SAS/STAT
| Title | Level | Training Formats |
|---|---|---|
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression 
This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum. A more advanced treatment of ANOVA and regression occurs in the Statistics 2: ANOVA and Regression course. A more advanced treatment of logistic regression occurs in the Categorical Data Analysis Using Logistic Regression course and the Predictive Modeling Using Logistic Regression course. |
2 Fundamental |     |
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SAS Programming for R Users
This course is for experienced R users who want to apply their existing skills and extend them to the SAS environment. Emphasis is placed on programming and not statistical theory or interpretation. Students in this course should have knowledge of plotting, manipulating data, iterative processing, creating functions, applying functions, linear models, generalized linear models, mixed models, stepwise model selection, matrix algebra, and statistical simulations. |
3 Intermediate | |
Imputation Techniques in SAS  
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3 Intermediate | |
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Electric Load Forecasting: Fundamentals and Best Practices
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3 Intermediate | |
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Social Network Analysis for Business Applications
Go beyond the traditional clustering and predictive models to identify patterns in your business data. Social network analysis describes customers' behavior, but not in terms of their individual attributes. Rather than basing models on static individual profiles, social network analysis depicts behavior in terms of how individuals relate to each other. In practical terms this approach highlights connections between individuals and organizations and how important they might be in viral effect throughout communities and particular groups. For business purposes, social network analysis can be employed to avoid churn, diffuse products and services, and detect fraud and abuse, among many other applications. This course shows you how to build networks from raw data and presents different approaches for analyzing your customers, focusing on their relationships and connections within the network. Based on the recognition of customers', or organizations', roles within communities or special groups, you can improve business performance and better understand how your customers are using products and services. In addition to the network analysis approach to linking distinct entities, playing different roles on particular connections, this course also shows you a set of network optimization algorithms that you can use to solve a variety of complex business problems. Methods such as minimum-cost network flow, shortest path, linear assignment, minimum spanning tree, eigenvector, and transitive closure are presented in a business perspective for problem solving. This course contains practical examples based on SAS Social Network Analysis Server and PROC OPTGRAPH. |
3 Intermediate | |
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SAS Data Integration Studio 2: Additional Topics
This course expands on the knowledge learned in SAS Data Integration Studio: Essentials and provides additional information on setting up change management, working with slowly changing dimensions, working with the Loop transformations, and defining new transformations. |
3 Intermediate | |
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Explaining Analytics to Decision Makers: Insights to Action
Success in analytics means getting your work applied. Getting your work applied means getting your work understood. Getting your work understood means using a different set of tools than those you used to develop your work. This course discusses the major impediments to the effective communication of analytics and presents solutions. You will learn a variety of approaches including visualizations, foreshadowing, messaging, interpersonal communications, presentations, and most importantly, understanding your audience and adapting your message to the audience. Each approach is explored and the role in the whole of the communication process is considered. A framework is presented to help think through the process. An individual can use this framework to plan personal communication efforts. An organization can use this framework to develop an expectation of communication for all levels of the organization. |
3 Intermediate | |
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Conjoint Analysis: Evaluating Consumer Preferences Using SAS Software
This course discusses a method in marketing research called conjoint analysis that is used to analyze consumer preferences for products and services.The e-learning version of this course includes data so that you can practice the software demonstration steps in your own SAS environment. |
3 Intermediate | |
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Discrete Choice Modeling Using SAS Software
This marketing research course shows how to design a discrete choice experiment and how to analyze discrete choice data in SAS software. Analytical advice regarding number of choice sets, the number of alternatives, and number of subjects is also given. This course includes practice data and exercises. |
3 Intermediate | |
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Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models
This course introduces some methods commonly used in program evaluation and real-world effectiveness studies, including two-stage modeling, interrupted time-series, regression discontinuity, and propensity score matching. These methods help address questions such as: Which medicine is more effective in the real world? Did an advertising program have an impact on sales? More generally, are the changes in outcomes causally related to the program being run? |
3 Intermediate | |
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Data Science: Building Recommender Systems with SAS and Hadoop
