With the advent of digital tools and technologies, the domains of studying digital technologies have also expanded and specialized. In this article, we will discuss data science vs computer science to discuss their similarities and differences. We will also discuss which domain among data science and computer science is best suitable for you.

What is Data Science?

Data science is the domain in which we study and practice scientific methodologies and algorithms to extract knowledge from available data. In data science, we usually study and practice statistics, probability, programming languages (Python/R/ SQL), machine learning, data visualization, and other concepts. Data science is closely related to data mining and big data technologies. 

What is Computer Science?

Computer science is the umbrella domain in which we study anything and everything computers and computing concepts. In computer science, we study logical architecture, design, and development of software applications and hardware devices that we use in different day-to-day life. The main areas of computer science include programming, algorithm design, computer architecture, operating systems, software engineering, compiler design, database management, human-computer interaction, etc. Modern computer science domains like machine learning, computer vision, artificial intelligence, cyber security, and data science are also considered subdomains of computer science.

Data Science vs Computer Science: Similarities

Although data science and computer science are often considered to be different fields, they have many similarities and dependence on each other. Following are some of the similarities between computer science and data science.

1. Programming: In computer science as well as data science, programming is a fundamental skill. In computer science, knowing programming languages is a must as you need to build software applications. In data science, you can thrive in your job without the knowledge of programming languages based on your job responsibilities. However, knowing programming languages like Python, R, SQL, and Rust will give you great power to develop algorithms and analyze data.
2. Mathematics: Both computer science and data science relies heavily on mathematics. You need to learn linear algebra, calculus, statistics, and probability theory for understanding algorithms, data analysis, and machine learning techniques.
3. Data manipulation: Software applications generate and transmit a huge amount of data. Computer science deals with data structures, algorithms, and efficient data processing techniques for generating and transmitting high-quality data. Data science focuses on collecting, cleaning, transforming, and analyzing data to extract meaningful insights. Hence, we can say that computer science is the data producer whereas data science is the data consumer in the data manipulation process.
4. Machine Learning: Both data science and computer science utilize machine learning techniques. Computer science employs machine learning algorithms to build intelligent systems like self-driving cars, robots, etc. Similarly, data science relies on machine learning techniques for predictive analytics, pattern recognition, and data-driven decision-making.
5. Statistics: Both computer scientists and data scientists use statistical methods to analyze and interpret data. Computer scientists often employ statistical techniques for analyzing experimental results and performance evaluation. Similarly, data scientists use statistical inference, hypothesis testing, and regression analysis for data modeling.
6. Database systems: Both data science and computer science involve working with databases. Computer science focuses on designing and managing efficient database systems. Data science involves querying and extracting data from databases for analysis and visualization.
7. Data visualization: Computer Science uses visualization techniques to represent complex data structures and algorithms. Data science employs visualizations to communicate insights and patterns discovered from data analysis.
8. Big data: Both data science and computer science are concerned with the challenges and opportunities presented by big data. Computer science addresses issues like data storage, retrieval, and distributed computing. Similarly, data science focuses on extracting value from large, complex datasets through techniques like parallel computing and distributed processing.
9. Problem-solving: Both data science and computer science have problem-solving as one of their core skills. Computer science involves designing algorithms and writing code to solve computational problems. Data science employs statistical and computational techniques to solve complex data-related problems and make data-driven decisions.
10. Ethical considerations: Computer science deals with privacy, security, and ethical concerns related to software development. Similarly, data science focuses on ethical issues around data collection, usage, and potential biases in algorithms and models.

Suggested reading: Is data science hard to learn?

Computer Science vs Data Science: What Are the Differences?

As discussed above, computer science and data science have many similarities. However, there are stark differences between the job responsibilities and activities in these domains. That’s why they have been classified into different domains. Right? 

Let us discuss some of the differences between computer science vs data science.

