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.
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SAS® Advanced Analytics Certification Curriculum
Taught by certified instructors at High-Tech facilities across the country:
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:
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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:
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.
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.