Predictive Analytics & Modeling With Sas
Published 10/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.32 GB | Duration: 7h 53m

Learn and get expertise on Predictive Analytics & Modeling with SAS

What you'll 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
Requirements
Prior knowledge of Quantitative Methods, MS Office and Data will be useful
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.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.
Overview
Section 1: PM SAS EM INTRO
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
Section 2: PM SAS EM 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
Section 3: SAS PM EM COMBINATION
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
Section 4: SAS PM EM NEURAL NETWORK
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
Section 5: SAS PM EM REGRESSION
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.

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