Advanced Analytics for IoT Using SAS(R) Event Stream Processing

Course code: AIOT35

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.

and certified lecturers

recognized certifications

Wide range of technical
and soft skills courses

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 7 hours

Language: en

Price without VAT: 360 EUR


Starting date: Upon request

Type: Upon request

Course duration: 7 hours

Language: en

Price without VAT: 600 EUR


Type Course
Language Price without VAT
Upon request E-learning 7 hours en 360 EUR Register
Upon request Upon request 7 hours en 600 EUR Register
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Target group

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

Course structure

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.


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

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