Designed for professionals, data analysts, statisticians and scientists who want to be able to communicate about data more confidently and perform common statistical analysis.
Digital courses
Unlimited access to digital courses and books in this subscription.
Prepare for certification
Unlimited access to certification preparation materials.
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Earn a digital badge for each completed course.
Hands-on learning and practice
Get Full access to SAS software to practice as you learn.
About this Subscription
Statistical techniques are used to drive decisions in fields including business, banking, insurance, science, engineering, environment, health care, and more. These statistics courses provide the foundation needed to master statistical vocabulary and concepts and learn how to apply common statistical procedures to make more informed and accurate business decisions.
Learn how to:
Understand key concepts in statistics, including definitions, terminology, and common procedures and techniques.
Use t tests, ANOVA, linear regression, and logistic regression.
Use SAS Studio tasks to execute the FREQ, MEANS, and UNIVARIATE procedures.
Build predictive models in an interactive, exploratory way.
Understand the fundamental aspects of probability and distributions.
Interpret statistical results based on sample size, confidence intervals and significance.
Apply Bayesian analysis using the PHREG, GENMOD and MCMC procedures.
More fully understand fundamental principles and best practices of statistics.
Courses Included in Subscription
Fundamental Statistical Concepts
1
COURSE
Essential Data Analysis Using SAS® Studio Tasks: A Point-and-Click Approach
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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.
COURSE
Introduction to Statistical Concepts
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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.
COURSE
SAS Analytics: Getting Started
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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.
COURSE
Leading with Analytics
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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.
Applied Statistics for Machine Learning Associate Certification
2
COURSE
Statistics You Need to Know for Machine Learning
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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 Machine Learning Using SAS Viya, Interactive Machine Learning in SAS Viya, SAS Visual Statistics in SAS Viya: Interactive Model Building, and Supervised Machine Learning Procedures Using SAS Viya in SAS Studio.
For students interested in statistics for inference and explanatory analysis used in scientific and medical research, Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression is an appropriate foundational course.
CERTIFICATION PREP
SAS Certified Associate: Applied Statistics for Machine Learning
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As you prepare for the Applied Statistics for Machine Learning certification exam, you will develop a deep understanding of statistical and machine learning concepts as well as learn how to apply explanatory and predictive modeling techniques using SAS Viya.
CERTIFICATION PREP
Practice Exam: Applied Statistics for Machine Learning
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SAS Certified Specialist: Applied Statistics for Machine Learning
Modeling Using SAS Visual Statistics Associate Certification
3
COURSE
SAS® Visual Statistics in SAS® Viya®: Interactive Model Building
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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.
CERTIFICATION PREP
SAS Certified Associate: Modeling Using SAS Visual Statistics
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As you prepare for the Modeling Using SAS Visual Statistics certification exam, you will build and explore descriptive and predictive models, perform model validation, assess model goodness of fit, modify and compare models, and practice scoring models.
CERTIFICATION PREP
Practice Exam: Modeling Using SAS Visual Statistics
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SAS Statistical Business Analyst Certification
4
COURSE
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
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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.
COURSE
Predictive Modeling Using Logistic Regression
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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.
CERTIFICATION PREP
SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling
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Preparation for the SAS Statistical Business Analysis Using SAS 9: Regression and Modeling certification exam involves learning how to conduct and interpret complex statistical data analysis. You will be guided into analyses of variance, linear and logistic regressions, preparing inputs for predictive models and measuring model performance.
More Statistical Models
5
COURSE
SAS® Enterprise Guide®: ANOVA, Regression, and Logistic Regression
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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.
COURSE
Statistics 2: ANOVA and Regression
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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.
COURSE
Categorical Data Analysis Using Logistic Regression
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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.
COURSE
SAS® Programming for R Users
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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.
COURSE
Bayesian Analyses Using SAS®
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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.
COURSE
Statistical Process Control Using SAS/QC® Software
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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.
Principles and Best Practices
6
COURSE
Graphing Data Effectively and Avoiding Common Pitfalls
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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.
COURSE
The Modeling Life Cycle for Data Scientists
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This course gives an overview of the statistical methods used by data scientists, with an emphasis on the applicability to business problems. Attendees do not need access to software for this course, and the mathematical details are kept to a minimum.
COURSE
Responsible Innovation and Trustworthy AI
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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.
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.
COURSE
Modern Data Science with SAS® Viya® Workbench and Python
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This course showcases how to manage a data science project using both SAS and Python to predict customer churn for a fictitious online personal styling service. Using SAS Viya Workbench, you’ll explore how to access, transform, and analyze data from cloud object storage and data lakehouses, then build machine learning models in both SAS and Python. By the end, you’ll be equipped to handle data exploration, model deployment, and integrate version control with GitHub in a modern cloud environment.
Machine Learning Leadership and Practice - End-to-End Mastery
7
COURSE
The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
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This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning.
COURSE
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
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This course will guide you to lead or participate in the end-to-end implementation of machine learning (aka predictive analytics). Unlike most machine learning courses, it prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment.
Whether you'll participate on the business or tech side of a machine learning project, this course delivers essential, pertinent know-how. You'll learn the business-level fundamentals needed to ensure the core technology works within – and successfully produces value for – business operations. If you're more a quant than a business leader, you’ll find this is a rare opportunity to ramp up on the business side, since technical ML trainings don’t usually go there. But know this: The soft skills are often the hard ones.
COURSE
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
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This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants.
And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers.
“I think the self-paced training was the BEST I’ve ever taken. The videos were short and segmented correctly to keep my attention and the activities and quizzes were just enough to help my confidence.”
