Using Python and R with SAS® Viya® for Advanced Analytics
3h 36m + Hands-On Practice
Available in:
Using Python and R with SAS® Viya® for Advanced Analytics
VMLOPR : VMPR41
In this course, you learn to use R and Python to take control of the SAS Viya Cloud Analytic Services (CAS) distributed computing environment to develop machine learning models. You learn to upload data into the in-memory distributed environment, analyze data using Pandas like functionality, build machine learning models, and assess those models in CAS using familiar open-source functionality via the SWAT (SAS Scripting Wrapper for Analytics Transfer) package.
Learn How To
- Use the SWAT package to control CAS via R and Python.
- Manage, alter, and prepare data using Python and R syntax.
- Create and assess machine learning models for classification tasks, including logistic regressions, decision trees, forests, support vector machines, and neural networks.
- Create and assess deep learning models for forecasting, sentiment prediction, and image classification.
- Use open-source syntax to automate the submission of CAS actions using functions and loops.
Who Should Attend
Data scientists with Python or R experience who want to take advantage of SAS Viya distributed analytics
Prerequisites
Students should have experience working with data, creating predictive models, and writing open-source programs. Some SAS experience is recommended.
SAS Products Covered
SAS Viya;SAS Machine Learning
Course Outline
SAS Viya and Open-Source Integration
- SAS Viya and Cloud Analytic Services (CAS).
- Open-source development interfaces.
- Scripting Wrapper for Analytics Transfer (SWAT).
- Fundamentals of the R and Python APIs.
- Predictive modeling.
- Predictive models.
- Model assessment.
- Creating, scoring, and assessing predictive models with the R/Python API.
- Introduction to text analytics.
- Natural language processing.
- Traditional neural networks versus deep learning.
- Recurrent neural networks.
- Time series modeling and forecasting.
- Exponential smoothing models.
- ARIMAX models.
- Creating forecasting models using the R/Python API.
- Deep learning image classification.
- Modeling interactions in factorization machines.
Live Class Schedule
Duration: 12 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
This course isn't publicly scheduled, but private training and mentoring may be available. Contact us to explore options.
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Mentoring Services
Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.