Data Science vs. Computer Science: Goals, Roles & Salary in 2024

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Modern enterprise environments are dominated by IT and data, and you’ll see lots of debate around what the role of data science vs. computer science is. The truth is that both data scientists and computer scientists are essential for maintaining the IT infrastructure and the data that it generates on a daily basis.

In this article, we break down the difference between data science and computer science. We also examine the expected roles and responsibilities and find out the average salary practitioners in each field can expect to make.

Key Takeaways

  • Data science is about learning the expertise necessary to extract insights from a data set.
  • Computer science is about learning how hardware and software work.
  • Each field has crossover skills, but they are distinct disciplines.
  • As of July 2024, computer scientists can expect to earn an average of $129,000
  • Data scientists can expect to earn an average of $157,000.

Data Science, Computer Science & How They Work

What Is Data Science?
Data science is a field where practitioners use mathematics, statistics, programming, data analytics, AI, and machine learning expertise to discover insights hidden in a dataset.

Part of this role includes data ingestion, collecting raw and unstructured data from multiple sources, cleaning and integrating the data via extract transform load (ETL), and other methods so that it can be stored in a data warehouse or lake.

Once the data has been processed and stored, it can be analyzed to find biases, patterns, and trends. These can be converted into actionable insights that key stakeholders can use to improve their decision-making.

What Is Computer Science?
Computer science is the study of computer hardware and software. This includes a range of subject areas including software engineering, programming, mathematics, probability, statistics, data analysis, machine learning and network design.

At a high level, the role of a computer scientist is to know how hardware and software work. They must be able to maintain the performance of key infrastructure, have knowledge of network design, and code and develop new products.

The computer scientist role may also be referred to as a computer programmer, software engineer, systems analyst, or systems engineer, or even a data scientist, despite all the differences.

Data Science vs. Computer Science: Side-by-Side Comparison

Feature  Data Science  Computer Science 
Definition  Data science is a field where practitioners use skills in mathematics, programming, and other areas to identify patterns and trends in a data set. Computer science is the study of how computer hardware and software work.
Responsibilities  Data collection, data analysis, developing machine learning models, and sharing insights with other stakeholders. Developing and testing software applications, maintaining hardware and network, and coding new products.
Study Areas  Statistics, mathematics, machine learning, data visualization, data analytics, data mining, and data storytelling. Computational systems, programming languages, algorithm design, data structure, and machine learning.
Qualifications  Bachelor’s or master’s degree in data science, computer science, statistics, or mathematics. Bachelor’s or master’s degree in computer science, software engineering, or other related field.
Programming languages  Python, R, SQL Java, C++, C#
Tools  Tableau, Apache Hadoop Git, Docker, Kubernetes
Relevant Skills  Mathematics, programming, critical thinking, problem-solving, and logic. Mathematics, problem-solving, programming, software design, and algorithm coding.
Potential Job Titles  Data scientist, data analyst, and machine learning engineer. Computer programmer, software engineer, systems analyst, systems engineer, or even a data scientist.
Average Salary  $157,000 $188,000

Major Differences Between Data Science and Computer Science

Both data science and computer science have different objectives. In a nutshell, data science is about learning the skills necessary to process and extract insights from a dataset. Meanwhile, fundamentally, computer science is about understanding how computers and computational systems work.

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The data scientist is tasked with collecting unstructured and structured data within an environment, ingesting and processing it so that they can share patterns and trends with other stakeholders. In contrast, the computer scientist’s role is to design and code software, manage the performance of existing hardware, and develop new algorithms.

Likewise, the difference between data analytics vs. computer science is that data analytics is the process of using analytics to understand a dataset, compared to developing and maintaining computational systems in general.

In short, the main difference between data science and computer science is that one is designed for processing data, and the other is intended to maintain and develop computational systems.

While many of the skills needed for each discipline overlap, each has a unique role to play.

What about pay and career progression opportunities in computer science vs. data science?

It’s arguable that computer science offers more career flexibility because it covers a wider array of disciplines and expertise than data science, which is more focused on ingesting and interpreting data.

Data Science vs. Computer Science

Career Prospects in Data Science

Responsibilities

Some of the typical roles and responsibilities that data scientists can expect to fill include:

  • Identify and ingest raw structured and unstructured data
  • Clean and process data for storage
  • Store the data in a data lake or warehouse
  • Querying the data to extract insights and identify trends
  • Create models to predict future trends
  • Communicate key findings to other stakeholders

Salary Potential

Average Annual Salary: $157,000

Salary Range: $124,000 – $203,000

Data scientists have the potential to enjoy a lucrative salary. According to Glassdoor as of July 1, 2024, the average salary of a data scientist is $157,000 per year in the U.S., but experienced professionals in this field can expect to make as much as $203,000 per year.

Career Prospects in Computer Science

Responsibilities

Some of the typical roles and responsibilities that computer scientists can expect to fulfill include:

  • Design new hardware and software products
  • Maintain and optimize the performance of hardware and software
  • Write and analyze code or fix bugs in existing code
  • Integrate new systems and train users on how to use them
  • Monitor network and system performance

Salary Potential

Average Annual Salary: $188,000

Salary Range: $143,000 – $251,000

Moving into computer science also has the potential to offer significant pay. Glassdoor research finds that computer scientists can expect an average salary of $188,000 per year in the U.S., but pay could also go as high as $251,000.

So if we were to compare a data science vs. computer science salary we could say that the latter pays more on average.

The Bottom Line

Reflecting on the data science vs. computer science debate, we can see that each type of practitioner has a valuable role to play in enabling IT and data-centric companies to perform at their best.

If you want to secure one of the most in-demand tech jobs in 2024, estimate your knowledge, skills, and background and consider what education, certificates, and courses you can pursue to get the necessary qualifications.

FAQs

Which is better, data science or computer science?

Does data science pay more than computer science?

Is data science more math or coding?

Is data science harder than programming?

Is it possible to transition from computer science to data science?

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Tim Keary
Technology Specialist
Tim Keary
Technology Specialist

Tim Keary is a freelance technology writer and reporter covering AI, cybersecurity, and enterprise technology. Before joining Techopedia full-time in 2023, his work appeared on VentureBeat, Forbes Advisor, and other notable technology platforms, where he covered the latest trends and innovations in technology.