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Predictive Analytics & Modeling with SAS
Learn and get expertise on Predictive Analytics & Modeling with SAS

What you will learn

The course helps you with a hand on analytics and to become an expertise in data handling and sas platform

You can learn the regression tool usage with details on regression table, result of the regression model and creating flow diagrams etc

It has helped to build the base for other statistical analysis

Learning hands on Predictive Modelling with SAS Enterprise Miner

Description

Predictive Analytics & Modeling can be understood as the process of creation, test, and validation of a model. It uses concepts from statistics in predicting the outcomes. Predictive Analytics & Modeling contains a different set of methods like machine learning, statistics, artificial intelligence and so on. These models are made up of several predictors, also called attributes that are likely to impact future results. Predictive modeling is currently the most widely used in computer science, information technology, and information services domain.

This Predictive Analytics & Modeling course targets to provide predictive modeling skills as mentioned above to business sectors/domains. Quantitative methods and predictive modeling concepts from this predictive modeling course could be extensively used in many fields to understand the current customer behavior, customer satisfaction, financial market trends, studying effects of medicine in pharma sectors after drugs are developed and administered.

Minitab or SAS and SPSS are among the leading developers in the world towards building statistical analysis software. Across the world, these software’s are used by thousands of companies. These are also used by over 10000 universities and colleges for research and teaching. Some major clients of Minitab, for example, consist of Pfizer, Royal Bank of Scotland, Nestle, Boeing, Toshiba, and DuPont.


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Many independent studies conducted by companies like Mckinsey, Gartner, and others have predicted that data science, machine learning, and predictive modeling is going to be the biggest jobs of the 21st century and these professionals are going to be rewarded the best for it.

This course covers many tangible skills that students can count on for jobs and career switch. These skills are explained here to help students understand the value of this Predictive Analytics & Modeling course.

  • Skill to analyze data and see a complex pattern: data understanding and pattern extraction is a key skill for predictive modeling and a successful person in this domain should be able to make sense of data in no time. In this course, you will learn how to do that. You will be taught various types of data distribution, data patterns, and data understanding techniques. These skills will help you lifelong in making better and more intuitive decisions in all fields of work.
  • Hands-on coding skill: – The Predictive Analytics & Modeling course teaches three tools- Minitab, SAS, and SPSS. For that, this predictive modeling course is quite good. For predictive modeling and machine learning course one needs to be comfortable with coding, and hence having a sharp understanding of practical implementation is very important. This course teaches all these skills so that the student is industry ready and can comfortably work in real-life use cases.
  • Strong understanding of concepts: – Machine learning concepts such as regression, classification, support vector machines, neural network, ROC curve, and many more concepts are taught which are frequently asked in interviews and which judges a candidate’s understanding of predictive modeling.
English
language

Content

PM SAS EM INTRO

Introduction of SAS Enterprise Miner
Select a SAS Table
Creating Input Data Node
Metadata Advisor Options
Add More Data Sources
Sample Statistics
Trial report
Properties of Cluster Node
Variable Selection

PM SAS EM VARIABLE SELECTION

Input Variable
Input Variable Continues
Values of R-Square
More on Variable Selection
Binary Target Variable
Variable and Effect Summary
Variable Selection – Variable ID’s
Variable Frequency Table
Variable S – Updating Model Comparison
Run Data Partition Node
Variable Selection – Fit Statistics
Understanding Transformation of Variables
Score Ranking Overlay Res
Update Transformation of Variables

SAS PM EM COMBINATION

Combination of Different Models
Properties of Neural Network
Analyzing the Output Variable
Combination of Regression Model
Combination – Result of Regression Node
Combination Iteration Plot
Subseries Plot
Creating Densemble Diagram
SAS Code
Decision Tree Model
Run and Upadate Decision Tree Model
Creating Dscore Node
DT – Resulf of Model Comparison
Leaf Statistics and Tree Map
Interactively Decision Trees
Result Node Data Partition
Interactively Trees Window
Building a Decision Trees

SAS PM EM NEURAL NETWORK

Neural Network Model
Neural Network Model Output
Model Weight History
Neural Network – Final Weight
ROC Chart
Neural Network -Iteration Plot
Neural Network – SAS Code
Neural Network – Cumulative Lift
Decision Processing
Results of Auto Neural Node
Run Model Comparison
DEX – Variable ID’s
Average Square Error
Score Rating overlay – Event
Run Dmine Regression Node

SAS PM EM REGRESSION

Regression with Binary Target
Regression – Table Effect Plots
Result of Regression Model
Update Regression Node
Creating Flow Diagram