Data Science vs. Computer Science: Career Differences

Computer technology and big data impact many aspects of modern life, from the way we shop to how we navigate cities and connect with others via social media platforms. Data scientists and computer scientists play an integral role in creating these digitally driven tools, making them user-friendly, and tapping into their potential to help companies refine their products and services. That said, the precise roles of professionals in data science vs. computer science differ significantly in terms of output and required skill set.

Data scientists look for meaning in large swaths of data using tools such as data visualization, data mining, and predictive statistical analysis. They develop the infrastructures needed for analytics, testing, developing decision-making through machine learning, and refining final data products.

Computer science, on the other hand, refers to the study of computing design and architecture. Computer scientists develop computer hardware, software, and computing networks, working with tools that range from programming languages to artificial intelligence. They invent and design new approaches to computing or find innovative ways to use existing computing methodologies.

It’s worth noting that data scientists and computer scientists can have cross-functional roles. However, data scientists tend to work closely with top executives to gain insights into company goals. With this understanding they can strategize ways to leverage company data to improve products and services. Computer scientists, who typically have backgrounds in computer engineering, tend to focus on software design and automation.

Both jobs are integral to our modern world. A computer scientist might work as a software developer, creating the codes that underlie the mobile and computer applications we use to navigate, such as Google Maps. A data scientist might work to analyze the data generated by such apps. For example, tracking the most popular driving routes of Google Maps users can provide useful information to improve the functionality of the app.

While data science and computer science are distinct fields, the U.S. Bureau of Labor Statistics (BLS) combines the two in its projections. Professionals in both fields can expect a promising job outlook in the coming years. The BLS projects positions in data science and computer science to increase 22% between 2020 and 2030 — much faster than the 8% average projected for all occupations.

Data science experts can choose from a number of careers. The following positions represent some of the options professionals in the field can pursue:

Data analysts evaluate data to answer questions and help solve business problems. By examining trends and patterns in data, they help companies determine how to lower costs, price products, and improve customer retention. According to September 2021 PayScale data, data analysts have a median annual salary of around $62,000.

Computer systems analysts examine computer systems and procedures to determine their efficiency and effectiveness. Based on their findings, they innovate solutions to problems and leverage opportunities to make improvements. This involves researching new technologies to determine their costs and benefits, as well as making recommendations for infrastructure upgrades. In May 2020, computer systems analysts had a median annual salary of $93,730, according to the BLS.

Data architects create frameworks that manage an organization’s data. The frameworks they design define the processes used to create, acquire, store, control, access, and purge data. They collaborate with upper-level management and other data science experts to evaluate data needs and strategically leverage company data. Data architects have a median annual salary of approximately $122,000, according to PayScale data as of September 2021.

Applications architects design the major architectural elements of apps. This includes the infrastructure, user interface, and middleware. They also conduct design and code reviews and serve as the technical leaders of application development teams. Applications architects have a median annual salary of approximately $117,000, according to PayScale data as of September 2021.

Machine learning engineers research, build, and design artificial intelligence (AI) systems. This involves developing AI algorithms that can make predictions, choosing appropriate data sets and representation models, and determining data differences that may influence the performance of models. Machine learning engineers have a median annual salary of approximately $113,000, according to PayScale data as of September 2021.

The field of computer science offers an array of career opportunities for those with the right skills and expertise. Some in-demand positions in the field include the following:

Software developers design computer applications and programs. This involves analyzing users’ needs and creating software that meets those needs. Software developers also test and debug applications, making sure programs run smoothly. In May 2020, software developers had a median annual salary of $110,140, according to the BLS.

Computer hardware engineers find ways to increase the power and speed of computers. They accomplish this by researching, designing, and testing computer systems and their components. They are also responsible for updating computer equipment and maintaining compatibility between computer components and new software. According to the BLS, computer hardware engineers had a median annual salary of $119,560 in May 2020.

Computer network architects construct data communication networks such as intranets and local area networks (LANs) that can connect multiple users. This involves designing infrastructures that safeguard data, upgrading hardware and software, and troubleshooting problems. According to the BLS, computer network architects had a median annual salary of $116,780 in May 2020.

