Analyzing IoT Data in Python

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
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Clock4 HoursPlay16 VideosCode53 ExercisesGroup2,509 Learners
Database4250 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.

  1. 1

    Accessing IoT Data

    Free
    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.
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  2. 2

    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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  3. 3

    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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  4. 4

    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.
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Datasets
EnvironmentTraffic (heavy vehicles)Traffic (light vehicles)
Collaborators
Hadrien LacroixHillary Green-Lerman
Matthias Voppichler Headshot

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.
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Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

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Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA

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