课程

Analyzing IoT Data in Python

中级技能水平

更新时间 2024年6月

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

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PythonData Manipulation4 小时16 视频53 练习4,250 经验值6,754成就声明

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课程描述

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.

先决条件

Data Manipulation with pandas

1

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.

Introduction to IoT data

50 经验值

IoT devices

50 经验值

Data acquisition

100 经验值

Acquire data with pandas

100 经验值

Understand the data

50 经验值

100 经验值

Read data from file

100 经验值

Understanding the data

100 经验值

Introduction to Data streams

50 经验值

What is MQTT

50 经验值

MQTT single message

100 经验值

Save Datastream

100 经验值

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.

Perform EDA

50 经验值

100 经验值

Histogram Plot

100 经验值

50 经验值

Dealing with missing data

50 经验值

Missing data

100 经验值

Missing data II

100 经验值

Gather minimalistic incremental data

50 经验值

Which timestamp?

50 经验值

Cache Datastream

100 经验值

Date and Time

100 经验值

Prepare and visualize incremental data

50 经验值

50 经验值

Reformat data

100 经验值

Analyzing Energy counter data

100 经验值

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.

Combining datasources for further analysis

50 经验值

Concatenate DataFrames

100 经验值

Combine and resample

100 经验值

Correlation

50 经验值

100 经验值

100 经验值

50 经验值

Standard deviation

100 经验值

Autocorrelation

100 经验值

Seasonality and trends

50 经验值

Seasonal decomposition

100 经验值

Seasonal decomposition II

100 经验值

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.

Prepare data for machine learning

50 经验值

Train/Test split

100 经验值

Logistic Regression

100 经验值

Scaling data for machine learning

50 经验值

Model performance

100 经验值

100 经验值

100 经验值

Develop machine learning pipeline

50 经验值

Creating Pipelines

100 经验值

Store Pipeline

100 经验值

Apply a machine learning model

50 经验值

Model predictions

100 经验值

Apply model to data stream

100 经验值

Wrapping up

50 经验值

Analyzing IoT Data in Python

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