Coursera

Getting Started with SAS Programming (Coursera)

Offered by SAS,
Getting Started with SAS Programming (Coursera)

This course is for users who want to learn how to write SAS programs to access, explore, prepare, and analyze data. It is the entry point to learning SAS programming for data science, machine learning, and artificial intelligence. It is a prerequisite to many other SAS courses.

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

By the end of this course, you will know how to use SAS Studio to write and submit SAS programs that access SAS, Microsoft Excel, and text data. You will know how to explore and validate data, prepare data by subsetting rows and computing new columns, analyze and report on data, export data and results to other formats, use SQL in SAS to query and join tables.
Prerequisites:
Learners should have experience using computer software. Specifically, you should be able to understand file structures and system commands on your operating systems and access data files on your operating systems. No prior SAS experience is needed.
Course 1 of 3 in the SAS Programmer Professional Certificate

Syllabus

WEEK 1
Course Overview and Data Setup
In this module you learn about the course and you set up the data you need to do the practices in the course.
Essentials
In this module you learn how to use SAS programming tools and the fundamentals of SAS program structure and syntax.

WEEK 2
Accessing Data
In this module, you learn to identify the features of a SAS table, access data through SAS libraries, and import data into SAS.

WEEK 3
Exploring and Validating Data
In this module, you learn to use SAS procedures that provide insights about your data. You also learn to subset data so you can focus on particular segments, format data so you can easily understand it, and sort data to identify and resolve duplicate values.

WEEK 4
Preparing Data
In this module, you learn how to do some common data manipulations, such as filtering rows and columns, computing new columns, and performing conditional processing.

WEEK 5
Analyzing and Reporting on Data
In this module, we concentrate on summarizing data by using the SAS procedures that we touched on for data exploration. You also learn how to use titles, column labels, footnotes, and macro variables to enhance your reports and make them more meaningful.

WEEK 6
Exporting Results
In this module, you learn to export SAS tables and results to Excel, Microsoft Word, and PDF files.
Using SQL in SAS
In this module, you learn to use the SQL procedure to read and filter data. You also learn to create and join tables by using SQL.

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

Related Courses

Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

Data Science

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Jun 26th 2026
5-12 Weeks
ML NLP Machine Learning
Details
Advanced Algorithms and Complexity (Coursera) Coursera
University of California, San Diego,Higher School of Economics - HSE University

Advanced Algorithms and Complexity (Coursera)

CS: Software Engineering CS: Theory

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision.

Jun 22nd 2026
5-12 Weeks
Algorithms Data Structures Machine Learning
Details
Python for Data Science, AI & Development (Coursera) Coursera
IBM

Python for Data Science, AI & Development (Coursera)

CS: Programming Data Science

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

Jun 23rd 2026
5-12 Weeks
Programming Python Artificial Intelligence
Details
Generative AI Essentials: Overview and Impact (Coursera) Coursera
University of Michigan

Generative AI Essentials: Overview and Impact (Coursera)

Robotics & Computer Vision

With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like.

Jun 26th 2026
1 Week
Artificial Intelligence AI Coursera Plus
Details
Reproducible Research (Coursera) Coursera
Johns Hopkins University

Reproducible Research (Coursera)

Statistics & Data Analysis Data Science

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations.

Jun 22nd 2026
4 Weeks
Data Analysis Data Science Reproducible Research
Details
Introduction to Data Science in Python (Coursera) Coursera
University of Michigan

Introduction to Data Science in Python (Coursera)

CS: Software Engineering Statistics & Data Analysis

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Jun 22nd 2026
4 Weeks
Python Data Analysis Data Science
Details
Data Manipulation at Scale: Systems and Algorithms (Coursera) Coursera
University of Washington

Data Manipulation at Scale: Systems and Algorithms (Coursera)

Statistics & Data Analysis Data Science

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Jun 22nd 2026
4 Weeks
Algebra Algorithms Databases
Details
Preparing for the Google Cloud Professional Data Engineer Exam (Coursera) Coursera
Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam (Coursera)

CS: Information & Technology Computer Science

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Jun 27th 2026
5-12 Weeks
Machine Learning Data Engineer Google Cloud
Details
Exploratory Data Analysis (Coursera) Coursera
Johns Hopkins University

Exploratory Data Analysis (Coursera)

Statistics & Data Analysis Data Science

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.

Jun 22nd 2026
4 Weeks
Statistics Data Analysis Data Science
Details
Relational Database Support for Data Warehouses (Coursera) Coursera
University of Colorado System

Relational Database Support for Data Warehouses (Coursera)

Statistics & Data Analysis Data Science

Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts. In the assignments in this course, you can use either Oracle or PostgreSQL.

Jun 22nd 2026
5-12 Weeks
Databases SQL Big Data
Details