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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.

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4 Hours16 Videos53 Exercises4,587 Learners

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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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    Introduction to IoT data
    50 xp
    IoT devices
    50 xp
    Data acquisition
    100 xp
    Acquire data with pandas
    100 xp
    Understand the data
    50 xp
    Store data
    100 xp
    Read data from file
    100 xp
    Understanding the data
    100 xp
    Introduction to Data streams
    50 xp
    What is MQTT
    50 xp
    MQTT single message
    100 xp
    Save Datastream
    100 xp
  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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    Perform EDA
    50 xp
    Line plots
    100 xp
    Histogram Plot
    100 xp
    Clean Data
    50 xp
    Dealing with missing data
    50 xp
    Missing data
    100 xp
    Missing data II
    100 xp
    Gather minimalistic incremental data
    50 xp
    Which timestamp?
    50 xp
    Cache Datastream
    100 xp
    Date and Time
    100 xp
    Prepare and visualize incremental data
    50 xp
    Pivoting
    50 xp
    Reformat data
    100 xp
    Analyzing Energy counter data
    100 xp
  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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    Combining datasources for further analysis
    50 xp
    Concatenate DataFrames
    100 xp
    Combine and resample
    100 xp
    Correlation
    50 xp
    Heatmaps
    100 xp
    Pairplot
    100 xp
    Outliers
    50 xp
    Standard deviation
    100 xp
    Autocorrelation
    100 xp
    Seasonality and trends
    50 xp
    Seasonal decomposition
    100 xp
    Seasonal decomposition II
    100 xp
  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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    Prepare data for machine learning
    50 xp
    Train/Test split
    100 xp
    Logistic Regression
    100 xp
    Scaling data for machine learning
    50 xp
    Model performance
    100 xp
    Scaling
    100 xp
    Scaling II
    100 xp
    Develop machine learning pipeline
    50 xp
    Creating Pipelines
    100 xp
    Store Pipeline
    100 xp
    Apply a machine learning model
    50 xp
    Model predictions
    100 xp
    Apply model to data stream
    100 xp
    Wrapping up
    50 xp

Datasets

EnvironmentTraffic (heavy vehicles)Traffic (light vehicles)

Collaborators

Hadrien Lacroix
Hillary Green-Lerman

Prerequisites

Data Manipulation with pandas
Matthias Voppichler HeadshotMatthias 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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