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What is this course all about?
This course is all about credit scoring / logistic regression model building using SAS. It explains
There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. Some of the discussion item would be
What kind of material is included?
It consists of video recording of screen (audio visual screen capture), pdf of presentations, Excel data for workout, word document containing code and Excel document containing step by step model development workout details
How long the course will take to complete?
Approximately 30 hours
How is the course structured?
It has seven sections, which step by step explains model development
Why Take this course?
The course is more intended towards students / analytics professionals to
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Certificate of completion.
|Section 1: Course Outline|
Introduction to logistic Regression Modelling - High levelPreview
Udemy Content details - Model workout details and excel file downloadsPreview
Tips for Students
Course Content PDF
|Section 2: Introduction to Credit Scoring / Credit Score card development|
3C Concept of Credit Approval Process
High Level Understanding of Score
Benefit of scoring (modelling)
Introduction to modeling
Types of scoresPreview
A typical risk score
|Section 3: Data Design for Modelling|
Model Design Example
Model Design - definitions and pointers
Decide Performance window by Vintage AnalysisPreview
Model Design Precaution
|Section 4: Data Audit - Make sure to check that data is right for the modelling|
Essential Data Quality
Getting free access to SAS
How to download excel / word files ?Preview
Download Excel and word file for the explanation provided as part of feel the data section
Feel the data - View it's contents
Feel the data - know it's distinct values
Feel the data - know it's distribution
Feel the data - Understand Coefficient of variance (need and applicability)
Feel the data - know kurtosis and skewness
Feel the data - know the percentile
Feel the data - know stem n leaf diagramPreview
Feel the data - Understand box plot to detect outliers
Feel the data - Understand and interpret normal probability plot
Missing Value treatment And Flooring / Capping Guidiline
|Section 5: Variable Selection - Select important numeric and character variables|
Variable Selection - High level and flow chart of steps
Important Character / Categorical Variable selection - high levelPreview
Please download the excel for better understanding!
Getting Chi-Square statistics using SAS
Please download two excel and the word document for model / data work out
Model Workout - 01 Data Treatment
Numeric Variable Selection - Part 01
SAS Macro to check directional sense of numeric variable
Recap Linear Regression
Introduction to Logistic Regression
Theory and Example of Step wise selection of Numeric Variable
Appendix - Fisher's linear discriminant function to select important numeric Var
Appendix - Information Value method of selecting important variables (all types)
Appendix -Phi Square and Cramer's V for important categorical variable selection
|Section 6: Multi Collinearity Treatment|
Common Sense Understanding of Multi collinearity and it's impact
Detecting Multi Collinearity
Multi Collinearity Treatment - part 01
Multi Collinearity Treatment - part 02
Model Data workout - 02 Bi Variate strength of variables
Model Data workout - 03 Multi Collinearity Treatment (Scientifically)
|Section 7: Iterate for final model / Understand strength of the model|
Introduction to final model development steps
Logistic Model Information - part 01
Logistic Model Information - part 02
Model Fit Statistics
Log Likelihood ratio - part 01
Log Likelihood Ratio - part 02
Model Fit Statistics - Revisit
Maximum Likelihood Estimate
Concordance, Somer's D, Gamma, Tau etc.Preview
Ideal logistic regression output
Model Data Workout - part 04 Try Model on 10 variables
Model Data Workout - part 05 Select best 8 variables
|Section 8: Strength of a Model and Model Validation Methods|
Model Data Workout - part 06 Coefficient Stability Check
Understand Score and Generate Score in the data set
Theoretical Understanding of KSPreview
Model Data Workout - part 08 Generate KS Statistics for the model
Model Data Workout - part 09 Understand and Generate Gini Statistics
Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk
Model Presentation Guideline - What should be presented to business
How to download excel / word files ?
|Section 9: Reject Inference - Developing application score on scored population|
Introduction to reject inference! What it is? Why it is needed?
How to do reject inference?
Impact of the new model - swapset analysis / more base with same approval ratePreview
Swapset analysis supplementary video
Do you need reject inference all the time?
I am a seasoned Analytics professional with 15+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.