Challenges of IoT Applications in Data Science
Apart from all the above applications of IoT , there are also a few challenges that has to be overcome before it becomes a perfect choice. Some of the challenges faced are listed below:
- Data Storage and Analysis: Iot devices carry with huge amounts of data which is very expensive and challenging to store and process large volumes of data. Hence, effective solutions are needed to store and process lots of data.
- knowledge discovery and computational complexities: As the data is massive, figuring out and understanding useful information is very hard because of its size and complexity. An action that could be taken to solve this would be putting away the data that is acquired from the working frameworks.
- Data Analysis and Visualization: It is often challenging to apply data science methods in a secure way and also it is difficult to present the data in a meaningful way.
- Too Much Data: Too much of data becomes overwhelming and difficult to filter out. Errors in data entry operations could lead to poor data quality. However, much categorized industry principles needs to be utilized to overcome this.
- Balancing Scale and Speed: Data from IoT devices can become challenging to analyze in large scale environments like cloud. The cloud may not be suitable for real time processing scenarios.
Data Science for Internet of Things (IoT) Applications
As we all know today’s digital world revolves around data. To deal with huge amounts of dynamic data, we adopt data science techniques with IoT devices to make lives easier and to handle scenarios taking immediate action.
In this article, we will discuss the different techniques of data science that can be used with IoT and the key applications of Data Science for IoT. Finally, we discuss the Challenges that are faced while applying Data Science to IoT applications. Let us start with “What is IoT”?
Table of Content
- What is IoT?
- What is Data Science?
- Difference between Traditional Data Science and IoT
- Data Science Techniques used in IoT applications:
- IoT Applications Empowered by Data Science
- Challenges of IoT Applications in Data Science
- Conclusion