Computer technology and big data impact many aspects of modern everyday 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 these two professions differ significantly in terms of output and required skill set. This article examines the difference between data science vs. computer science and explains what it takes to work in these in-demand fields.
The Importance of Data Scientists and Computer Scientists to Modern Society
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.
Both jobs are integral to our modern world. For example, a computer scientist may 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 may 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.
These next sections define the varied skills needed in data science vs. computer science.
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:
- Critical Thinking: 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 used to determine what metrics will most fully answer the data question at hand.
- Mathematical Knowledge: 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.
- Communication: The outcomes of data analysis must usually be relayed to other persons, such as coworkers or senior company leaders. Verbal and written communication skills are key, as are visual communication abilities like creating charts and graphs.
- Risk Analysis: Data scientists must frequently consider business risk when working with data and making subsequent proposals. Their work is not conducted in a vacuum. They must take into account the overall purpose of their work and the data, such as capitalizing on certain customer purchase patterns.
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:
- Analytical 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 the code but identify potential problems and ways to improve it.
- Creativity: A computer scientist must be able to think outside the box to develop innovative solutions. Often, the solutions to their problems don’t exist within traditional channels and frameworks, requiring them to develop novel approaches.
- Critical Thinking and Problem Solving: 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.
- Understanding Source Control: When creating code, multiple team members will often work on various parts of the code and then bring these together to create the final product. Computer scientists must be adept at source control, which ensures all parts come together in a uniform whole at the end.
- Understanding the Software Development Life Cycle (SDLC): 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.
Careers in Data Science vs. Computer Science
While data science vs. computer science may represent distinct employment fields, the U.S. Bureau of Labor Statistics (BLS) combines these two areas in its projections. The outlook in terms of career prospects is positive. According to 2018 BLS data, job growth of 16% — much faster than average — is expected for the category of computer and information research scientists from 2018 to 2028. These careers also pay very well, upwards of six figures. The BLS reports the median pay in 2018 for this employment group as $118,370 per year.
How Graduates Benefit from a Bachelor’s in Data Science
Individuals who are interested in exploring data science vs. computer science can start building the knowledge they will need by pursuing higher education. 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, Statistical Modeling, Introduction to Python, and Introduction to SQL.
Learn more details about this online degree program, how to enroll, and how it prepares students for careers in today’s rapidly progressing technology landscape.