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Analyzing IoT Data in Python
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
- 4 hours
- 16 Videos
- 53 Exercises
- 660 Participants
- 4,250 XP
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Course Description
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.
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1
Accessing IoT Data
FreeIn 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.
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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.
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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.
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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.
-
1
Accessing IoT Data
FreeIn 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.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
Matthias Voppichler
Software Developer
Matthias is an IT Developer with over 10 years of experience in analyzing data and developing data Pipelines for different kinds of data. His responsabilities include developing and maintaining data Pipelines, as well as supporting the business to gather insights from the collected data and finding new ways to combine different data sources.
See MoreCollaborators
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Hadrien Lacroix
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Hillary Green-Lerman