There has been a common misconception and confusion about Data science vs Computer Science. Moreover, many people struggle to distinguish both them and pick the right stream.
There has been a misconception stating that data science and computer science are the same. But that is not TRUE!
Even though they belong to the same parent branch, they have unique characteristics and perks. To clarify your doubts and give a good understanding of Data Science and Computer Science, we have composed this post that will discuss all the nooks and crannies of Data Science and Computer Science in detail. So make sure that you don’t miss out on something.
Data Science vs Computer Science: In A Nutshell
Computer Science can be referred to as the study of computers and their minute technical aspects. These include studying the distant types of domains like computer architecture, computer programming, networking, databases, and the processes involved in functioning the computer as a system.
Data Science studies relational, non-relational, structured, unstructured, and semi-structured, from which the information is extracted to refine and sort the data for industrial uses. This involves using different technologies like data sorting, mining, parsing, optimizing, etc., which can be used to make the data sorted and easily readable by human eyes.
Here is a quick comparison between computer science and data science!
| Parameters | Data Science | Computer Science |
| Definition Application Merits Presence Industrial Usage | Refers to the study of data processing and optimization. Data Science is used for data analysis and data optimization. The benefit of computer science is that it has room for the growth and development of technology. Computer Science has been in the industry for a long time. The application of Computer Science is in all industries. | Refers to the study of computer networks, processes, architecture, and other fundamental concepts. Used for enhancing the overall computer power and use of it. The benefit of data science is that large volumes of data can be efficiently handled and processed. Data Science is a newly developed field and is spreading its roots. The application of Data Science is in industries where data is in bulk. |
Data Science vs Computer Science: Usage
Though both domains might seem similar to a certain extent, there are vast differences between them that may exploit each other. To understand this better, we will be going through each one of them on-by-one in this section.
Data Science: The Usage
Data Science is a recent field introduced in the industry, and the results it has been generating have been highly phenomenal. Data Science makes data processing easier for humans. This involves data sorting, data mining, data optimization, data visualization, and data analysis. All these things collectively make data science way more popular and demanding. Here are some of the applications and uses of Data Science:
- In Medical Healthcare: Used for visually analyzing and filtering the data collected from large DNA databases, disease databases, etc.
- In Insurance Companies: Data Science is used in insurance companies to predict the damage that can be caused to the victims.
- Social Media: Data Science targets a specific set of customers based on their actions that have been recorded and processed with complex mathematical algorithms.
Computer Science: The Usage
Computer Science has been in the industry since the revolution of computers started. The application and usage of computer science are so vast that more than 80% of the world’s enterprises rely on it. Computer Science has been taught in all the possibilities we can think about. From financial sectors to top IT firms, computer science has been constant.
It enables different industries to take a technical approach to their problem and save time and human effort. It helps design software, websites, networks, and databases that provide security to them concerning physical and virtual aspects. Here are some umbrella industries where computer science is used.
- In Healthcare: maintaining and developing a record system for patients and DNA, developing a remote-monitoring system for them as well.
- In the Financial Sector: Setting up networks for sharing data and digital assets over the internet, including transactions and digital records for blockchains.
- In the Meteorological Department: Used for predicting the time and the behavior of natural calamities like hurricanes and high tides.
Data Science vs Computer Science: Merits
Data Science and Computer Science each have their own benefits in their own respective fields. Once you master the art of combining both these domains, you can create a powerful system to do whatever a human brain is capable of imagining. But, it is necessary for us to understand how these domains have their own merits and how one stands out from another.
Data Science: Merits
Due to its technological advancements, data science has been on the rising side of the graph. The amendments that data science has brought in the field of computers have made businesses and all other industries eager to teach it in their core software. Here are some merits that Data Science has:
- Cost and Resource effectiveness: Identifying the patterns and the behavior of the data helps us to find ways to reduce the costs of the core business operations.
- Reduces Time: Processing large amounts of data manually takes plenty of time. Due to increased time and effort due to data science, all these operations are made simple and can be completed in a few clicks.
- Makes you stand out from competitors: Keeping up with the technological trends and including them in your business adds to your entire operation.
Computer Science: Merits
- Computer Science helps develop innovative and advanced solutions for all industrial needs, like setting infrastructure for companies, banks, etc.
- Computer Science as a field can help you reduce time and save human efforts by increasing the computational power and finding solutions for making the existing solutions efficient and powerful.
- It also helps identify the loopholes and vulnerabilities in your computers and applications to protect them from malicious cyber activities.
The field of Data Science and Computer Science has expanded exponentially, so the merit list can not be written all at once. But their above-stated points will give you a brief knowledge about the core merits of each field.
Data Science vs Computer Science: Career
Now that you have explained the basic understanding of how these fields differ, understanding the job roles for each would be simple. A survey done by Indeed in 2022 shows an increase in Data Scientist’s jobs by 364,000, so there is a shortage of Data Scientists in the industry. So one can prioritize acquiring data science skills, which will land you a salary of $95,000/year.
Compared to Computer Science, new branches will be the upcoming trend in the industry, like cyber security, blockchains, AI & ML, etc. These roles can land you a job for $131,490 as a fresher, which is extremely good for a fresher.
Data Science and Computer Science both are immensely growing in their respective fields. But the most demanding one is Data Scientist. As compared to Computer Science.
Data Science vs Computer Science: Final Verdict!
Data Science and Computer Science are proliferating with the increase in technological advancement, and they also have their benefits. We hope that you have got a brief understanding of our post.
Let us know in the comment section below which field has compelled you and what will be your approach to each area.
FAQs On Data Science vs Computer Science
Which one is better: Data Science or Computer Science?
To summarize everything, Computer Science is much easier than Data Science because it has several algorithms and mathematics involved.
Does Google hire Data Scientists?
Yes, Google hires data scientists and provides amazing CTCs as well. But you should have considerable knowledge and must have worked on projects.
Does data science require coding knowledge?
Yes, Data Science does require coding knowledge to some basic extent in python and other object-oriented languages.
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