SAS/STAT

Название Уровень Форматы обучения
Статистика 1: Введение в дисперсионный анализ, линейную и логистическую регрессию Free e-learning
Данный курс предназначен для пользователей программного обеспечения SAS/STAT, выполняющих статистический анализ. В курсе обсуждаются t-тесты, дисперсионный анализ (ANOVA), линейная регрессия, а также краткое введение в построение логистической регрессии. Прохождение этого курса (или соответствующие практические знания) являются необходимым требованием для прохождения многих курсов по статистическому анализу. Более детальное обсуждение дисперсионного анализа и регрессии даётся в курсе Statistics 2: ANOVA and Regression. Более детальное обсуждение логистической регрессии даётся в курсах Categorical Data Analysis Using Logistic Regression course и Predictive Modeling Using Logistic Regression.Данный курс может помочь вам при подготовке к следующим сертификационным экзаменам: SAS Certified Clinical Trials Programmer Using SAS, SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling, SAS Big Data Preparation, Statistics, and Visual Exploration Exam

2 Fundamental Classroom Live Web Classroom e-Learning
SAS Programming for R Users Free e-learning
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 e-Learning
Explaining Analytics to Decision Makers: Insights to Action Business Knowledge Series
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 Live Web Classroom
Data Cleaning Techniques Business Knowledge Series
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 Live Web Classroom
Conjoint Analysis: Evaluating Consumer Preferences Using SAS Software Business Knowledge Series
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 e-Learning
Discrete Choice Modeling Using SAS Software Business Knowledge Series
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 e-Learning
Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models Business Knowledge Series
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 e-Learning
Survival Data Mining: A Programming Approach Business Knowledge Series
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 Live Web Classroom
Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS Business Knowledge Series
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.

4 Expert Classroom Live Web Classroom
Robust Regression Techniques in SAS/STAT Business Knowledge Series
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 e-Learning
Net Lift Models: Optimizing the Impact of Your Marketing Efforts Business Knowledge Series
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.

4 Expert Classroom Live Web Classroom e-Learning
Introduction to Statistical Concepts Free e-learning
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 e-Learning
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 Live Web Classroom e-Learning
Прогнозирование временных рядов с использованием SAS Forecast Server
В данном курсе рассматривается использование интерактивного интерфейса SAS Forecast Studio для автоматического создания прогнозов.

2 Fundamental Classroom Live Web Classroom e-Learning
Построение Моделей и Решение Оптимизационных Задач с SAS/OR
В этом курсе объясняется, как формулировать и получать решение для оптимизационных задач с использованием процедуры OPTMODEL, начиная от подачи входных данных и заканчивая интерпретацией полученного вывода и созданием отчетов. Курс покрывает задачи линейного, целочисленного, частично целочисленного и нелинейного программирования, особенное внимание уделяется формулировке и построению моделей.Данный курс может помочь при подготовке к следующим сертификационным экзаменам: SAS Text Analytics, Time Series, Experimentation and Optimization.

3 Intermediate Classroom
Прогнозирование временных рядов с использованием SAS Forecast Server
В данном курсе рассматривается использование интерактивного интерфейса SAS Forecast Studio для автоматического создания прогнозов.

3 Intermediate Classroom
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 Live Web Classroom
Управление жизненным циклом аналитических моделей с использованием SAS Model Manager версии 14.1
В данном курсе изучаются основные функции SAS Model Manager: управление источниками данных, создание проекта, импорт моделей, использование SAS Model Manager Query Utility, создание задач по определению количественных показателей, экспорт моделей и проектов в репозиторий SAS и создание и настройка версий жизненных циклов.Курс также охватывает создание отчётов со сравнением моделей, ввод в эксплуатацию моделей, создание отчётов для мониторинга моделей, введённых в эксплуатацию, и создание пользовательских отчётов.

3 Intermediate Classroom Live Web Classroom
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 e-Learning
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 e-Learning
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 e-Learning
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 Live Web Classroom e-Learning
SAS Enterprise Guide: дисперсионный, регрессионный и логит-регрессионный анализ
Данный курс разработан для пользователей SAS Enterprise Guide, желающих выполнять статистический анализ.

3 Intermediate Classroom Live Web Classroom e-Learning
Статистика 2: Дисперсионный и регрессионный анализ
Данный курс обучает анализу данных с непрерывным и дискретным откликом. В курсе обсуждаются линейная регрессия, регрессия Пуассона, отрицательная биномиальная регрессия, гамма регрессия, дисперсионный анализ, линейная регрессия с переменными-индикаторами, ковариационный анализ и смешанные модели дисперсионного анализа.

3 Intermediate Classroom Live Web Classroom e-Learning
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 Live Web Classroom
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 Live Web Classroom
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.

The self-study e-learning includes:

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

4 Expert Live Web Classroom
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.

The self-study e-learning includes:

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

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

4 Expert Live Web Classroom
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 Live Web Classroom
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 Live Web Classroom e-Learning
Подготовка данных для интеллектуального анализа данных
В данном курсе рассматриваются программные методики и подходы, которые могут быть использованы аналитиками для трансформации исходных данных в формы, которые наиболее подходят для построения прогнозных моделей. На протяжении данного курса активно применяется программирование на языке SAS.

4 Expert Classroom Live Web Classroom e-Learning
Моделирование с помощью логистической регрессии
В этом курсе изучается построение предсказательных статистических моделей с помощью программного обеспечения SAS/STAT, и, в частности, процедуры LOGISTIC. Обсуждаются подходы для выбора переменных, оценки эффективности моделей, обработки пропущенных значений, использования эффективных методов для обработки больших наборов данных.Этот курс может помочь вам подготовиться к следующим сертификационным экзаменам: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling, SAS Advanced Predictive Modeling.

4 Expert Classroom Live Web Classroom e-Learning