst Using SAS 9 Training in Hyderabad India

SAS Certified Statistical Business Analyst Using SAS 9

This course is designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis. It covers analysis of variance, linear and logistic regression, preparing inputs for predictive models, and measuring model performance.

Duration: 40-45hrs

Course Content:

Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

Course Overview and Review of Concepts
  • Descriptive statistics.
  • Inferential statistics.
  • Examining data distributions.
  • Obtaining and interpreting sample statistics using the UNIVARIATE procedure.
  • Examining data distributions graphically in the UNIVARIATE and FREQ procedures.
  • Constructing confidence intervals.
  • Performing simple tests of hypothesis.
  • Performing tests of differences between two group means using PROC TTEST.
ANOVA and Regression
  • Performing one-way ANOVA with the GLM procedure.
  • Performing post-hoc multiple comparisons tests in PROC GLM.
  • Producing correlations with the CORR procedure.
  • Fitting a simple linear regression model with the REG procedure.
More Complex Linear Models
  • Performing two-way ANOVA with and without interactions.
  • Understanding the concepts of multiple regression.
Model Building and Effect Selection
  • Automated model selection techniques in PROC GLMSELECT to choose from among several candidate models.
  • Interpreting and comparison of selected models.
Model Post-Fitting for Inference
  • Examining residuals.
  • Investigating influential observations.
  • Assessing collinearit.
Model Building and Scoring for Prediction
  • Understanding the concepts of predictive modeling.
  • Understanding the importance of data partitioning.
  • Understanding the concepts of scoring.
  • Obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM.
Categorical Data Analysis
  • Producing frequency tables with the FREQ procedure.
  • Examining tests for general and linear association using the FREQ procedure.
  • Understanding exact tests.
  • Understanding the concepts of logistic regression.
  • Fitting univariate and multivariate logistic regression models using the LOGISTIC procedure.
  • Using automated model selection techniques in PROC LOGISTIC including interaction terms.
  • Obtaining predictions (scoring) for new data using PROC PLM.

Predictive Modeling Using Logistic Regression
    Predictive Modeling
    • business applications
    • analytical challenges
    Fitting the Model
    • parameter estimation
    • adjustments for oversampling
    Preparing the Input Variables
    • missing values
    • categorical inputs
    • variable clustering
    • variable screening
    • subset selection
    Classifier Performance
    • ROC curves and Lift charts
    • optimal cutoffs
    • K-S statistic
    • c statistic
    • profit
    • evaluating a series of models

    SAS Online Training From Hyderabad India - SAS ADMIN Training Institutes in Hyderabad - SAS Administration Online Training

    SAS Online Training From Hyderabad India - SAS ADMIN Training Institutes in Hyderabad - SAS Administration Online Training 


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    We offer instructor led online training. Classroom trainings are conducted in Hyderabad , India.

    Classroom training in Hyderabad



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