IoT Data Analytics using Python M S Hariharan
(ebook)
(audiobook)
(audiobook)
- Autor:
- M S Hariharan
- Wydawnictwo:
- BPB Publications
- Ocena:
- Stron:
- 398
- Dostępne formaty:
-
ePubMobi
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Opis
książki
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IoT Data Analytics using Python
Harness the power of Python to analyze your IoT data
Key Features
Learn how to build an IoT Data Analytics infrastructure.
Explore advanced techniques for IoT Data Analysis with Python.
Gain hands-on experience applying IoT Data Analytics to real-world situations. Description
Python is a popular programming language for data analytics, and it is also well-suited for IoT Data Analytics. By leveraging Python's versatility and its rich ecosystem of libraries and tools, Data Analytics for IoT can unlock valuable insights, enable predictive capabilities, and optimize decision-making in various IoT applications and domains.
The book begins with a foundation in IoT fundamentals, its role in digital transformation, and why Python is the preferred language for IoT Data Analytics. It then covers essential data analytics concepts, how to establish an IoT Data Analytics environment, and how to design and manage real-time IoT data flows. Next, the book discusses how to implement Descriptive Analytics with Pandas, Time Series Forecasting with Python libraries, and Monitoring, Preventive Maintenance, Optimization, Text Mining, and Automation strategies. It also introduces Edge Computing and Analytics, discusses Continuous and Adaptive Learning concepts, and explores data flow and use cases for Edge Analytics. Finally, the book concludes with a chapter on IoT Data Analytics for self-driving cars, using the CRISP-DM framework for data collection, modeling, and deployment.
By the end of the book, you will be equipped with the skills and knowledge needed to extract valuable insights from IoT data and build real-world applications. What you will learn
Explore the essentials of IoT Data Analytics and the Industry 4.0 revolution.
Learn how to set up the IoT Data Analytics environment.
Equip Python developers with data analysis foundations.
Learn to build data lakes for real-time IoT data streaming.
Learn to deploy machine learning models on edge devices.
Understand Edge Computing with MicroPython for efficient IoT Data Analytics. Who this book is for
If you are an experienced Python developer who wants to master IoT Data Analytics, or a newcomer who wants to learn Python and its applications in IoT, this book will give you a thorough understanding of IoT Data Analytics and practical skills for real-world use cases. Table of Contents
1. Necessity of Analytics Across IoT
2. Up and Running with Data Analytics Fundamentals
3. Setting Up IoT Analytics Environment
4. Managing Data Pipeline and Cleaning
5. Designing Data Lake and Executing Data Transformation
6. Implementing Descriptive Analytics Using Pandas
7. Time Series Forecasting and Predictions
8. Monitoring and Preventive Maintenance
9. Model Deployment on Edge Devices
10. Understanding Edge Computing with MicroPython
11. IoT Analytics for Self-driving Vehicles
Learn how to build an IoT Data Analytics infrastructure.
Explore advanced techniques for IoT Data Analysis with Python.
Gain hands-on experience applying IoT Data Analytics to real-world situations. Description
Python is a popular programming language for data analytics, and it is also well-suited for IoT Data Analytics. By leveraging Python's versatility and its rich ecosystem of libraries and tools, Data Analytics for IoT can unlock valuable insights, enable predictive capabilities, and optimize decision-making in various IoT applications and domains.
The book begins with a foundation in IoT fundamentals, its role in digital transformation, and why Python is the preferred language for IoT Data Analytics. It then covers essential data analytics concepts, how to establish an IoT Data Analytics environment, and how to design and manage real-time IoT data flows. Next, the book discusses how to implement Descriptive Analytics with Pandas, Time Series Forecasting with Python libraries, and Monitoring, Preventive Maintenance, Optimization, Text Mining, and Automation strategies. It also introduces Edge Computing and Analytics, discusses Continuous and Adaptive Learning concepts, and explores data flow and use cases for Edge Analytics. Finally, the book concludes with a chapter on IoT Data Analytics for self-driving cars, using the CRISP-DM framework for data collection, modeling, and deployment.
By the end of the book, you will be equipped with the skills and knowledge needed to extract valuable insights from IoT data and build real-world applications. What you will learn
Explore the essentials of IoT Data Analytics and the Industry 4.0 revolution.
Learn how to set up the IoT Data Analytics environment.
Equip Python developers with data analysis foundations.
Learn to build data lakes for real-time IoT data streaming.
Learn to deploy machine learning models on edge devices.
Understand Edge Computing with MicroPython for efficient IoT Data Analytics. Who this book is for
If you are an experienced Python developer who wants to master IoT Data Analytics, or a newcomer who wants to learn Python and its applications in IoT, this book will give you a thorough understanding of IoT Data Analytics and practical skills for real-world use cases. Table of Contents
1. Necessity of Analytics Across IoT
2. Up and Running with Data Analytics Fundamentals
3. Setting Up IoT Analytics Environment
4. Managing Data Pipeline and Cleaning
5. Designing Data Lake and Executing Data Transformation
6. Implementing Descriptive Analytics Using Pandas
7. Time Series Forecasting and Predictions
8. Monitoring and Preventive Maintenance
9. Model Deployment on Edge Devices
10. Understanding Edge Computing with MicroPython
11. IoT Analytics for Self-driving Vehicles
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Szczegóły książki
- ISBN Ebooka:
- 978-93-555-1575-9, 9789355515759
- Data wydania ebooka :
-
2024-10-10
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