1. Essential Data Analysis Using SAS Studio Tasks: A Point-and-Click Approach
In this course, you gain introductory knowledge into exploratory data analysis using SAS Studio tasks that execute the FREQ, MEANS, and UNIVARIATE procedures. The course focuses on the differences among these procedures as well as output interpretation. Introductory statistics terminology is also discussed.
2. 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.
3. SAS Analytics: Getting Started
Get started on your journey to using SAS for advanced analytics by spending a bit of time each day with a new aspect of SAS Analytics in SAS Viya. Over the course of three weeks, you’ll get a good idea of the skills you may need to develop and the tasks that will be needed to make the most of SAS Viya for predictive modeling, time series forecasting, optimization, and more advanced AI algorithms. At the end of your journey be sure to explore formal enablement opportunities you can take advantage of to continue your quest in becoming an expert in advanced analytics.
4. Leading with Analytics
You know that analytics can help your company succeed. However, it is not always clear where and how analytics can help. Even worse, it can sometimes seem like everyone is speaking a different language. This course helps you lead your organization to greater success by pairing your expertise about the business with an understanding of where and how data science can help. You build on your strengths to collaborate effectively with experienced data scientists and to mentor novice analytics professionals to engage in the business. You also learn about five organizational styles for analytics with proven business outcomes.
5. Statistics You Need to Know for Machine Learning
When it comes to using data, there are two main camps, traditional statistics and machine learning, and the two camps complement each other. Statistics remains highly relevant, irrespective of the “bigness” of data. Its role remains what it has always been, but it is even more important now. There is a need to transition from traditional statistical modeling to the machine learning world. This course introduces the statistical background necessary for machine learning using SAS Viya. Knowledge of statistics relevant to machine learning will prepare you to become a data scientist. The course prepares you for future instruction on doing machine learning (including its underlying methodology that has statistical foundations) and enables you to develop a deeper understanding of machine learning models.
This course is a prerequisite to many of the courses in the data science curriculum. A more advanced treatment of machine learning occurs in the courses Supervised Machine Learning Pipelines Using SAS(R) Viya(R), Interactive Machine Learning in SAS® Viya®, SAS® Visual Statistics in SAS® Viya®: Interactive Model Building, and Supervised Machine Learning Using SAS(R) Viya(TM) .
For students interested in statistics for inference and explanatory analysis used in scientific and medical research, Statistics I: Introduction to ANOVA, Regression, and Logistic Regression is an appropriate foundational course.
6. SAS Visual Statistics in SAS Viya: Interactive Model Building
This course introduces SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data. This course is appropriate for users of SAS Visual Analytics in SAS Viya.
7. Practice Exam: Modeling Using SAS Visual Statistics
8. 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 II: 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.
9. 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.
10. SAS Statistical Business Analyst Certification Preparation
SAS certifications are globally recognized, so having them on your résumé proves you’ve received in-depth training from industry experts. These materials will help prepare you to earn the SAS Statistical Business Analyst Certification.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. Graphing Data Effectively and Avoiding Common Pitfalls
The human mind is geared to look at patterns and attach immediate explanation to the patterns, consciously or subconsciously, and sometimes that conclusion or explanation might not be apt. This course discusses the common fallacies and paradoxes in statistics and data analysis, as well as common pitfalls in data visualization. The course also teaches general guidelines that help you avoid those pitfalls to ensure a fair and accurate representation of facts.
18. Introduction to Data Science Statistical Methods
This course gives an overview of the statistical methods used by data scientists, with an emphasis on the applicability to business problems. No software is shown in the course, and the mathematical details are kept to a minimum.
19. 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.
