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Using SAS Viya REST APIs with Python and R

Jordan Bakerman and Ari Zitin

SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems. And once SAS Viya has done the heavy lifting, you’ll be able to download data to the client and use native open source syntax to compare results and create graphics.

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What's inside

Syllabus

Course Overview
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
SAS® Viya® and Open Source Integration
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In this module you learn about the analytical processing engine behind SAS Viya, the Cloud Analytic Services server. You also learn how to submit data processing commands to SAS Viya from the open source languages R and Python.
Machine Learning
In this module you learn how to use R and Python to create, optimize, and assess SAS Viya predictive models. You also learn how to use R and Python to efficiently manage the creation and assessment of these models.
Text Analytics
In this module you learn how natural language processing is used to analyze collections of text documents. You also learn how to turn blocks of unstructured text into numeric inputs suitable for predictive modeling.
Deep Learning
In this module you learn how deep learning methods extend traditional neural network models with new options and architectures. You also learn how recurrent neural networks are used to model sequence data like time series and text strings, and how to create these models using R and Python APIs for SAS Viya.
Time Series
In this module you learn how to model time series using two popular methods, exponential smoothing and ARIMAX. You also learn how to use the R and Python APIs for SAS Viya to create forecasts using these classical methods and using recurrent neural networks for more complex problems.
Image Classification
In this module you learn how convolutional neural networks are used to classify images and how to use the R and Python APIs for SAS Viya to create convolutional neural networks.
Factorization Machines
In this module you learn how factorization machines are used to create recommendation engines and how to build factorization machine models in SAS Viya using the R and Python APIs.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides a comprehensive study of the SAS Viya APIs to enable control over the SAS Cloud Analytic Services from Jupyter Notebooks in R or Python
Learners will gain proficiency in uploading data into the cloud, analyzing data, and creating predictive models with SAS Viya using the SAS Scripting Wrapper for Analytics Transfer (SWAT) package
Offers hands-on labs and interactive materials to enhance the learning experience
Develops professional skills in a particular topic or set of topics
Taught by Jordan Bakerman and Ari Zitin, recognized instructors in the field
Explicitly requires learners to come in with extensive background knowledge first

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Reviews summary

Practical sas viya rest api course

According to students, this course provides engaging assignments and is useful for beginners. However, learners report that the lab not working is an issue.
Assignments keep students engaged.
"Very useful for beginners."
Lab issues may hinder progress.
"Lab is not working"

Activities

Coming soon We're preparing activities for Using SAS Viya REST APIs with Python and R. These are activities you can do either before, during, or after a course.

Career center

Learners who complete Using SAS Viya REST APIs with Python and R will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
As a Machine Learning Engineer, SAS Viya will be an essential tool for you to leverage. This course will help you use the SAS Viya APIs to build advanced machine learning models. This can aid you in a variety of tasks, from predicting customer churn to optimizing marketing campaigns.
Data Scientist
Data Scientists employ SAS Viya to better understand data and its predictive power. By learning to use the SAS Viya APIs from this course, you can stay at the forefront of this approach to data analysis.
Data Engineer
Data Engineers use SAS Viya to build and maintain big data solutions. This course will provide you with the skills to use SAS Viya APIs, helping you to automate tasks and improve the efficiency of your data engineering pipelines.
Statistician
Statisticians use SAS Viya to perform statistical analysis on large datasets in a high performance computing environment. This course will help you to use SAS Viya APIs, making your work more efficient and opening up new possibilities for your career.
Data Architect
Data Architects leveraging SAS Viya to design and manage big data solutions. This course will help you use the SAS Viya APIs, allowing you to develop architectures that meet the specific needs of your organization and its unique data challenges.
Consultant
Consultants use SAS Viya to help clients solve business problems and implement data-driven solutions. This course will provide you with the knowledge and skills necessary to use SAS Viya APIs effectively, enabling you to provide valuable insights and solutions to your clients.
Researcher
Researchers use SAS Viya to conduct advanced data analysis and modeling. This course will provide you with the skills to use SAS Viya APIs, allowing you to access and manipulate big data more efficiently and to develop more sophisticated research models.
Data Analyst
Data Analysts utilize SAS Viya for data analysis as well as advanced tasks such as predictive modeling. This course will help you to use the SAS Viya APIs, allowing you to become more efficient and effective in your work.
Risk Analyst
Risk Analysts use SAS Viya to assess and manage risk. This course can provide you with the skills to use SAS Viya APIs, allowing you to develop more sophisticated risk models and to make more informed decisions about risk management.
Financial Analyst
Financial Analysts leverage SAS Viya to analyze financial data and make investment recommendations. This course can help you build a foundation in SAS Viya APIs, providing you with the skills to extract valuable insights from financial data and make more informed investment decisions.
Product Manager
Product Managers that leverage SAS Viya to develop and launch data-driven products. This course can help you gain a deep understanding of SAS Viya APIs, enabling you to make informed decisions about product features and to create products that meet the needs of your customers.
Quantitative Analyst
For the Quantitative Analyst, SAS Viya offers advanced analytics tools that can aid in investment strategy and modeling. This course can help you take full advantage of these tools, giving you an edge in this highly competitive role.
Software Developer
Software Developers create and maintain software solutions that use SAS Viya. This course will help you to gain the skills needed to use SAS Viya APIs in your software development projects, allowing you to create more powerful and efficient applications.
Business Analyst
Business Analysts use SAS Viya to make data-driven decisions. This course will help you to become proficient in using SAS Viya APIs, allowing you to extract valuable insights from data in order to make better business decisions for your clients.
Academic
Academics may use SAS Viya to conduct research and teach data science courses. This course can provide you with a solid foundation in using SAS Viya APIs, enabling you to integrate SAS Viya into your research and teaching more effectively.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Using SAS Viya REST APIs with Python and R.
Comprehensive guide to using Python for data analysis. It covers all the basics of data analysis, including data cleaning, exploration, and visualization. It also covers more advanced topics, such as machine learning and deep learning.
Comprehensive guide to using R for data science. It covers all the basics of data analysis, including data cleaning, exploration, and visualization. It also covers more advanced topics, such as machine learning and deep learning.
Comprehensive guide to advanced analytics with R. It covers all the basics of advanced analytics, including machine learning, deep learning, and text mining. It also covers more advanced topics, such as natural language processing and computer vision.
Comprehensive guide to time series analysis with R. It covers all the basics of time series analysis, including time series decomposition, forecasting, and model selection. It also covers more advanced topics, such as state space models and GARCH models.
Comprehensive guide to text mining with R. It covers all the basics of text mining, including text preprocessing, text analysis, and text classification. It also covers more advanced topics, such as sentiment analysis and topic modeling.
Comprehensive guide to machine learning with Python. It covers all the basics of machine learning, including data preprocessing, model selection, and evaluation. It also covers more advanced topics, such as deep learning.
Comprehensive guide to deep learning with Python. It covers all the basics of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. It also covers more advanced topics, such as natural language processing and computer vision.
Comprehensive guide to machine learning with Python for beginners. It covers all the basics of machine learning, including data preprocessing, model selection, and evaluation. It also covers more advanced topics, such as deep learning.
Comprehensive guide to deep learning with Python for beginners. It covers all the basics of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. It also covers more advanced topics, such as natural language processing and computer vision.

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