Aspirants who want to work in the IT sector or software companies always look out to upskill themselves. Their main goal is to increase their employment prospects by adding new and emerging technologies to their skill set. However, many remain confused between data science and computer science courses. Though correlated, the difference between data science and computer science is huge.
Data science courses include studying data structures, data types, data classification methods, etc. On the other hand, computer science courses involve the study of computer architecture, its design, and applications. To get a deeper perspective on CS vs data science courses, let's do a detailed comparison between the two.
Difference between data science & computer science
The basic differences between data science and computer science have been iterated below:
Data Science vs Computer Science: Definition
Computer science includes an in-depth study of the software and hardware components of a computer. It also sheds insights on networking, operating systems, programming languages, and data manipulation through different programs and algorithms. Therefore, we can consider data science as a part of computer science.
Data science focuses on extracting, analysing, and interpreting data. Though programming languages like R and Python are also used here, their job is mainly to interpret or make the data easily comprehensible.
Data science concepts are often used to forecast the trends in different sectors or the entire economy. Historical data is essential while identifying current and future market trends.
Data Science vs Computer Science: Uses
Computer science is mainly related to developing new technologies and improving existing ones. For example, companies employ computer science graduates to develop better software for managing their backend operations.
Data science is used for managing and analysing data. It is done using advanced data analysis tools and programming languages.
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Data Science vs Computer Science: Evolution
The evolution of computer science can be traced back to when Charles Babbage first invented computers. On the other hand, data science has evolved recently as a branch of computer science.
Statistics can be considered the parent branch of data science. However, statistical methods and analytical models existed long before the evolution of data science as an independent subset of science.
As a result, we have a long history of computer science in academics. In contrast, data science courses and data science certifications have been introduced by academicians only in the last decade or so.
Data Science vs Computer Science: Profession
Aspirants who study computer science are considered computer science professionals. However, they can continue to acquire skills in their preferred areas of interest.
For example, computer science professionals can pursue a career in networking, software development, testing, data security, and other related fields.
Data science professionals can acquire new skills to become data analysts, data scientists, data architects, data engineers, etc. Therefore, we can safely say that both fields offer tremendous job opportunities to people interested in working in the field of science and technology.
Related Article: Data Scientist Job Description
Data Science vs Computer Science: Applications
Data science applications are mainly used in industries where data is important. For example, data is the backbone of e-commerce industries, stock markets, and various other industries.
However, computer science applications can be used in all companies to leverage technology. For example, a payroll system can be used in all industries to manage employees' salaries.
Key differences between data science and computer science
The below points highlight the key differences between data science and computer science from an academic or professional point of view:
CS vs Data Science: Subjects
The main subjects of computer science include algorithms, data structures, programming languages, computer architecture, front- and back-end technologies, and more. The key subjects of data science include calculus, statistics, advanced statistical concepts, data engineering, and more.
CS vs Data Science: Knowledge Areas
Computer science teaches us how computers function and how processors are designed to provide optimum performance while multi-tasking and multi-threading. It reveals how memory can be managed and how systems can be built from scratch using different technologies like Java, C++, PHP, HTML5, and more.
Data science teaches us to retrieve, store, and manage data. It also helps us understand how data can predict future trends. The use of data in making different business decisions can also be studied in data science courses and data science certifications. How to avoid data redundancy, how to ensure data security, and management and manipulation of data are some important lessons that data science courses teach us.
CS Vs Data Science: Courses
Computer science degrees are available as graduate and master-level courses. Aspirants can also apply for computer science certifications to learn new and evolving technologies like AI, cloud computing, etc.
Graduate, master, and diploma-level data science degrees are available today. Data science certifications include the study of R programming language, Python, Oracle, and other evolving programming languages that are related to data science. Machine learning is also a critical component of data science course.
Conclusion
Computer science and data science are the two most prominent fields as far as academics and career growth are concerned. The huge job scope for data science and computer science certification holders has increased their demand manifold. However, aspirants must obtain these certifications from reputed institutes.
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