Become a SAS® Certified Advanced Analytics Professional.
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.
Course content is designed to prepare you for the certification exams.
Real-world case studies enable you to apply what you have learned.
Pass all three exams to earn your certification credential.
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:
The Advanced Analytics Certification program includes three learning modules, comprising a total of 9 courses.
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).
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.
This course explores predictive modeling using SAS/STAT® software, with an emphasis on the LOGISTIC procedure.
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.
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.
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.
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).
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.
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.
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Learn to manage big data, focusing on data quality and visual data exploration for advanced analytics, plus communication skills.Select
Learn analytical modeling, machine learning, experimentation, forecasting and optimization.Select
Learn it all. This program includes all coursework from both the big data and advanced analytics programs.Select
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