This course teaches you how to apply advanced analytics techniques to IoT processes. The course addresses analysis of data at rest as well as streaming data. By using the SAS Viya environment with SAS Event Stream Processing, you learn how to deploy your own deep learning models to streaming data.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

Learn how to

• Identify the analytical capabilities included with SAS Event Stream Processing.
• Construct streaming analytics projects using SAS Event Stream Processing Studio.
• Include your own SAS analytical models in a SAS Event Stream Processing project.
• Create a project using the SAS ESPPy Python interface in a Jupyter Notebook.

Who should attend

Data scientists, data analysts, IoT project team members, and anyone interested in analyzing event stream data

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	1 day
Live Web Classroom:	2 half-day session(s)	System Requirements
e-Learning:	7 hours/180 day license

Before attending this course, it is strongly recommended that you attend the SAS Event Stream Processing Essentials course.

This course addresses SAS Analytics Platform, SAS Event Stream Processing software.

This course is appropriate for those who have SAS Event Stream Processing, and also for people who have SAS Event Stream Processing and SAS Viya or who have SAS Viya alone.

The Internet of Things and Event Stream Processing

• Introduction.
• SAS Event Stream Processing Studio: project and windows.
• IOT data.
• Working with projects in XML.

Training and Scoring Streaming Data

• K-means clustering.
• Streaming text sentiment analysis.

Advanced Analytics

• Why use offline models in-stream?
• How to add an offline model to SAS Event Stream Processing.
• Defining a SAS Micro Analytic Service module input handler.
• SAS Event Stream Processing command-line interactions.

The Python Modeling Interface (Self-Study)

• Load a SAS Event Stream Processing project in Python.
• Considerations when building a SAS Event Stream Processing project in Python.
• Integrating Deep Learning, CAS, Event Stream Processing, and Python.