Using SAS Viya REST APIs with Python and R
Master SAS Viya's REST APIs for data analysis using Python and R. Learn cloud analytics, machine learning, and deep learning integration with hands-on practice.
Master SAS Viya's REST APIs for data analysis using Python and R. Learn cloud analytics, machine learning, and deep learning integration with hands-on practice.
SAS Viya is a powerful in-memory distributed environment designed for efficient big data analysis. This comprehensive course teaches professionals how to leverage SAS Viya APIs through Jupyter Notebook using R or Python. Students learn to manage cloud analytics, create predictive models, and implement both machine learning and deep learning solutions. The course covers data uploading, analysis techniques, and the SWAT package implementation. Through practical exercises, participants master various modeling techniques including text analytics, time series analysis, and image classification. The course emphasizes hands-on learning with real-world applications.
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What you'll learn
Connect to SAS Cloud Analytic Services using R and Python
Implement machine learning models with SWAT package
Create and optimize deep learning neural networks
Analyze text data using natural language processing
Develop time series forecasting models
Build image classification systems
Skills you'll gain
This course includes:
401 Minutes PreRecorded video
23 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 8 modules in this course
The course offers a comprehensive introduction to using SAS Viya REST APIs with Python and R. Students learn to leverage cloud analytics services, implement machine learning models, and perform complex data analysis. The curriculum covers various advanced topics including text analytics, deep learning, time series analysis, image classification, and factorization machines, providing practical skills for real-world data science applications.
Course Overview
Module 1 · 1 Hours to complete
SAS Viya and Open Source Integration
Module 2 · 2 Hours to complete
Machine Learning
Module 3 · 3 Hours to complete
Text Analytics
Module 4 · 2 Hours to complete
Deep Learning
Module 5 · 2 Hours to complete
Time Series
Module 6 · 2 Hours to complete
Image Classification
Module 7 · 1 Hours to complete
Factorization Machines
Module 8 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Data Science Educator and Analytics Expert
Ari Zitin is a Senior Analytical Training Consultant at SAS, where he specializes in teaching advanced analytical techniques and the effective use of SAS software for data analysis. He holds bachelor's degrees in both physics and mathematics from the University of North Carolina at Chapel Hill, where his research focused on low-energy physics data related to neutrinos. Zitin has also taught introductory and advanced physics courses at UC Berkeley while pursuing a master's degree in physics with a focus on nonlinear dynamics. At SAS, he has developed courses that integrate Python programming with SAS analytical procedures, enhancing the learning experience for users. His expertise extends to conducting workshops on machine learning and model interpretability, helping participants understand complex analytical models and their applications. Through his work, Zitin is committed to empowering individuals and organizations to leverage data-driven insights for informed decision-making.
Statistical Forecasting Expert and Programming Education Innovator
Dr. Jordan Bakerman serves as an Analytical Training Consultant at SAS, where he specializes in bridging open-source and SAS analytics platforms. His Ph.D. research at North Carolina State University focused on leveraging social media data to forecast real-world events, including civil unrest and influenza rates. As the creator of the widely-used "SAS Programming for R Users" course, he developed an innovative cookbook-style approach to help R programmers transition efficiently to SAS. His teaching portfolio includes courses on statistical analysis, regression modeling, and API integration between SAS Viya and open-source platforms. Through his Coursera courses "Introduction to Statistical Analysis: Hypothesis Testing," "Regression Modeling Fundamentals," and "Using SAS Viya REST APIs with Python and R," he shares his expertise in statistical programming and analysis. His work focuses on making advanced statistical concepts accessible while helping professionals integrate open-source tools with SAS technologies.
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