SAS Social and Behavioral Research​ Learning Subscription

Kód kurzu: SASSBRLS

The focus of this learning subscription is on learning how to apply various statistical methods, behavioral analytics, and SAS procedures to analyze human behavior and use past actions to make predictions for the future. Demand for these skills continues to grow and appeals to a wide range of organizations from government agencies, marketing, and financial institutions to major corporations.

SAS Products Covered

  • SAS Viya
  • SAS Visual Data Mining and Machine Learning
  • Base SAS
  • None

certifikovaní lektori

uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: Self-Paced

Dĺžka kurzu: 365 dní

Jazyk: en

Cena bez DPH: 625 EUR


Forma Dĺžka
Jazyk Cena bez DPH
Na vyžiadanie Self-Paced 365 dní en 625 EUR Registrovať
G Garantovaný kurz

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Popis kurzu

Learn how to:

  • Apply a variety of multivariate statistical methods to research and business data and interpret the results.
  • Identify complex and dynamic patterns within multilevel data.
  • Analyze linear mixed models using SAS procedures.
  • Analyze observational data collected over time.
  • Apply SAS procedures for fitting statistical models.
  • Analyze data collected from complex probability surveys.
  • Apply structural equation modeling (SEM) to instances in fields where measurement error, latent variables, and uncertain causal conditions are commonly encountered like social, economic, and behavioral sciences.
  • Understand the results of your analysis and present your conclusions.

Cieľová skupina

For anyone who wants to gain behavioral analytics skills using SAS that can be applied in the areas of social and behavioral science disciplines like sociology, psychology, education, public policy, marketing, applied behavioral science, and social science.

Štruktúra kurzu

Includes the following on-demand courses:

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.

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.

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.

The self-study e-learning includes:

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

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 the SAS System course instead.

The self-study e-learning includes:

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

Statistical Analysis with the GLIMMIX Procedure

This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models.

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.

Structural Equation Modeling Using SAS

This course introduces the experienced statistical analyst to structural equation modeling (SEM) in the CALIS procedure in SAS/STAT software. The course also introduces the PATHDIAGRAM statement in the CALIS procedure, which 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. This course does not address models containing categorical endogenous variables or multilevel SEM, as these methods are not supported in the CALIS procedure.

Using SAS Viya REST APIs with Python and R

In this course, you learn to use the R and Python APIs to take control of SAS Cloud Analytic Services (CAS) and submit actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models on CAS using familiar open-source functionality via the SWAT (SAS Wrapper for Analytics Transfer) package.

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.

Responsible Innovation and Trustworthy AI

This course is designed for anyone who wants to gain a deeper understanding about the importance of trust and responsibility in AI, analytics, and innovation. The content is especially geared to those who are making business decisions based on machine learning and AI systems and those who are designing and training AI systems.

Whether you are a programmer, an executive, an advisory board member, a tester, a manager, or an individual contributor, this course helps you gain foundational knowledge and skills to consider the issues related to responsible innovation and trustworthy AI. Empowered with the knowledge from this course, you can strive to find ways to design, develop, and use machine learning and AI systems more responsibly.

This course will be released several modules at a time until all modules are available. We expect that each module can be completed in under an hour, and you can work at your own pace to complete the material. As we release new modules, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.

Predpokladané znalosti

Before taking these courses, it is recommended that you take the SAS Programming 1: Essentials and Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression courses, which are available in instructor-led or free online e-learning formats. The Statistics 2: ANOVA and Regression course or equivalent knowledge is also helpful.

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pruduktová podpora


This learning subscription is not currently associated with a SAS credential.