This introduction to working with Apache Hadoop is designed for statisticians, as well as experienced SAS programmers with a background in statistics. The course is problem-driven and focuses on helping you understand what data scientists do, the problems they solve, and their methods. By taking a practical approach to the subject, including multiple hands-on exercises, you will leave class with skills that you can immediately apply to real-world problems. You also learn how recommender systems can be leveraged in industries such as Health Care, Finance, and Telecom. |
4 Expert | |
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Electric Load Forecasting: Advanced Topics and Case Studies
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4 Expert | |
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Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS
This course teaches how to identify complex and dynamic patterns within multilevel data to inform a variety of decision-making needs. The course provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM) and their appropriate use in a variety of settings. |
4 Expert | |
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Robust Regression Techniques in SAS/STAT
This course is designed for analysts, statisticians, modelers, and other professionals who have experience and knowledge in regression analysis and who want to learn available procedures in SAS/STAT software for robust regression. The two procedures addressed in the course are the ROBUSTREG procedure and the QUANTREG procedure. This course includes practice data. |
4 Expert | |
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Net Lift Models: Optimizing the Impact of Your Marketing Efforts
The true effectiveness of a marketing campaign is not the response rate; it is the incremental impact. That is, true effectiveness is additional revenue, directly attributable to the campaign, that would not otherwise have been generated. The problem is that targeting strategies often are not designed to maximize the incremental impact. Typical targeting models are successful at finding clients who are interested in the product, but too often these clients would have bought the product regardless of whether they received a promotion. In such cases, the incremental impact is insignificant, and marketing dollars could have been spent elsewhere. Incremental lift models are designed to maximize incremental impact (that is, the incremental lift over the control group) by targeting the undecided clients who can be motivated by marketing. The self-study e-learning includes:
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4 Expert | |
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FDP - Shaping an Advanced Analytics Curriculum
The course teaches you fundamental concepts and relevant techniques in statistical and analytical domains that are relevant in today's world. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advanced analytics for designing better curriculum and effective pedagogy. |
0 No level | |
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Big Data Challenges and Analysis-Driven Data
This course provides an overview of the challenges with big data and analysis-driven data. |
1 Beginner | |
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Introduction to Statistical Concepts
This course covers basic statistical concepts that are critical for understanding and using statistical methods. This course explains what statistics is and why it is important to understand the characteristics of your data. The information in this course is a prerequisite for many other statistical courses that SAS Education offers. The course is appropriate for Base SAS and SAS Enterprise Guide users. Data, practices, and a case study are included. |
1 Beginner | |
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Programming with SAS/IML Software
This course teaches you how to use the IML procedure via the programming language. You benefit from this course if you plan to use SAS/IML for manipulating matrices, simulating data, writing custom statistical analyses, or working with R. The programs in this course require SAS/IML 12.3 or later to run. The self-study e-learning includes:
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2 Fundamental | |
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Forecasting Using SAS Forecast Server Software
This course prepares you to generate large volumes of forecasts automatically using the SAS Forecast Studio interactive interface. This course includes practice data and exercises. This course supports both the desktop and client/server versions. Additional topics for students that license the client/server version of SAS Forecast Studio include producing reports using sample stored processes and a demonstration of SAS Time Series Studio. The self-study e-learning includes:
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2 Fundamental | |
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Using SAS Forecast Server Procedures
This course teaches you how to create and manage a complete forecasting system using the SAS Forecast Server procedures, giving you the power to confidently plan your business operations. |
3 Intermediate | |
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Building and Solving Optimization Models with SAS/OR
This course focuses on formulating and solving mathematical optimization models using the OPTMODEL procedure, from inputting data to interpreting output and generating reports. The course covers linear, integer, mixed integer, and nonlinear programming problems, with an emphasis on model formulation and construction. |
3 Intermediate | |
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Survival Analysis Using the Proportional Hazards Model
This course discusses survival analysis concepts with an emphasis on health care problems. The course focuses on the Cox proportional hazards model, not the parametric models, and is not designed for predictive modelers. |
3 Intermediate | |
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Fitting Poisson Regression Models Using the GENMOD Procedure
This course is for those who analyze the number of occurrences of an event or the rate of occurrence of an event as a function of some predictor variables. For example, the rate of insurance claims, colony counts for bacteria or viruses, the number of equipment failures, and the incidence of disease can be modeled using Poisson regression models. This course includes practice data and exercises. |
