Data Science Versus Computer Science: What’s the Difference?

Data science and computer science careers share some overlapping principles and tools, but they ultimately have very different areas of focus. Here’s what you need to know as you enter the field.
Headshot of author Matt Przybyla
Matt Przybyla
Expert Contributor
February 22, 2022
Updated: April 6, 2022
Headshot of author Matt Przybyla
Matt Przybyla
Expert Contributor
February 22, 2022
Updated: April 6, 2022

Data science and computer science often go hand-in-hand, but what makes them different? What do they have in common? After holding several different roles in data science departments at various companies, I have discovered some general qualities common to the data science process, along with how computer science is incorporated into that process as well. Anyone who currently works in or who is interested in entering either field should note the differences between these two disciplines, as well as when one requires concepts and principles from the other. 

Usually, a data scientist will benefit from learning computer science first and then specializing in machine learning algorithms. Some data scientists, however, start by jumping straight into statistics before learning how to code, focusing on the theory behind data science and machine learning algorithms. That was my approach, and I learned computer science and programming afterward. 

That being said, does a data scientist really need to understand computer science? The short answer is yes. Although computer science encompasses data science and is especially critical to artificial intelligence, I believe the main component of computer science is software engineering. Here, I will outline the differences between these two disciplines and their practice, as well as their respective similarities. I will also dive deeper into the focus of each field, including common tools, skills, languages, steps and concepts.

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What Does a Data Scientist Do?

So, what does a data scientist actually do? We hear the buzzwords often in the tech industry, but are those actually the keywords we employ in our everyday work? The answer is both yes and no. 

Undoubtedly, I employ many main tools and languages at least daily. As a data scientist, I’m required to explore the company’s data while also determining how that data affects a product. Ultimately, any data scientist will be encouraged to study current data, find new data and solve business and product issues, all with the use of machine learning algorithms (e.g., random forest). Although computer scientists can solve some of these same problems, for the sake of the title “data scientist,” the role requires someone who is solely focused on machine learning algorithms as the method of making an otherwise manual process not only more efficient but also more accurate.

Here are some of the steps of the data science process that a data scientist can expect to employ.

What Does a Data Scientist Do?

  • Explores current data, as well as finding new data.
  • Uses SQL to query and understand the company data.
  • Uses Python or R to explore data in a data frame or something similar.
  • Performs exploratory data analysis using libraries like pandas_profiling.
  • Isolates the business question and possible impact a model should have for success.
  • Searches and runs base machine learning algorithms to compare against the null or current process.
  • Optimizes the final or ensemble of algorithms for the best results.
  • Displays results with some type of visualization (e.g., Seaborn, Tableau)
  • Works alongside a computer scientist, perhaps, or an MLOps engineer.
  • Deploys and predicting with your final model in the company ecosystem.
  • Summarizes improvements.

​​​​As you can see, this process can sometimes be shared with others like artificial intelligence engineers, data engineers, computer scientists, MLOps engineers, software engineers and so on. What makes the data scientist’s role unique is the focus on machine learning theory and its effects on a business problem.

And here are some of the tools that a data scientist can expect to employ.

What Tools Does a Data Scientist Use?

  • SQL
  • R, SAS
  • Python
  • Tableau
  • Jupyter Notebook
  • PySpark
  • Docker
  • Kubernetes
  • Airflow
  • AWS/Google

Although the data science process is fairly set in stone, much like the scientific method is, the tools that a data scientist uses are open to negotiation. That being said, I would say most data scientists primarily use SQL, Python, and a Jupyter Notebook or something similar because these tools or languages can be applied to any business. Some companies, however, will have certain preferences or requirements that necessitate using Google Data Studio over Tableau, for example. 

 

What Does a Computer Scientist Do?

Although the field of computer science is more widespread and varied than the specific job title of computer scientist, some roles out there carry this name. Despite that, computer science jobs tend to entail software engineering specifically. Other tasks that could fall under the computer scientist umbrella include, but are not limited to, database administration, hardware engineering, systems analysis, network architecture, web development and a plethora of IT roles.

This variety makes a computer scientist role a little more difficult to define precisely, which is similar to data science’s inclusion of machine learning operations, data engineering, data analytics, and so on. Ultimately, you and the company you work for will have to define your role in computer science. Looking at a job description, of course, is an easy way to find out what any specific subrole is like.

Here are some of the steps of the computer science process a computer scientist can expect to employ.

What Does a Computer Scientist Do?

  • Understands the business, data, products, and of course, software.
  • For a specific problem, defines the requirements.
  • Understands and designs the system and software.
  • Implements the process and carries out unit testing.
  • Understands how the software will be integrated and how it affects the system.
  • Oversees operations and maintenance.

Although this process is not exactly like that of a specialized data scientist, it still shares some of the broader aspects of a more technical process, including but not limited to understanding software, data and implementing an improvement and thereafter analyzing and reporting on its effect.

And here are some of the tools and lanagues a computer scientist can expect to employ.

What Tools Does a Computer Scientist Use?

  • IDEs
  • Testing software
  • Python, and other object-oriented programming languages
  • Slack
  • Amazon
  • Notes
  • Atom
  • Visual Studio
  • Microsoft Azure
  • GitHub
  • Atlassian

A computer scientist can expect to employ a broad range of tools and languages. Once again, the toolkit depends on your area of focus — is it software engineering, is it network analysis, is it IT? Hopefully, you can find a role out there for you that not only fits your skills, but also one that you prefer to do. 

 

Data Science Versus Computer Science: Similarities and Differences

Now that we have discussed the main qualities and expectations of these two roles, we will explore both the similarities and differences between them. Of course, there are more points to be discussed, but these are some of the main ones based on my experience.

Here are the similarities that you can expect between the two roles.

Data Science Versus Computer Science: Similarities

  • Both require an understanding of the business domain and its products.
  • Both require working knowledge of the company’s data.
  • Both roles usually require fluency with the use of Git or GitHub.
  • Both follow a systematic approach to the scientific processes.
  • Both are expected to be leaders in technology.
  • Both usually require proficiency in at least one programming language.
  • Both can start in one role and switch to the other.
  • Both are cross-functional.

And here are the differences that you can expect between the two roles.

Data Science Versus Computer Science: Differences

  • Data scientists focus on machine learning algorithms, whereas computer scientists focus on software design.
  • Computer science encompasses more information and the roles offer more variety.
  • The necessary education is different for each, usually reflected in the differences between a computer science and a data science degree.
  • Data scientists usually have a background in statistics, whereas computer scientists have a background in computer engineering.
  • Computer scientists are usually more automation- and object-oriented-focused.
  • Data scientists often work more closely with product managers or other business-facing roles.

Because these roles are both very inclusive of other subroles, they may differ vastly from one another at one company but be surprisingly similar at another.

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Data Science Versus Computer Science: An Overview

As you can see, these positions require different skills, tools, and languages; however, they also share some of those same qualities. The main goal of a data scientist is to solve business problems using machine learning algorithms, while the main job of a computer scientist is either the direction of object-oriented programming and software engineering or managing IT, which requires a general working knowledge of everything computer-related in a business.

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