Data Science vs. Computer Science: What’s the Difference?

Data science is the practice of generating valuable insights from data using techniques from various areas, such as machine learning, math, statistics, and computer programming. Businesses across all industries can use these data-based insights to drive improved, informed decisions. 

Computer science is a larger field that encompasses everything related to computers' work and the continued development of their capabilities. Although computer science largely revolves around software, it also includes areas such as human-computer interaction, networks, programming, databases, cybersecurity, artificial intelligence, and machine learning. 

Learn more about data science vs. computer science, including their impact on various industries and details on a variety of careers in each field.

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Data science

Data science is relevant in any industry or company that has access to data that it wants to transform into actionable insights. Data allows leaders to make data-driven decisions, identify problems and growth opportunities, and improve outcomes. 

Data science involves four key stages: data collection, data processing, data analysis, and communication. 

• Data collection: During data collection, you gather data from various sources. It’s essential you have tools in place for storing data and preparing it for processing.

• Data processing: Data processing involves cleaning data to verify its accuracy and quality as part of the preparation needed for analysis. 

• Data analysis: During data analysis, you use techniques such as machine learning and predictive analytics to extract information from the data.

• Communication: A key component of data science is communicating your findings. Data visualizations are crucial for taking complex information and presenting it in tables, graphs, and charts so it’s easier to understand.

Who uses data science?

A range of industries use data science, which can provide value anywhere organizations collect data. Businesses can use data science to develop sales forecasts, learn more about their customers to offer more personalized experiences, improve operational efficiency, and identify growing trends within their industry. 

Data science holds power that expands beyond business. In health care, data science helps monitor and treat diseases, enables more efficient lab reporting, and can even improve a machine’s ability to analyze medical imaging. In the oil and gas industry, data science allows companies to monitor their systems, identify maintenance needs to improve worker safety and locate drilling sites.

Data scientists are in high demand, and employment in this field will likely increase by 36 percent in the decade spanning 2023 to 2033 [1]. Data scientists aren’t the only professionals who use data science techniques. You can find job opportunities in this field with titles such as data analyst, data architect, and machine learning engineer.

Advantages of data science

Implementing data science can be quite rewarding. Instead of relying solely on intuition and leaving business decisions up to chance, data science helps you make informed decisions that are more likely to lead to positive outcomes. These outcomes can include increased operational efficiency, innovation, and revenue.

Disadvantages of data science

Data science does come with some potential challenges. Organizations looking to increase their use of data science will likely experience an increase in costs, as it can be expensive to acquire the necessary infrastructure and staff to handle your data. It’s essential that you also secure data, ensuring its safety from breaches and cyber threats. Working as a data scientist comes with unique challenges as well. You must be able to manage and prepare data from various sources and eliminate bias from your algorithms for accurate results.

Computer science

The field of computer science encompasses many subjects directly related to computers. It includes theoretical principles as well as the actual development and implementation of technology across many different fields of computer science. Computer science focuses on subjects including artificial intelligence, software systems, data structures, computer architecture, algorithms, numerical analysis, and more. 

Software is perhaps the most predominant area of computer science, as software is what allows humans and computers to interact with one another. Software plays an important role throughout computer science, and with the help of artificial intelligence, computer scientists are finding ways to develop automated software that can operate independently. 

Who uses computer science?

Considering the extensive use of technology today, the effects of computer science are prevalent in virtually all industries. While the technology industry certainly has a need for computer science professionals, such as software engineers, web developers, cybersecurity analysts, data scientists, and machine learning engineers, you can find many opportunities in other fields as well. Many industries, including health care, education, government, and finance, lead the way in employing data scientists. Now is a great time to pursue a career in the field, with an estimated 26 percent employment growth in computer science from 2023 to 2033 [2].

Advantages of computer science

Computer science is responsible for many powerful and beneficial developments in the modern world. Computer science has enabled easier access to learning through the internet, simplified life through automation, and made communication simpler through applications on your phone. The development of computers and technology within computer science drives much of the innovation around us. 

Disadvantages of computer science

The field of computer science faces ethical challenges as technological advancements present opportunities for the spread of misinformation and misuse of personal information, and questions as to whether or not artificial intelligence will replace people's jobs arise. While some states have digital privacy laws, technology often develops faster than regulations can be put in place, leaving organizations and computer science professionals responsible for making ethical decisions.

Can I become a data scientist with a computer science degree?

If you’re interested in working in data science or computer science, you have several options that can help you build the skills necessary to succeed. Whether you’re interested in pursuing a career in data science or computer science, a bachelor’s degree in either area can help you qualify for a job, as can other similar degrees.

You can also find boot camps, which are shorter-term options for gaining relevant skills for the specific career you want to pursue. Additionally, online courses, such as those offered on Coursera, serve as a great resource for growing your skills in data science, computer science, programming languages, software engineering, and many other specializations.

Data science vs. computer science: Which field pays more?

Data scientists earn a median annual salary of $108,020, compared to computer scientists, who earn a median annual salary of $145,080 [1,2]. Your location, education, industry, and experience may affect the salary you earn.

Getting started in data science or computer science with Coursera

Data science is a field focused on gleaning valuable insights from data, while computer science offers a broader scope that involves the various aspects of computer systems. 

On Coursera, you can find highly rated professional certificates and develop skills for careers in data science and computer science. With IBM’s IBM Data Science Professional Certificate, you’ll learn the tools, languages, and libraries used by professional data scientists, including Python and SQL. 

You can also earn an IBM Full Stack Software Developer Professional Certificate, where you’ll develop with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap.

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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