標題 等級 培訓格式
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 Live Web Classroom
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
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression Free e-learning
本課程涵蓋了一系列的統計主題和使用 SAS 軟體進行統計分析。課程著重於T 檢定,變異數分析(ANOVA), 線性迴歸和邏輯斯迴歸。

2 Fundamental Classroom e-Learning
Forecasting Using SAS Forecast Server Software
本課程介紹如何利用SASForecast Studio 進行大量且自動化的時間序列預測與分析,課程包含實機操作講解與練習題。單機版或client/server 版使用者皆適合此課程。

2 Fundamental Classroom 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
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
SAS Data Integration Studio 2: Additional Topics Business Knowledge Series
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 Live Web Classroom
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 Analysis Using the Proportional Hazards Model

3 Intermediate Classroom
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 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
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 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

3 Intermediate Classroom e-Learning
SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression
本課程內容包含許多統計分析方法介紹,以及如何使用SAS Enterprise Guide執行這些統計分析,主題包括推論性統計介紹、變異數分析、多元迴歸、類別資料分析和邏輯斯迴歸 。

3 Intermediate Classroom Live Web Classroom 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.

The self-study e-learning includes:

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

4 Expert e-Learning
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
Bayesian Analyses Using SAS
本課程著重在利用不同模型程序進行貝氏分析,包含羅吉斯迴歸模型、Cox 比例涉險模型、廣義線性混合模型、零膨脹卜瓦松模型等。課程中也會提到交叉設計與整合分析中的貝氏分析原理。

4 Expert 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
Applied Clustering Techniques
本課程對各種集群分析方法的概念與SAS 語法的實作進行全面性的介紹,其中包含資料的準備,變數的篩選,k-means分群法和階層式分群法。

4 Expert Classroom Live Web Classroom
Mixed Models Analyses Using the SAS System

4 Expert e-Learning
Feature Engineering and Data Preparation for Analytics
本課程介紹如何利用SAS 程式語法進行特徵工程,萃取出有意義的輸入變數,以增進預測模型的表現。同時課程中也會對建模過程常見的資料選取陷阱進行說明,並提供避免踏入陷阱的適合策略。

4 Expert Classroom e-Learning
Predictive Modeling Using Logistic Regression
本課程的範圍主要著重於使用SAS/STAT 中的LOGISTIC 程序建置預測模型。本課程亦將探討如何使用有效率的技巧處理大量資料,選擇變數、評估模型、處理遺失值的問題。

4 Expert Classroom Live Web Classroom e-Learning