OEM Name: SAS India Pvt Ltd
Technologies: Data Analytics
-
Expert
Course level -
35 Hrs
Duration -
₹ 60000
Course fee Excluding GST -
Installments Allowed
Fee payment
-
Senior Secondary
Min. qualification -
N/a
Min. academic % -
18 yrs
Min. age -
Provided
Placement assistance
Description & features
Applied Analytics Using SAS Enterprise Miner, Predictive Modeling Using Logistic Regression
Key Features
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.
•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.Global exam aligned with this course: SAS Certified Statistical Business Analyst : Regression and Modelling
Curriculum
Course Prerequisite:
Basic technical user skills with statistics
Career Progression:
SAS Certified Statistical Business Analyst : Regression and Modeling
Course Content
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.
Documents
