Advanced Analytics Certification

SAS® Advanced Analytics Certification Curriculum: Expand your analytical skill set. Make yourself more marketable. And become a more valued asset by learning the latest advanced analytics techniques for solving critical business challenges across every domain.

Duration Days
Certificate SAS Global
Language English

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Course Description

Learn How To!

  • Machine learning and predictive modeling techniques.
  • How to apply these techniques to distributed and in-memory big data sets.
  • Pattern detection.
  • Experimentation in business.
  • Optimization techniques.
  • Time series forecasting.

SAS software covered

  • SAS® Enterprise Miner™
  • SAS/ETS®
  • SAS® High-Performance Data Mining
  • SAS® In-Memory Statistics (PROC IMSTAT)
  • SAS® Studio
  • SAS/OR®
  • SAS® Text Miner
  • SAS® Visual Statistics
  • SAS tools for integrating with open source

About Program

SAS® Advanced Analytics Certification Curriculum

  • Covers 9 Courses
  • Case Studies (Real-world case studies enable you to apply what you've learned.)
  • 3 Exams
  • Pass all three exams to earn your certification credential.

Format of Training

Taught by certified instructors at High-Tech facilities across the country:

  • A SAS expert at your side.
  • Focused learning away from the office
  • Networking opportunities
  • State-of-the-art facilities
  • Electronic course notes downloadable to your device and permission to print
  • Business Knowledge Series: in-depth courses on the latest business topics
  • We offer Connected Classes! Watch for courses in Cary, New York, Arlington, Dallas and San Francisco that connect remote students via our Live Web classroom.

Self Paced Learning Platform

SAS e-learning courses are online, hands-on tutorials that you can access whenever and wherever is convenient for you – satisfaction guaranteed. All you need is an internet connection. With e-learning from SAS, you can:

  • Train when and where you want.
  • Learn at your own pace.
  • Access the same content as our instructor-based courses, only optimized for self-study.
  • Earn a certificate of completion.
  • Benefit from courses created by SAS experts.

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Get a quick, easy start with SAS® online training – or expand your learning without spending a dime.


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.

And one of the following:

  • SAS Programming for R Users – available as a free online e-learning course
  • SAS Programming for Data Science Fast Track – four e-learning courses providing a good SAS programming foundation

Training Features

48 hours of Instructor-led Classes. 

Course includes embedded practice content for strenthening programming skills. 

One attempt of Global certification worth $180

The perfect starting point for those interested in a career as a SAS® professional. Successful candidates should have experience in programming and data management using SAS®9.  

Validate your skills. Stand out.

  • Earn recognition for your knowledge.
  • Increase your value to your employer.
  • Enhance your credibility as a SAS professional.
  • Get a digital badge you can share.

Course Curriculum

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).

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.

Course 2: Predictive Modeling Using Logistic Regression

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

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.

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.

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

In this course, you will learn to use SAS Text Miner to uncover underlying themes or concepts contained in large document collections, automatically group documents into topical clusters, classify documents into predefined categories, and integrate text data with structured data to enrich predictive modeling endeavors.

Course 2: Time Series Modeling Essentials

In this course, you'll learn the fundamentals of modeling time series data, with a focus on the applied use of the three main model types for analyzing univariate time series: exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unobserved components (UCM).

Course 3: Experimentation in Data Science

This course explores the essentials of experimentation in data science, why experiments are central to any data science efforts, and how to design efficient and effective experiments.

Course 4: Optimization Concepts for Data Science

This course focuses on linear, nonlinear and efficiency optimization concepts. Participants will learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. Course demonstrations include examples of data envelopment analysis and portfolio optimization. The OPTMODEL procedure is used to solve optimization problems that reinforce concepts introduced in the course.

Module 3 prepares you for the Text Analytics, Time Series, Experimentation and Optimization certification exam.


Real-world case studies enable you to apply what you have learned.

Success Stories

Course Fees