On one side, we have all-encompassing Computer Science, on the other, we have the recent subject of Data Science, which has gained a lot of popularity. In this post, we will compare Data Science vs. Computer Science, discussing differences, course fees, job profiles, market opportunities, and expected salaries. If you are interested in making a career in either of the two or want to learn more about the topic, read till the end.
Compare Data Science vs Computer Science
You must have heard terms such as Artificial Intelligence, Machine Learning, Neural Networks, Big Data and Deep Learning. These terms can be heard in Data Science class and Computer Science. But what are the key differences? That is what we are going to unravel in this guide.
We will compare Data Science vs Computer Science on the following parameters.
- Key differences
- Course fees
- Skills gained and required
- Job Profile
- Scope and Salary
Let us talk about them in detail.
1] Key differences
Both Computer Science and Data Science are similar but have some key differences. Computer Science or CS enables one to study computer hardware and software. You can learn how to create software, manage databases, implement firewalls, configure networking devices, and program websites and webpages. Not only that, but you can dig deep into each of those concepts to learn and make a career out of it.
On the other hand, Data Science combines several academic disciplines or professional concepts to manage and understand data. You will use mathematical concepts such as statistics to understand data and computer technologies are there to assist you. Then, the data that you fetched will be used to understand the populace and will be used for Machine Learning.
So, after this analysis, we can conclude that computer science is the study of computer hardware and software and data science uses these technologies to study data.
2] Course fees
Since both data science and computer science are in demand, a lot of universities offer their courses. Due to this, the course fees vary a lot. However, a lot of conservative universities refrain from adding Data Science as a course. That is why, a lot of private universities charge a lot for the course as they are in demand and the supply is relatively low. However, if you can enroll in a good university, anywhere in the world, the course fee will be almost similar.
You can look at some free online courses from top universities to expand your horizons.
3] Skills gained and required
If you are a good program or are interested in programming you will most likely be able to adapt to both the courses. However, neither of the fields, just require programming skills. A Data Scientist must be thorough with mathematical concepts including but not limited to Statistics. They must also know or learn data visualization skills and technology. Do keep in mind that, if you know neither of the skills mentioned earlier, but you are interested in data science, do not worry as you will learn all of them after enrolling in a good course.
When it comes to Computer Science, one can camouflage their weaknesses and make a career in something they are actually good at. If you don’t like programming, no problem, go towards, computer networking. If you don’t want to work on boring databases and are interested in learning generative AI, sure enough, become a prompt engineer. Therefore, computer science is a vast ocean from which all you need is a corner.
4] Job Profile
Now, let us talk about what kind of roles you will get once you complete these courses. As mentioned earlier, Computer scientists can work on various subjects. They can manage computer networking, work on databases, code software, manage the IT infrastructure of a corporation, and more. So, there are various job profiles and roles that they can get depending on their choices and the requirements of the company they are working for.
Whereas, Data Scientists, is a niche that is focused on the business aspect of an organization. They fetch data, analyze it, and assist in making important business decisions based on it. Not only that, the data can be used in various research and development fields, especially in machine learning.
5] Scope and Salary
If you are well-versed in the current job environment, you would know how lucrative both Computer Science and Data Science fields are.
Let us talk about Data Science first. Many companies are demanding Data scientists as there is a huge flow of data coming their way. And only someone, who is a master in statistics, data visualization tools, and has an understanding of data is desperately needed. If you are a beginner, you can expect a salary somewhere around $60,000 to $80,000 annually in the US. But, once experienced, you can expect a salary around $100,000 annually.
However, in the Computer Science realm, there are various carriers, such as prompt engineer, DBA, programmer, and network administrator; hence, there are various salary brackets. Nonetheless, if we talk about developers, a good one can earn up to $80,000 annually during the beginning of their career and then can go up to $100,000 per annum.
In conclusion, we can say that both fields are pretty lucrative. However, if you are certain that you want to be a Data Scientist, go for it; you won’t regret it. But, in case, you are in some dilemma, go for Computer Science then you can choose a field you like.
Which career is best Data science or Computer science?
Data Science is a growing profession and almost every single organization requires a bunch of data scientists. On the other hand, Computer Science is evergreen. Programmers, DBAs, Network Admins, and Cloud experts will always be required. So, yes, Data Science is in demand and will be in the foreseeable future, but CS will never go out of demand.
Why Computer science is better than Data science?
Computer Science includes many things in it, whereas, Data Science is a very particular niche. If you are a Data Scientist, you will deal with data and make meaning out of it. That is why, a lot of consensus believes that CS is better as it is ever-evolving. However, both fields are equally good and lucrative.