This course is part of the SAS Statistical Business Analyst Professional Certificate

4.6
stars
42 ratings

Michael J Patetta +1 more instructor

4,452 already enrolled

Offered By

SAS Statistical Business Analyst Professional CertificateSAS

About this Course

14,195 recent views

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 3 in the

SAS Statistical Business Analyst Professional Certificate

Intermediate Level

Approx. 16 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Skills you will gain

Oversampling
Logistic Regression
Predictive Modelling
regression

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 3 in the

SAS Statistical Business Analyst Professional Certificate

Intermediate Level

Approx. 16 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.53/5 (13 Ratings)

Michael J Patetta

Principal Analytical Training Consultant

Education

4,452 Learners

1 Course

Marc Huber

Sr Analytical Training Consultant

4,629 Learners

2 Courses

Offered by

SAS

Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Syllabus - What you will learn from this course

Week1

Week 1

1 hour to complete

Course Overview and Logistics

1 hour to complete

1 video (Total 1 min), 6 readings

2 hours to complete

Understanding Predictive Modeling

2 hours to complete

15 videos (Total 34 min), 1 reading, 6 quizzes

15 videos

Overview53sIntroduction19sGoals of Predictive Modeling1mTerms for Elements in Predictive Modeling49sBasic Steps of Predictive Modeling2mApplications of Predictive Modeling1mDemonstration Scenario: Target Marketing for a Bank1mDemo: Examining the Code for Generating Descriptive Statistics and Frequency Tables2mIntroduction21sData Challenges6mAnalytical Challenges2mSeparate Sampling1mAvoiding the Optimism Bias: Honest Assessment2mSplitting the Data for Model Training and Assessment3mDemo: Splitting the Data5m

1 reading

Summary10m

6 practice exercises

Practice: Exploring the Bank Data for the Target Marketing Project20mPractice: Exploring the Veterans' Organization Data Used in the Practices20mQuestion 1.015mQuestion 1.025mQuestion 1.035mPractice: Splitting the Data20m

Week2

Week 2

2 hours to complete

Fitting the Model

2 hours to complete

18 videos (Total 54 min), 1 reading, 4 quizzes

18 videos

Overview54sIntroduction22sUnderstanding the Logistic Regression Model2mConstraining the Posterior Probability Using the Logit Transformation1mUnderstanding the Fitted Surface1mInterpreting the Model by Calculating the Odds Ratio3mUnderstanding Logistic Discrimination1mEstimating Unknown Parameters Using Maximum Likelihood Estimation2mInterpreting Concordant, Discordant, and Tied Pairs1mUsing PROC LOGISTIC to Fit Logistic Regression Models24sDemo: Fitting a Basic Logistic Regression Model, Part 18mDemo: Fitting a Basic Logistic Regression Model, Part 212mScoring New Cases26sDemo: Scoring New Cases7mIntroduction16sUnderstanding the Effect of Oversampling53sUnderstanding the Offset1mDemo: Correcting for Oversampling6m

1 reading

Summary10m

4 practice exercises

Question 2.015mQuestion 2.025mPractice: Fitting a Logistic Regression Model20mFitting the Model Review30m

Week3

Week 3

3 hours to complete

Preparing the Input Variables, Part 1

3 hours to complete

26 videos (Total 76 min)

26 videos

Overview34sIntroduction22sReasons for Missing Data2mComplete Case Analysis1mMethods for Imputing Missing Values2mMissing Value Imputation with Missing Value Indicator Variables3mDemo: Imputing Missing Values4mCluster Imputation1mIntroduction25sProblems Caused by Categorical Inputs4mSolutions to Problems Caused by Categorical Inputs39sLinking to Other Data Sets56sCollapsing Categories by Thresholding53sCollapsing Categories by Using Greenacre's Method3mDemo: Collapsing the Levels of a Nominal Input, Part 16mDemo: Collapsing the Levels of a Nominal Input, Part 210mReplacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding2mDemo: Computing the Smoothed Weight of Evidence4mIntroduction20sProblem of Redundancy2mVariable Clustering Method1mUnderstanding Principal Components5mDivisive Clustering3mPROC VARCLUS Syntax1mSelecting a Representative Variable from Each Cluster1mDemo: Reducing Redundancy by Clustering Variables8m

9 practice exercises

Question 3.015mPractice: Imputing Missing Values20mQuestion 3.025mQuestion 3.035mQuestion 3.045mPractice: Collapsing the Levels of a Nominal Input20mPractice: Computing the Smoothed Weight of Evidence20mQuestion 3.055mPractice: Reducing Redundancy by Clustering Variables20m

Week4

Week 4

4 hours to complete

Preparing the Input Variables, Part 2

4 hours to complete

23 videos (Total 92 min), 1 reading, 12 quizzes

23 videos

Introduction28sDetecting Nonlinear Relationships4mDemo: Performing Variable Screening, Part 15mDemo: Performing Variable Screening, Part 24mUnivariate Binning and Smoothing2mDemo: Creating Empirical Logit Plots10mRemedies for Nonlinear Relationships2mDemo: Accommodating a Nonlinear Relationship, Part 16mDemo: Accommodating a Nonlinear Relationship, Part 27mIntroduction26sSpecifying a Subset Selection Method in PROC LOGISTIC1mBest-Subsets Selection54sStepwise Selection2mBackward Elimination1mScalability of the Subset Selection Methods in PROC LOGISTIC2mDetecting Interactions2mBIC-based Significance Level2mDemo: Detecting Interactions7mDemo: Using Backward Elimination to Subset the Variables4mDemo: Displaying Odds Ratios for Variables Involved in Interactions3mDemo: Creating an Interaction Plot3mDemo: Using the Best-Subsets Selection Method3mDemo: Using Fit Statistics to Select a Model9m

1 reading

Summary of Preparing the Input Variables, Parts 1 and 210m

12 practice exercises

Question 3.065mPractice: Performing Variable Screening20mPractice: Creating Empirical Logit Plots20mQuestion 3.075mQuestion 3.085mQuestion 3.095mPractice: Using Forward Selection to Detect Interactions20mQuestion 3.105mPractice: Using Backward Elimination to Subset the Variables20mQuestion 3.115mPractice: Using Fit Statistics to Select a Model20mPreparing the Input Variables Review30m

Reviews

4.6

12 reviews

5 stars
81.39%
4 stars
9.30%
2 stars
4.65%
1 star
4.65%

TOP REVIEWS FROM PREDICTIVE MODELING WITH LOGISTIC REGRESSION USING SAS

by SSApr 10, 2021

Great training sets of problems. Good guidance & teaching.

by RMJun 14, 2021

Thank you so much to the instructor, Michael J Patetta for teaching this course!

by MCDec 30, 2022

Very completed and deep knowledge shared with very friendly ways, explained the knowledge very clearly. Also the practices help me to understand the knowledge better.

by TWJul 28, 2021

This was another great course from SAS and Coursera. I had no experience with predictive modelling prior to the course and learned quite a bit about modelling in the SAS environment.

View all reviews

About the SAS Statistical Business Analyst Professional Certificate

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.

Build employee skills, drive business results

Discover Coursera for Business

Predictive Modeling with Logistic Regression using SAS

About this Course

Skills you will gain