Difference between Traditional Data Science and IoT
The basic difference between the two types of data is that the traditional data is created by human whereas the IoT data is created by the machine. Collecting and organized data is a straightforward process in traditional data science and the content is consumed based upon the request whereas in IoT the data is continuosly pushed and seems never-ending. The continuos arriving data must be quickly analyzed and instant decisions must be taken. If the data processing is slowed down in IoT, the overall value of the data might be essentially reduced.
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