Humans have been bombarded with data ever since their inception and with the growth in technological means of obtaining data, more and more data is still being discovered.
Data Science is the study of data whereas the Internet of Things is a network of smart and connected devices.
- The Internet of Things (IoT) is a network of physical objects that can communicate with each other, while data science is the study of data.
- IoT collects data from various sources, while data science analyzes and interprets data to extract insights.
- IoT is more focused on the collection and transmission of data, while data science is focused on the analysis and interpretation of data.
Internet of Things vs Data Science
The Internet of Things (IoT) describes the network of physical objects—“things”— that are embedded with sensors, software, and other technologies. Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices.
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The Internet of Things is a very large network of devices that are embedded with sensors and the necessary hardware and software to connect to the internet through
which they share information across various consumer, commercial, or government communication networks. It is a very broad term including all sorts of heterogeneous devices.
Data Science is a field of study whose primary focus is to extract data from very large data sets and make the extracted information useful and actionable for a wide variety of purposes in varied domains.
It is helpful in the generation of insights from otherwise less useful and tough to analyze data.
|Parameters of Comparison||Internet Of Things||Data Science|
|Meaning||It is a term used to describe internet-enabled interconnected devices that share information.||It is the science dealing with the collection, analysis, and interpretation of data from unorganized Big Data.|
|Application||It is used for the creation of technology that can learn constantly without human effort.||It aims at analyzing a given set of data and applying the inference in strategies that develop humankind.|
|Subject of Analysis||The internet of things deals with analyzing machine-generated data.||Data Science deals with both human and machine-generated data.|
|Use||It is used as a component of machine learning and also to monitor smart devices.||Data Science is extensively used to collect smart information from big sets of data that contain unusable information.|
|Time Period||The Internet of Things is made up of smart devices that process and transmit data quickly or in-real time.||Data Science is predominantly human-analysis and is, therefore, time-consuming.|
What is Internet of Things?
The Internet of Things is a very large collection of physical objects or devices that have the ability to gather data, even it is very small data, and transmit it to other devices over the internet.
This is why it is called the internet of things as it uses the internet to create a smart network of things that gather data from all over the world that
enhances learning and machine capabilities and aims at doing it to a point where human intervention would not be required.
The internet of things is very heterogeneous and it contains machines ranging from mobile phones, smartwatches, home pods, and other devices with sensors and the ability of transmission.
Home systems that form a network of control across a house are an example of a much smaller network and the Internet of Things is just like that but spanning the whole world instead.
The internet of things is applied in various different sectors such as consumer electronics, commercial computers, industrial devices, etc. It is a constantly growing network.
The fast expansion of the internet of things has raised questions around privacy and security because in the end, digital devices can still be hacked and private data can be stolen.
What is Data Science?
Data science is a field of study that has combined many other fields of study such as mathematics, statistics, computer science, etc.
It uses various data handling techniques to make data from very big and unorganized sets easier to understand and more direct. The term Data Science was first used in 1985 in China as a replacement for Statistics.
Tradition data collection and processing methods can not be applied to very large fields of data to produce meaningful results therefore a lot of more efficient techniques are introduced in the field of data science.
It is a useful field of study as strategies that can prove to be very efficient and innovative can be crafted using inference from data science.
Data Science is very useful for business organizations as well as they can figure out methods of cost-cutting and better utilize their budget.
Big Data is the term used to refer to the largest sets of unfiltered data that has been produced in the digital era that has the potential to be incredibly useful if it is sorted and important conclusions are drawn from it.
Data Science treats Big Data and data scientists convert this scattered information into useful insights for organizations to plan accordingly.
Main Differences Between Internet of Things and Data Science
- The internet of things is a collection of tangible physical devices whereas Data Science is a field of study and is intangible.
- Internet of things is populated by machines and uses machine learning and artificial intelligence whereas Data Science is a human job using statistics.
- The internet of things is digital and is very fast or almost in real-time whereas data science is very time-consuming.
- The data collected by the internet of things has also been generated by machines whereas in Data science, the data analyzed can be from any source.
- The internet of things is dependent on the internet and connectivity is what brings it to life, whereas Data Science has no definite need for the internet.
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Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.