Coursera

Preparing for the SAS® Viya® Programming Certification Exam (Coursera)

Offered by SAS,
Preparing for the SAS® Viya® Programming Certification Exam (Coursera)

Welcome to the Preparing for the SAS Viya Programming Certification Exam course. This is the third and final course in the Coursera SAS Programmer specialization. You will apply what you have learned in the first two courses by writing code to execute in SAS Cloud Analytic Services and practicing for the SAS certification exams.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

This is an advanced course, intended for learners who have completed the first two courses in the Coursera SAS Programmer specialization: SAS Programming for Distributed Computing in SAS Viya and CASL Programming for Distributed Computing in SAS Viya.
By the end of the course, you be prepared to take either of these SAS credential exams:

  • SAS® Viya® Programming Associate
  • SAS® Viya® Programming Specialist

Course 3 of 3 in the Distributed Programming in SAS® Viya® for Data Analysts Specialization.

Syllabus

WEEK 1
Course Overview and Logistics
In this module you set up your practice data for this course.
Practice what you Learned in SAS Programming for Distributed Computing in SAS Viya
In this module you practice you what you learned in the SAS Viya Programming course by using the software to complete programming tasks.

WEEK 2
Practice what you Learned in CASL Programming for Distributed Computing in SAS Viya
In this module you practice what you learned in the CASL Programming course by using software to perform tasks.
Take a SAS Certification Practice Exam
In this module you learn how to get certified in SAS Viya Programming and take one or both SAS Certification practice exams.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Machine Learning for Data Analysis (Coursera) Coursera
Wesleyan University

Machine Learning for Data Analysis (Coursera)

Statistics & Data Analysis Data Science

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Sep 7th 2026
4 Weeks
Python Machine Learning Data Analysis
Details
CASL Programming for Distributed Computing in SAS® Viya® (Coursera) Coursera
SAS

CASL Programming for Distributed Computing in SAS® Viya® (Coursera)

Statistics & Data Analysis Data Science

Welcome to the CASL Programming for Distributed Computing in SAS Viya course. SAS Viya is an AI, analytic and data management platform running on a scalable, distributed, cloud-native architecture. In this course you will learn how how to use the native CAS programming language (CASL) to leverage SAS Cloud Analytics Services (CAS), the high-performance, in-memory analytics and distributed computing engine in SAS Viya . You will learn how to use CASL to access, explore, prepare, analyze, and summarize data in the CAS server's massively parallel processing environment.

Jan 6th 2025
5-12 Weeks
Data Analysis SAS Viya Coursera Plus
Details
Regression Modeling in Practice (Coursera) Coursera
Wesleyan University

Regression Modeling in Practice (Coursera)

Statistics & Data Analysis

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Sep 11th 2026
4 Weeks
Python Data Analysis Linear Regression
Details
Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera)

Statistics & Data Analysis Data Science

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Sep 28th 2026
4 Weeks
Algebra Linear Algebra Data Science
Details
Doing More with SAS Programming (Coursera) Coursera
SAS

Doing More with SAS Programming (Coursera)

Statistics & Data Analysis CS: Programming

This course is for business analysts and SAS programmers who want to learn data manipulation techniques using the SAS DATA step and procedures to access, transform, and summarize data. The course builds on the concepts that are presented in the Getting Started with SAS Programming course and is not recommended for beginning SAS software users.

Sep 7th 2026
5-12 Weeks
Data Analysis Data Science SAS
Details
Applied Social Network Analysis in Python (Coursera) Coursera
University of Michigan

Applied Social Network Analysis in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to network analysis through the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness.. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

Sep 28th 2026
4 Weeks
Python Networks Social Networks
Details
Data Analysis Tools (Coursera) Coursera
Wesleyan University

Data Analysis Tools (Coursera)

Statistics & Data Analysis Data Science

In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

Sep 7th 2026
4 Weeks
Python Data Analysis ANOVA
Details
Basic Statistics (Coursera) Coursera
University of Amsterdam

Basic Statistics (Coursera)

Statistics & Data Analysis Data Science

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.

Sep 28th 2026
5-12 Weeks
Statistics Probability Regression
Details
Understanding China, 1700-2000: A Data Analytic Approach, Part 1 (Coursera) Coursera
The Hong Kong University of Science and Technology - HKUST

Understanding China, 1700-2000: A Data Analytic Approach, Part 1 (Coursera)

Statistics & Data Analysis Data Science

The purpose of this course is to summarize new directions in Chinese history and social science produced by the creation and analysis of big historical datasets based on newly opened Chinese archival holdings, and to organize this knowledge in a framework that encourages learning about China in comparative perspective. Our course demonstrates how a new scholarship of discovery is redefining what is singular about modern China and modern Chinese history.

Sep 28th 2026
5-12 Weeks
History Education China
Details
Dealing With Missing Data (Coursera) Coursera
University of Maryland, College Park

Dealing With Missing Data (Coursera)

Statistics & Data Analysis Data Science

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.

Sep 7th 2026
4 Weeks
Data Analysis Data Collection SAS
Details