Networking is the process of connecting two or more computer systems, mobile phones, or even Internet of Things (IoT) devices. Switches, routers, and wireless access points are the primary fundamentals of networking infrastructure. The connected devices are able to communicate with one another as well as with other devices connected to different networks.
Data science is the study and practice of using scientific methods, processes, algorithms, and systems to extract knowledge from data, whether that data is organized or not.
Data Science VS Networking
Data science is concerned with the analysis, interpretation, and presentation of information and uses methods like machine learning, data mining, data storage, and visualization, whereas networking is more concerned with wired and wireless networks.
Data science deals with the analysis, upkeep, and processing of massive amounts of data, whereas networking is a field where data is transferred within networks.
Also Read: Top 6 Data Science Skills Every Organization Should Invest in for 2023
Advantages and Disadvantages
Both areas have various advantages and disadvantages that we must weigh carefully before choosing one over the other.
Networking is a well-established sector, and it shouldn’t be too difficult to obtain work in this area of expertise. It is well-known that landing a good job in the field of data science can be challenging due to the fact that employers expect candidates to not only excel academically but also have prior experience working in the relevant industry.
Data scientists need to be exceptionally smart and active because their career is quite demanding, even though Data Science has a quicker growth rate and is definitely more in demand than network administrator. Networking is for professionals who want to play it safe and easy, but data scientists need to be really active and smart.
Compared to employment connected to data science, which is predicted to rise by 15% in 2021, networking jobs are predicted to grow at a rate of only 6% by 2026. To choose a career between the two, it is abundantly evident how drastically different the work options are.
A networking entry-level job can bring in an average yearly compensation of $58,000, while experienced workers can make up to $117,000 per year. This is significantly less than what a data scientist would make. An entry-level data scientist’s annual salary is on average $98,233, according to PayScale. Therefore, as compared to a profession in networking, a career in data science is more rewarding.
Also Read: Cyber Security VS Data Science: What are the Similarities & Differences?
The Next Big Thing
Recently, there has been a growing tendency of networking professionals to transition into data science, despite the fact that the tools and technologies employed by the two fields are very unlike. A professional from any other discipline must be supported by a genuine and recognized certification because a career in data science necessitates extensive analysis and a statistical mindset.
The Top Certifications to Choose From are listed below:
- Data Science Certificate
- Microsoft Certified Solutions Expert (MCSE)
- Cloudera Certified Associate (CCA)
- Dell EMC Proven Professional certification program
- Certified Analytics Professional
- SAS Academy of Data Science
- Cloudera Certified Professional: CCP Data Engineer
Because of the aforementioned factors, experts all over the world favor data science, but the choice must be made in accordance with the preferences, areas of knowledge, and discretion of the individual. Any judgment must be based on a thorough analysis of all the factors and the determination of what is appropriate.