Logistic Regression using SAS - Indepth Predictive Modeling
4.3 (823 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,953 students enrolled

Logistic Regression using SAS - Indepth Predictive Modeling

Analytics /Machine Learning / Data Science: Statistical / Econometrics foundation, SAS Program details, Modeling demo
Bestseller
4.3 (823 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,955 students enrolled
Last updated 9/2019
English
English
Current price: $48.99 Original price: $69.99 Discount: 30% off
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This course includes
  • 13.5 hours on-demand video
  • 13 articles
  • 33 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn model development
  • Understand the science behind model development
  • Understand the SAS program required for various steps
  • Get comfortable with interpretation of SAS program output
  • See the step by step model development
Requirements
  • Basic knowledge of SAS
Description

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

  • How to clarify objective and ensure data sufficiency?
  • How do you decide the performance window?
  • How do you perform data treatment
  • How to go for variable selection? How to deal with numeric variables and character variables?
  • How do you treat multi collinerity scientifically?
  • How do you understand the strength of your model?
  • How do you validate your model?
  • How do you interpret SAS output and develop next SAS code accordingly?
  • Step by step workout - model development on an example data set

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

  • Get crystal clear understanding
  • Get jobs in this kind of work by clearing interview with confidence
  • Be successful at their statistical or analytical profession due to the quality output they produce
Who this course is for:
  • Students
  • Analysts / Analytics professional
  • Modelers / Statisticians
Course content
Expand all 109 lectures 17:54:18
+ Introduction to Credit Scoring / Credit Score card development
9 lectures 01:11:29
3C Concept of Credit Approval Process
15:31
High Level Understanding of Score
09:31
Benefit of scoring (modelling)
20:25
Introduction to modeling
07:09
A typical risk score
04:16
Introduction to Scoring FAQ
00:27
Section PDF
20 pages
+ Data Design for Modelling
7 lectures 58:43
Model Design Example
17:54
Model Design - definitions and pointers
13:19
Model Design Precaution
08:18
FAQ : for model design section
01:37
Section PDF
20 pages
+ Data Audit - Make sure to check that data is right for the modelling
18 lectures 02:02:17
Essential Data Quality
03:45
Getting free access to SAS
10 pages
If by chance: you are uncomfortable with SAS?
00:28
How to download excel / SAS code / word document etc.
00:06

Download Excel and word file for the explanation provided as part of feel the data section

Preview 09:02
Feel the data - View it's contents
09:29
Feel the data - know it's distinct values
09:02
Feel the data - know it's distribution
13:27
Feel the data - Understand Coefficient of variance (need and applicability)
08:16
Feel the data - know kurtosis and skewness
04:47
Feel the data - know the percentile
11:21
Feel the data - Understand box plot to detect outliers
06:15
Feel the data - Understand and interpret normal probability plot
22:27
Missing Value treatment And Flooring / Capping Guidiline
13:49
Section FAQ- for variable treatment
00:28
Check basic understanding of model design
10 questions
Check basic understanding of data audit
8 questions
Section PDF
31 pages
+ Variable Selection - Select important numeric and character variables
18 lectures 03:06:29
Variable Selection - High level and flow chart of steps
13:04

Please download the excel for better understanding!

Understand Chi-Square statistics for selecting Important Categorical Variables
19:52
Getting Chi-Square statistics using SAS
08:36

Please download two excel and the word document for model / data work out

Data Workout - Preamble
11:02
Model Workout - 01 Data Treatment
34:52
Numeric Variable Selection - Part 01
10:43
SAS Macro to check directional sense of numeric variable
14:58
Dealing with Independent date variables (date variables as Xs)
00:47
Recap Linear Regression
04:08
Introduction to Logistic Regression
11:57
Theory and Example of Step wise selection of Numeric Variable
19:53
Appendix - Fisher's linear discriminant function to select important numeric Var
09:23
Appendix - Information Value method of selecting important variables (all types)
10:07
Appendix -Phi Square and Cramer's V for important categorical variable selection
06:59
Section FAQ - for variable selection
01:01
Section PDF
64 pages
+ Multi Collinearity Treatment
9 lectures 01:09:12
Common Sense Understanding of Multi collinearity and it's impact
07:02
Detecting Multi Collinearity
10:10
Multi Collinearity Treatment - part 01
19:20
Multi Collinearity Treatment - part 02
05:58
Model Data workout - 02 Bi Variate strength of variables
09:41
Model Data workout - 03 Multi Collinearity Treatment (Scientifically)
12:36
FAQ for multi collinearity section
01:13
Section PDF
24 pages
+ Iterate for final model / Understand strength of the model
15 lectures 01:51:27
Introduction to final model development steps
04:07
Logistic Model Information - part 01
05:53
Logistic Model Information - part 02
04:48
Model Fit Statistics
02:58
Log Likelihood
15:12
Log Likelihood ratio - part 01
06:28
Log Likelihood Ratio - part 02
03:29
Model Fit Statistics - Revisit
13:23
Maximum Likelihood Estimate
12:44
Ideal logistic regression output
04:17
Model Data Workout - part 04 Try Model on 10 variables
06:50
Model Data Workout - part 05 Select best 8 variables
09:26
Section PDF
39 pages
+ Strength of a Model and Model Validation Methods
10 lectures 01:16:24
Model Data Workout - part 06 Coefficient Stability Check
11:28
Understand Score and Generate Score in the data set
12:21
Model Data Workout - part 08 Generate KS Statistics for the model
20:51
Model Data Workout - part 09 Understand and Generate Gini Statistics
11:30
Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk
07:56
FAQ - for strength of the model section
00:58
Model Presentation Guideline - What should be presented to business
05:00
Section PDF
25 pages
+ Reject Inference - Developing application score on scored population
6 lectures 35:43
Introduction to reject inference! What it is? Why it is needed?
11:21
How to do reject inference?
08:40
Swapset analysis supplementary video
03:07
Do you need reject inference all the time?
03:49
+ Appendix Topics (It will have contents based on student's demands)
12 lectures 01:25:00
K fold validation using simple SAS macro
09:52
FAQ by students of this course (will keep growing overtime)
13:15
Introduction to Multinomial Logistic Regression and solution approach
10:06
Ordinal Logistic Regression and Proportional Odds assumption
08:14
Demo of Ordinal Logistic Regression using SAS
10:37
Count Data Model - Poisson Regression
00:35
Bonus Topic - how to learn Predictive Modeling / Logistic Regression with R
01:06
Final Words
01:59