3 Intermediate | |
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Determining Power and Sample Size Using SAS/STAT Software
This course teaches you how to use the POWER and GLMPOWER procedures to compute prospective power and sample size calculations. |
3 Intermediate | |
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Statistical Process Control Using SAS/QC Software
This course is designed for professionals who use quality control or SPC methods to monitor, evaluate, and improve the quality of their processes. It is an ideal statistical training module to complement or supplement corporate quality training programs and Six Sigma programs. The self-study e-learning includes:
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3 Intermediate | |
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Statistics 2: ANOVA and Regression
This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course. |
3 Intermediate | |
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Managing SAS Analytical Models Using SAS Model Manager Version 14.2
This course focuses on the following key areas: managing SAS Model Manager data sources, creating a SAS Model Manager project, importing models into SAS Model Manager, using the SAS Model Manager Query Utility, creating scoring tasks, exporting models and projects into a SAS repository, and creating and configuring version life cycles. The course also covers generating SAS Model Manager model comparison reports, publishing and deploying SAS Model Manager models, creating SAS Model Manager production model monitoring reports, and creating user-defined reports. The self-study e-learning includes:
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3 Intermediate | |
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Categorical Data Analysis Using Logistic Regression
This course focuses on analyzing categorical response data in scientific fields. The SAS/STAT procedures addressed are PROC FREQ, PROC LOGISTIC, PROC VARCLUS, and PROC GENMOD. The ODS Statistical Graphics procedures used are PROC SGPLOT and PROC SGPANEL. The course is not designed for predictive modelers in business fields, although predictive modelers can benefit from the content of this course. |
3 Intermediate | |
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SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression
This course is designed for SAS Enterprise Guide users who want to perform statistical analyses. The course is written for SAS Enterprise Guide 8 along with SAS 9.4, but students with previous SAS Enterprise Guide versions will also get value from this course. An e-learning course is also available for earlier versions. |
3 Intermediate | |
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Statistical Analysis with the GLIMMIX Procedure
This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models. |
4 Expert | |
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Predictive Modeling Using SAS High-Performance Analytics Procedures
This course introduces the functionality in the SAS High-Performance Statistics and Data Mining procedures for predictive modeling. The course shows examples of working with SAS High-Performance procedures in a single-machine mode and in distributed mode. |
4 Expert | |
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Multivariate Statistics for Understanding Complex Data
This course teaches how to apply and interpret a variety of multivariate statistical methods to research and business data. The course emphasizes understanding the results of the analysis and presenting your conclusions with graphs. |
4 Expert | |
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Longitudinal Data Analysis with Discrete and Continuous Responses
This course is for scientists and analysts who want to analyze observational data collected over time. It is not for SAS users who have collected data in a complicated experimental design. They should take the Mixed Models Analyses Using SAS course instead. |
4 Expert | |
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Probability Surveys 1: Design, Descriptive Statistics, and Analysis
This course focuses on designing business and household surveys and analyzing data collected under complex survey designs. The course addresses the SAS procedures POWER, SURVEYSELECT, SURVEYMEANS, SURVEYFREQ, SURVEYREG, SURVEYLOGISTIC, and SURVEYIMPUTE. In addition, the graphing procedures GPLOT, SGPLOT, and SGPANEL are also covered. |
4 Expert | |
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Bayesian Analyses Using SAS
The course focuses on Bayesian analyses using the PHREG, GENMOD, and MCMC procedures. The examples include logistic regression, Cox proportional hazards model, general linear mixed model, zero-inflated Poisson model, and data containing missing values. A Bayesian analysis of a crossover design and a meta-analysis are also shown. |
4 Expert | |
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Applied Clustering Techniques
The course looks at the theoretical and practical implications of a wide array of clustering techniques that are currently available in SAS. The techniques considered include cluster preprocessing, variable clustering, k-means clustering, and hierarchical clustering. |
4 Expert | |
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Predictive Modeling Using Logistic Regression
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. |
4 Expert | |
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Mixed Models Analyses Using SAS
This course teaches you how to analyze linear mixed models using the MIXED procedure. A brief introduction to analyzing generalized linear mixed models using the GLIMMIX procedure is also included. |
4 Expert | |
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Feature Engineering and Data Preparation for Analytics
This course introduces programming techniques to craft and feature engineer meaningful inputs to improve predictive modeling performance. In addition, this course provides strategies to preemptively spot and avoid common pitfalls that compromise the integrity of the data being used to build a predictive model. This course relies heavily on SAS programming techniques to accomplish the desired objectives. The self-study e-learning includes:
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4 Expert | |