Predictive Analytics And Modeling For Decision Making

Learn how to build Predictive Models using Numerical Data using tools such as SAS

What you'll learn
It covers topics such as PM SAS EM Introduction, PM SAS EM variable selection, SAS PM EM combination, SAS PM EM neural network, and SAS PM EM regression.
It starts with an introduction to SAS and then gradually move towards topics such as selecting SAS tables, creating input data nodes
decision tree in SAS, creating score model, ROC chart, Neural network training, regression -table effect to name a few.
we provide more details on the predictive modeling course content and explain at a very high-level what concepts will be covered under course
Requirements
There are some pre-requisites for this predictive modeling training course that must be fulfilled otherwise the understanding, of course, could become difficult for some students. Do not worry, the pre-requisite is not very difficult and almost anyone can qualify for that. If not, you can enroll for a bridge course or learn the pre-requisite first and then enroll for this predictive modeling course
Description
Predictive 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 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 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.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 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 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.

Overview

Section 1: SAS - Predictive Modeling with SAS Enterprise Miner

Lecture 1 Introduction of SAS Enterprise Miner

Lecture 2 Select a SAS Table

Lecture 3 Creating Input Data Node

Lecture 4 Metadata Advisor Options

Lecture 5 Add More Data Sources

Lecture 6 Sample Statistics

Lecture 7 Trial report

Lecture 8 Properties of Cluster Node

Lecture 9 Variable Selection

Lecture 10 Input Variable

Lecture 11 Input Variable Continues

Lecture 12 Values of R-Square

Lecture 13 More on Variable Selection

Lecture 14 Binary Target Variable

Lecture 15 Variable and Effect Summary

Lecture 16 Variable Selection - Variable ID's

Lecture 17 Variable Frequency Table

Lecture 18 Variable S - Updating Model Comparison

Lecture 19 Run Data Partition Node

Lecture 20 Variable Selection - Fit Statistics

Lecture 21 Understanding Transformation of Variables

Lecture 22 Score Ranking Overlay Res

Lecture 23 Update Transformation of Variables

Lecture 24 Combination of Different Models

Lecture 25 Properties of Neural Network

Lecture 26 Analyzing the Output Variable

Lecture 27 Combination of Regression Model

Lecture 28 Combination - Result of Regression Node

Lecture 29 Combination Iteration Plot

Lecture 30 Subseries Plot

Lecture 31 Creating Densemble Diagram

Lecture 32 SAS Code

Lecture 33 Decision Tree Model

Lecture 34 Run and Upadate Decision Tree Model

Lecture 35 Creating Dscore Node

Lecture 36 DT - Resulf of Model Comparison

Lecture 37 Leaf Statistics and Tree Map

Lecture 38 Interactively Decision Trees

Lecture 39 Result Node Data Partition

Lecture 40 Interactively Trees Window

Lecture 41 Building a Decision Trees

Lecture 42 Neural Network Model

Lecture 43 Neural Network Model Output

Lecture 44 Model Weight History

Lecture 45 Neural Network - Final Weight

Lecture 46 ROC Chart

Lecture 47 Neural Network -Iteration Plot

Lecture 48 Neural Network - SAS Code

Lecture 49 Neural Network - Cumulative Lift

Lecture 50 Decision Processing

Lecture 51 Results of Auto Neural Node

Lecture 52 Run Model Comparison

Lecture 53 DEX - Variable ID's

Lecture 54 Average Square Error

Lecture 55 Score Rating overlay - Event

Lecture 56 Run Dmine Regression Node

Lecture 57 Regression with Binary Target

Lecture 58 Regression - Table Effect Plots

Lecture 59 Result of Regression Model

Lecture 60 Update Regression Node

Lecture 61 Creating Flow Diagram

Students from technical or computer science fields are highly welcome, similarly, those from mathematics or statistics background is highly suitable. Most commonly students have a degree in B. Tech / BCA/ B. Sc./ MCA/ M. Sc/ M. Tech or MBA degree. Entry-level working professionals from the software field, banking, insurance, share market, information technologies who want to migrate to data analysis are also very suitable and they comprise a major chunk of our class size. The predictive modeling course is also suitable for managers and seasoned industry professionals who want to be a consultant or data scientist.