Data Science	Computer Science
Data Science focuses on data analysis, statistics, machine learning, and deriving insights from data.	Computer Science is a diverse domain. It encompasses a wide range of topics, including algorithms, data structures, programming languages, operating systems, database management systems, computer networks, artificial intelligence, and software engineering.
Data Science aims to extract knowledge and insights from data to support decision-making and solve real-world problems.	We use computer science to understand the principles of computation, develop efficient algorithms, and build robust software systems.
Data Science emphasizes on data processing, statistical analysis, data visualization, and machine-learning techniques.	Computer Science places a strong emphasis on theoretical concepts, algorithms, system design, and software development.
While both fields require mathematical knowledge, Data Science places more emphasis on statistical concepts, probability theory, and linear algebra for data modeling and analysis.	Computer Science typically requires a solid foundation in discrete mathematics, principles of computation, logic, and algorithms.
Data Science primarily focuses on data analysis, modeling, and extracting insights from the available data.	Computer Science focuses on data generation, transmission, and storage. It deals with designing and developing efficient data storage systems, database management, and data retrieval techniques.
Data Science emphasizes experimental design for hypothesis testing and evaluating the effectiveness of machine learning models and data-driven approaches.	Computer Science often focuses on designing and conducting experiments to evaluate the performance of algorithms and software systems.
Data Science is a specialized field with career paths such as data analyst, data scientist, machine learning engineer, and business intelligence analyst.	Computer Science offers a wide range of career paths, including software engineering, systems analysis, database administration, network administration, network security, etc.

Data Science vs Computer Science: Salary

Salaries in both data science and computer science can vary significantly depending on factors such as location, experience, education, and the specific industry or company. However, generally speaking, data science tends to offer higher average salaries compared to computer science. Why?

Data science is a specialized field where you need a postgraduate degree or even a Ph.D. to excel in your job. On the other hand, computer science works as a gateway to different job roles such as software engineers, systems analysts, database administrators, network administrators, etc.  You can start in most of these job roles after graduation. Hence, the average salary for these roles tends to be lower than for data science jobs.

Data science has seen a surge in demand due to the growing importance of data analysis and machine learning in various industries. Skilled data scientists are sought after for their ability to extract insights from data and drive data-driven decision-making. As a result, the salaries in the field of Data Science have been quite competitive.

• According to recent reports and salary surveys, the average salary for Data Scientists can range from around $95,000 to $150,000 or more per year, depending on the factors mentioned above. In some cases, experienced Data Scientists with advanced degrees or specialized expertise can earn salaries well above these averages.
• Computer Science professionals can work in various roles as discussed above. Salaries in these job roles range from around $60,000 to $120,000 or more per year, depending on the specific role and other factors.

Data Science vs Computer Science: Which is Better For You?

By now, you might have understood that data science and computer science both are great domains to work in. However, it might be hard to decide if computer science or data science is better for you due to their similarities and differences. 

At undergraduate levels, data science isn’t taught separately. You need to take an undergraduate degree in computer science or information technology to learn data science too. For pursuing data science, you can also choose an undergraduate degree in statistics. In recent years, some of the institutions have started offering courses in data science. Hence, you might get options to choose between data science and computer science at the undergraduate level too. Following are some of the points that you can consider to decide which of these domains is better for you.

• If you have a strong interest in data analysis, statistics, and extracting insights from data, you should choose data science. On the other hand, if you have a passion for programming, algorithms, and software development, you should choose computer science.
• Data science involves working with large and complex datasets. It also requires an aptitude for mathematics and statistical concepts. If you are interested in working with large datasets, you should choose data science. If you enjoy working on a wide range of computer science topics, including software engineering, artificial intelligence, computer networks, databases, or operating systems, you should choose computer science.
• If you are interested in designing and building efficient systems and applications that solve complex computational problems, you should choose computer science. On the other hand, if you are interested in applying data analysis and machine learning in specific domains, such as business, finance, healthcare, or marketing, you should choose data science

Although I have discussed points to select the best course for you, I suggest you take an undergraduate degree in computer science first. Then, you can choose a master’s degree in data science or other computer science domains as per your interests. This will help you to explore both domains as computer science degrees teach many data science topics. After getting an undergraduate degree in computer science, even if you get into data science, you will have a basic understanding of how operating systems, database management systems, and computers in general work. This will also help you design analytical processes in data science that maximize computing resources. 

Conclusion

In this article, we discussed the similarities and differences between computer science vs data science. To learn more about domains in computer science, you can read this article on data science vs software engineering. You might also like this article on Hungarian notation in programming. 

I hope you enjoyed reading this article. Stay tuned for more informative articles. 

Happy learning!

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