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Datacamp

Analyzing IoT Data in Python

via Datacamp

Overview

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.

Have you ever heard about Internet of Things devices? Of course, you have. Maybe you also have a Raspberry PI in your house monitoring the temperature and humidity.
IoT devices are everywhere around us, collecting data about our environment.
You will be analyzing Environmental data, Traffic data as well as energy counter data.
Following the course, you will learn how to collect and store data from a data stream.
You will prepare IoT data for analysis, analyze and visualize IoT data, before implementing
a simple machine learning model to take action when certain events occur and deploy this model
to a real-time data stream.

Syllabus

Accessing IoT Data
-In this chapter, you will first understand what IoT data is.
Then, you learn how to aquire IoT data through a REST API and using an MQTT data stream to collect data in real time.

Processing IoT data
-In the second chapter, you will look at the data you gathered during the first chapter. You will visualize the data and learn the importance of timestamps when dealing with data streams. You will also implement caching to an MQTT data stream.

Analyzing IoT data
-In this chapter, you will combine multiple datasoures with different time intervals.
You will then analyze the data to detect correlations, outliers and trends.

Machine learning for IoT
-In this final chapter, you will use the data you analyzed during the previous chapters to build a machine learning pipeline. You will then learn how to implement this pipeline into a data stream to make realtime predictions.

Taught by

Matthias Voppichler

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