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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
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
This course helps students learn how basic statistical techniques can be used to better inform strategic business and decision-making challenges. The focus of the course is to learn the key tools and tactics of a corporate strategist/strategic planner. However, woven into the course are key analytics, powered by SAS, that can enable strategists to be more data driven and objective in terms of options, pros/cons, recommendations, and contingency plans.

2 Fundamental
Electric Load Forecasting: Fundamentals and Best Practices

3 Intermediate
Imputation Techniques in SAS
Concentrating on the needs of those relatively new to the use of multiple imputation tools in SAS, this course provides a general introduction to using the MI and MIANALYZE procedures for multiple imputation and subsequent analyses with imputed data sets.

3 Intermediate
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
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
Data Cleaning Techniques
This course, which was completely rewritten to be compatible with the third edition of the book Cody's Data Cleaning Techniques Using SAS, will help greatly speed up the process of detecting and correcting errors in both character and numeric data. In addition, there are sections on standardizing data and using Perl regular expressions to ensure that character values conform to a specific pattern (such as ZIP codes, phone numbers, and email addresses).

Although the course concentrates on methods of identifying data errors, it also teaches some programming techniques that might be new to you. For example, by using some of the latest SAS functions, you can convert a phone number in just about any form into a standard form, in only two SAS statements!

The course teaches several methods of detecting errors in numeric data including range checking as well as several methods of automatic outlier detection. There are chapters devoted to data that involves multiple observations per subject, SAS dates, and projects that include multiple data sets. The class closes with a demonstration of an innovative process that leverages integrity constraints and audit trails to detect and programmatically clean dirty data before it even gets into your analysis data set.

All students taking this class are presented with either a printed version or PDF version of the new Data Cleaning book and are given access to dozens of macros that will greatly speed up the laborious process of cleaning your data.

3 Intermediate
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
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
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
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
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
Survival Data Mining: A Programming Approach
This advanced course discusses predictive hazard modeling for customer history data. Designed for data analysts, the course uses SAS/STAT software to illustrate various survival data mining methods and their practical implementation.

Note: Formerly titled Survival Data Mining: Predictive Hazard Modeling for Customer History Data, this course now includes hands-on exercises so that you can practice the techniques that you learn. Other additions include a chapter on recurrent events, new features in SAS/STAT software, and an expanded section that compares discrete time approach versus the continuous time models such as Cox Proportional Hazards models and fully parametric models such as Weibull.

4 Expert
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
Structural Equation Modeling Using SAS
This course introduces the experienced statistical analyst to structural equation modeling (SEM) and the new PATH language in the CALIS procedure in SAS/STAT software. The course also introduces the PATHDIAGRAM statement in the CALIS procedure, which automatically draws path diagrams based on fitted models.

Structural equation modeling is a statistical technique that combines elements of traditional multivariate models, such as regression analysis, factor analysis, and simultaneous equation modeling. These models are often represented as matrices, equations, and/or path diagrams and can explicitly account for uncertainty in observed variables and for estimation bias due to measurement error. Competing models can be compared to one another, providing information about the complex drivers of the outcome variables of interest. Many applications of SEM can be found in the social, economic, and behavioral sciences, where measurement error and uncertain causal conditions are commonly encountered.

4 Expert
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:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

4 Expert
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
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:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

2 Fundamental
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:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.

2 Fundamental
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
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
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
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
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
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:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.

3 Intermediate
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
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
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:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.

3 Intermediate
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
Predictive Modeling Using SAS High-Performance Analytics Procedures
SAS high-performance procedures provide predictive modeling tools that have been specially developed to take advantage of parallel processing in both multithreaded single-machine mode and distributed multiple-machine mode to solve big data problems. This course gives overview of all SAS High-Performance solutions and specifically introduces the functionality in the SAS High-Performance Statistics and Data Mining procedures for predictive modeling. The course shows examples of applying advanced statistics to huge volumes of data and quickly retrain many predictive modes using all available processing power in a single-machine mode and in distributed mode.

4 Expert
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
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
Longitudinal Data Analysis Using 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
Design of Experiments for Direct Marketing
This course deals with the concepts and techniques that are used in the design and analysis of experiments. The course primarily focuses on direct marketing applications, but it is also relevant for someone interested in designing experiments in the fields of physical, chemical, biological, medical, economic, social, psychological, and industrial sciences; engineering; or agriculture. This course teaches you how to design efficient marketing experiments with more than one factor, analyze the results that your experiments yield, and maximize the information that is gleaned from a marketing campaign. Factorial and fractional factorial designs are discussed in greater detail.

4 Expert
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
Statistical Analysis with the GLIMMIX Procedure
This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models.

4 Expert
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
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:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.

4 Expert
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
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