Database administrators construct systems that organize, store, and secure data such as shipping records, financial figures, and user account information. They also monitor databases to identify slowdowns, troubleshoot problems in the system, and back up data to prevent loss. Database administrators had a median annual salary of $98,860 in May 2020, according to the BLS.

Computer and information research scientists develop new uses for existing technologies to help solve problems in other fields such as science, business, and medicine. This involves studying computing problems and creating models and theories to fix them. It may also involve creating new software systems or computing languages. In May 2020, computer and information research scientists had an annual median salary of $126,830, according to the BLS.

To thrive in any of these careers, professionals in both fields need specific skills and knowledge.

Data scientists ultimately strive to give data meaning. For example, an e-commerce company like Amazon may rely on data scientists to develop tools for gathering and analyzing customer purchase and browsing data, allowing the retailer to make targeted suggestions as to what consumers should buy.

Individuals who want to excel in a data science career must master the following skills:

The first step for a data scientist is to identify the problem or question they’re trying to resolve. With this in mind, they can decide how to best present data. Critical-thinking skills are crucial to determining what metrics will most fully answer the data question at hand.

Data scientists must know how to manipulate data to create understandable visual representations that can be used to make data meaningful. Mathematical knowledge is needed, in particular a thorough understanding of statistical modeling.

The outcomes of data analysis usually must be relayed to other people, such as coworkers or senior company leaders. Verbal and written communication skills are key, as are visual communication abilities such as being able to create compelling and easy to understand charts and graphs.

Data scientists must frequently consider business risk when working with data and making proposals. Their work is not conducted in a vacuum. They must consider the overall purpose of their work and the data, such as capitalizing on certain customer purchase patterns.

All data scientists must possess certain technological skills, but different employers will have different demands. Python is the most popular programming language employed by data scientists. Others include R; SQL, which can also be combined with PHP or JavaScript for web applications; and Hadoop and Spark, two forms of open-source software from Apache that are used for big data development and testing.

Computer scientists design and perfect computer hardware, software, and networks. For example, a computer hardware engineer may design, develop, and test components like circuit boards and memory devices.

Individuals who wish to excel in a computer science career should master the following skills:

When finding solutions to problems, computer scientists may have to work on code that was developed by other people. They must have the analytical skills to not only evaluate code but identify potential problems and ways to improve it.

A computer scientist must be able to think outside the box to develop innovative solutions to problems. Often, the solutions don’t exist within traditional channels and frameworks, requiring them to develop novel approaches.

Computer scientists must be able to think critically when designing new processing methodologies. Effective problem-solving allows them to identify what will and won’t work, which saves time in the development and testing phases.

Like data scientists, computer scientists need knowledge of Python and SQL. They must further master other programming languages such as HTML/CSS, JavaScript, C++/C#, and Java.

When creating code, multiple team members often work on various parts of the code and then bring them together to create a final product. Computer scientists must be adept at source control, which ensures all parts come together in a uniform whole at the end.

The SDLC provides an overview of how software is produced. Computer scientists must understand the different needed iterations ahead of each new software release, starting from the minimum viable product (MVP) and moving to the final product.

Individuals who are interested in learning more about the differences between data science vs. computer science can benefit from exploring higher education options. An ideal program to investigate is the Maryville University online bachelor’s in data science. The program covers such in-demand topics as computer science, programming, predictive modeling, and data analysis. Relevant coursework includes Foundations of Data Science, Mathematical Statistics, Introduction to Python, and Introduction to SQL.
Learn more about Maryville’s online degree program, how to enroll, and how the program prepares students for careers in today’s rapidly progressing technology landscape.
Recommended Readings

Recommended Reading

The Future of Data Science and Important Skills for Data Scientists

What Is a Data Science Major, and What Can You Do With It? 

Why Are Data Scientists in High Demand?

Sources

Knowlab, “The Difference Between Computer Science and Data Science”

Harvard Business Review, “What Data Scientists Really Do, According to 35 Data Scientists”

Towards Data Science, “The Most in Demand Skills for Data Scientists”

The Balance Careers, “Top 10 Computer Science Jobs”

Bureau of Labor Statistics, “Computer and Information Research Scientists”

CIO, “Essential Skills and Traits of Elite Data Scientists”