Best SAS Predictive Analytics and Modeling Techniques Training Course in India | Get Certified for Career in Advanced Analytics

This course covers a range of introductory statistical topics and uses SAS software to carry out analysis. Emphasis will be placed on the interpretation of the results. It covers the skills 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). Ready-to-use procedures handle a wide range of statistical techniques including simple descriptive statistics, data visualization, analysis of variance, regression, categorical data analysis, multivariate analysis, cluster analysis, and non parametric analysis are part of this program.

Suitable for Statisticians and business analysts who want to use a point-and-click interface to SAS; as well as data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner.

Learn how to:

• Generate descriptive statistics and explore data with graphs
• Perform analysis of variance
• Perform linear regression and assess the assumptions
• Use diagnostic statistics to identify potential outliers in multiple regression
• Use chi-square statistics to detect associations among categorical variables
• Fit a multiple logistic regression model
• Define a SAS Enterprise Miner project and explore data graphically
• Modify data for better analysis results
• Build and understand predictive models such as decision trees and regression models
• Compare and explain complex models
• Generate and use score code
• Apply association and sequence discovery to transaction data
• Use other modeling tools such as rule induction, gradient boosting, and support vector machines

The fees is inclusive of

• Fee: ₹66,000+GST | Duration: 48 Hours
• Training & Digital Badge
• Course material
• Two attempts of Global certification worth 250$ Each

Prerequisites

• Before attending this course, you should have knowledge in statistics covering p-values, hypothesis testing, analysis of variance, and regression. 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 necessary.

Course Outline

SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression

• Generate descriptive statistics and explore data with graphs
• Perform analysis of variance
• Perform linear regression and assess the assumptions
• Use diagnostic statistics to identify potential outliers in multiple regression
• Use chi-square statistics to detect associations among categorical variables
• Fit a multiple logistic regression model.

Applied Analytics Using SAS Enterprise Miner

• Define a SAS Enterprise Miner project and explore data graphically
• Modify data for better analysis results
• Build and understand predictive models such as decision trees and regression models
• Compare and explain complex models
• Generate and use score code
• Apply association and sequence discovery to transaction data.

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