sas wr problem For WR 3, visit the SAS Global Certification center at https://su

Question

sas wr problem

For WR 3, visit the SAS Global Certification center at

https://support.sas.com/certify/creds/ct.html

WR 3) Using the certification center website, answer the following:

- Which SAS credential recommends that candidates have experience with, linear regression, logistic regression, and measuring model performance.

- What else does it recommend?

- How many objectives are there in the expanded list of things to be able to do?

- Who administers this e x a m (besides SAS), and what is the e x a m I D?

Explanation / Answer

Advanced Analytics Certification

It covers the following

Prerequisite Skills

To enroll in the program, you need at least six months of programming experience in SAS or another programming language. We also recommend that you have at least six months of experience using mathematics and/or statistics in a business environment. If you're just getting started or need to brush up on your skills, we recommend:

Statistics 1: Introduction to ANOVA, Regression or Logistic Regression – available as an instructor-led course or free online e-learning course.

The exhaustive list includes

Module 1: Predictive Modeling

Course 1: Applied Analytics Using SAS Enterprise Miner

This course covers the skills required to assemble analysis flow diagrams using SAS Enterprise Miner for both pattern discovery (segmentation, association and sequence analyses) and predictive modeling (decision trees, regression and neural network models).

Topics Covered

Module 1 prepares you for the Predictive Modeling certification exam.

Module 2: Advanced Predictive Modeling

Course 1: Neural Network Modeling

This course helps you understand and apply two popular artificial neural network algorithms – multilayer perceptrons and radial basis functions. Both the theoretical and practical issues of fitting neural networks are covered.

Topics Covered

Course 2: Predictive Modeling Using Logistic Regression

This course explores predictive modeling using SAS/STAT® software, with an emphasis on the LOGISTIC procedure.

Topics Covered

Course 3: Data Mining Techniques: Predictive Analytics on Big Data

This course introduces applications and techniques for assaying and modeling large data. It presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models and mixture distribution models. You will perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics and SAS In-Memory Statistics.

Topics Covered

Course 4: Using SAS to Put Open Source Models Into Production

This course introduces the basics for integrating R programming and Python scripts into SAS and SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data exploration, model prototyping, and supervised and unsupervised learning techniques.

Topics Covered

Module 2 prepares you for the Advanced Predictive Modeling certification exam.

Module 3: Text Analytics, Time Series, Experimentation and Optimization

Course 1: Text Analytics Using SAS Text Miner

Course 2: Time Series Modeling Essentials

Course 3: Experimentation in Data Science

Course 4: Optimization Concepts for Data Science

It is administered by SAS academy

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