Networking is the interconnection of systems, mobile phones, and IOT devices. There are three basic elements to networking: switches, routers, and wireless access points. All devices connected can communicate with one another and across networks.
Data Science is the application of scientific methods, algorithms, and systems to extract meaningful insights out of both structured and unstructured data.

Networking vs. Data Science
Networking is about wired and wireless networks, while Data Science involves expertise in mathematics, statistics, and computer science disciplines. It uses techniques such as machine-learning, data mining and data storage and visualization.
Networking is the domain where data is exchanged among networks, while data science is the study of analyzing, maintaining, and processing large amounts.
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Advantages and disadvantages
Before deciding on one, there are many pros and disadvantages to both fields.
Networking is a very well-established field. Finding a job in this field is easy. It can be difficult to find a job in Data Science. Companies expect you to be proficient in your course and have previous experience in the field.
Networking is a career for professionals who want to be safe and easy. Data scientists, however, need to be exceptionally smart and active because the career is extremely demanding. Data Science has a faster growth rate than network administrator and is more in demand.
According to estimates, network jobs will grow at a rate only 6% by 2026. Data Science jobs are expected to grow at 15% by 2021. It is easy to see the vast differences in the job opportunities available.
An entry-level job in networking can pay you an average annual salary $58,000, while those who have worked for years can earn up to $117,000. This is a hugely lower salary than that of a data scientist. According to PayScale, an entry-level data scientist earns an average salary $98,233 per year. Data Science is a more lucrative career than a career as a networker.
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Emerging Trend
A rising trend in networking professionals moving into Data Science has been observed. However, the tools and technologies used in both domains are very different. A career in Data Science requires a lot analysis and a statistical mind. Any professional from another field must have a valid and recognized certification.
Here’s a list with the top certifications you can choose from:
Program for professional certification from Dell EMC Proven
Certified Analytics Professional
SAS Academy of Data Science
Microsoft Certified Solutions Expert (MCSE).
Cloudera Certifi