Data science vs. computer science: Which is right for you?

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  1. What is computer science?
  2. What is data science?
  3. What is the difference between data science vs. computer science?
  4. Computer science careers
  5. Does a Data Scientist need to know computer science?
  6. Data science vs. computer science salaries
  7. Which is right for you—data science or computer science?
  8. How to learn data science or computer science
  9. Tips for success

The fields of data science and computer science often overlap and have similar skill sets. For example, both learn programming languages. However, the type of languages they learn and how they apply them usually differs.

Data Scientists use SQL to manage databases. Computer science is a broad field. It incorporates many languages—CSS, Python, Java, and more—to build websites, mobile apps, software, and other technology.

Computer science and data science often work together, and both fields can lead to fulfilling careers. If you’re deciding between computer science vs. data science, you’ll want to consider their differences too. Each has a distinct career path and earning potential.

What are the other differences between computer science vs. data science? This article will break down skills, job titles, responsibilities, salary, and more to help you find the right fit.

What is computer science?

Computer science is the study of technology, particularly computers, hardware, and software. A Computer Scientist researches how technology can improve aspects of daily life.

They can oversee every step of developing new products, including design, development, and production.

The field of computer science is broad and constantly advancing, so experts in this field regularly update their knowledge and skills. Areas of specialty for Computer Scientists include:

  1. Algorithms
  2. Artificial intelligence
  3. Computer networks
  4. Cybersecurity
  5. Databases
  6. Programming languages
  7. Software development and engineering

What is data science?

Data science is the study of data to understand trends better and forecast the future. A Data Scientist collects data and uses analytical tools or machine learning algorithms to generate insights. Jobs in this field often require data analytics, data visualization, probability, and statistics expertise.

As a Data Scientist, you can pursue careers in business intelligence, cybersecurity, data engineering, and market data analytics.

What is the difference between data science vs. computer science?

Both data science and computer science may work with data, but they have a few crucial differences.

Data science careers

Many organizations rely on data to make business decisions, which means you can find career opportunities in numerous industries. For instance, data science professionals can work in agriculture, cybersecurity, finance, or insurance.

Additionally, you don't need a four-year degree to get a data science job. Instead, you can explore alternatives to college like apprenticeships, internships, or online courses.

One of the biggest advantages of doing a Multiverse data science apprenticeship is that you earn while you learn. We help pair apprentices with top companies providing on-the-job training and a salary.

Data science jobs

A background in data science could set you up for one of these careers:

  1. Data Analyst
  2. Data Scientist
  3. Business Intelligence Analyst
  4. Data Architect
  5. Machine Learning Engineer
  6. Data Engineer

Data science tools and languages

Data science jobs require some technical skills, but the exact platforms and programming languages you'll use depend on your position. These are popular tools in data science:

  1. Programming languages: SQL is the main programming language in data science. Data Scientists often use SQL to manipulate data. Most Data Scientists only need to know other programming languages to become Data Engineers.
  2. Data visualization tools: Data Scientists use charts and maps to represent insights and trends visually. Tools like Plotly and Tableau generate data visualizations that audiences can easily understand.
  3. Machine learning platforms: You can use machine learning tools to automate time-consuming data science tasks like analyzing data and creating predictive models to forecast future trends.

Computer science careers

Most people who study computer science prepare for software development or engineering roles.

A Software Engineer apprenticeship can open the door to the same career opportunities as earning a computer science Bachelor's degree.

Computer science jobs

Here are some popular computer science careers:

  1. Cloud Engineer
  2. Computer Systems Analyst
  3. Cybersecurity Analyst
  4. DevOps Engineer
  5. Full Stack Developer
  6. Hardware Engineer
  7. Mobile Developer
  8. Product Manager
  9. Software Engineer
  10. Systems Architect
  11. UX Designer
  12. Video Game Developer
  13. Web Developer

Computer science tools and languages

The programming languages and tools you’ll need in this field can vary depending on your role. Here are a few common computer science tools:

  1. Debuggers: Programmers use debugging tools to find errors in code.
  2. Programming languages: Computer science roles often require specific coding language proficiencies. For example, C++ can manage large amounts of memory, so it's a popular choice for coding browsers and video games. Similarly, Python is a versatile language that developers use to create artificial intelligence algorithms and websites.

Does a Data Scientist need to know computer science?

You don’t need to know computer science or software engineering to become a Data Scientist. However, knowledge of these topics could help advance your career.

Data Scientists may create or use machine learning algorithms that help source, organize, and use data. Studying computer science can make these duties easier because you’ll have a deeper understanding of algorithm design, programming languages, and other relevant skills.

Knowing computer science or software engineering can also help you become a Data Engineer. Data Engineers blend computer science and data science to construct and manage data pipelines.

Data science vs. computer science salaries

The salary you can earn by pursuing a data science or computer science career depends on your company, job title, location, and years of experience. According to Indeed, here are the average salaries for popular computer science and data science careers.

Which is right for you—data science or computer science?

Choosing a career path is a highly personal decision. If you’re trying to decide between computer science vs. data science, these considerations can help you pick the best option.

You may prefer data science if you’re:

  1. An analytical thinker
  2. Capable of working closely with clients
  3. Interested in artificial intelligence and machine learning algorithms
  4. Proficient in math skills like calculus and statistics
  5. Targeting a career related to data science, like Data Engineer or Machine Learning Expert

By contrast, computer science may be the right choice if you’re:

  1. An innovative thinker and problem solver
  2. Eager to immerse yourself in computational thinking and theories
  3. Interested in working with databases, products, and software systems
  4. Prepared to work with teams of Software Engineers, managers, and other professionals
  5. Ready to explore a broad range of career opportunities

How to learn data science or computer science

Launching a tech career can seem intimidating, but there are many affordable ways to get there. Here are two ways to get started.

Do an apprenticeship

You can gain hands-on experience working for a top company through a Multiverse apprenticeship. You’ll develop hard and soft skills as you create real-world solutions to problems earning a competitive salary. You can apprentice in various data science or computer science programs, including Strategic Analytics, Advanced Analytics, and Software Engineering.

Learn programming languages

Some computer science careers require proficiency in programming languages. Studying popular languages like JavaScript and Python can give you a strong coding foundation. Data Scientists often need to know SQL to manage databases. You can get started with free online resources, including coding forums and YouTube tutorials.

Tips for success

Technical skills can help you get started in data science or computer science. These tips will help you boost your resume and compete for top-paying positions.

Data science tips

A Data Scientist needs soft skills to translate data into stories and insights for decision-makers. Here are two skills that can set you up for more leadership opportunities and higher pay:

  1. Collaboration: Data Scientists frequently collaborate with different teams of tech professionals. They also share their findings with clients, executives, and stakeholders. Learn how to work well in a team and resolve conflicts to develop strong working relationships.
  2. Presentation skills: Data Scientists communicate business insights to clients, stakeholders, and other audiences. Honing your presentation skills through programs like Toastmasters(opens new window) allows you to convey information more effectively.

Computer science tips

Computer Scientists can also boost their career prospects by focusing on soft skills including:

  1. Networking: Many professionals find their next job by networking. Develop connections with people in your field by attending conferences, connecting on social media, and seeking opportunities to meet people.
  2. Problem-solving: Complete coding challenges, participate in hackathons, and work on technical assessments and interviews.
  3. Resilience: Creating new hardware or software often involves many mistakes and setbacks. You can impress your employer and peers by persevering through difficulties and trying new approaches.

With the right career preparation and skills, you can set yourself up for success in data science or computer science. If you want to gain experience fast, a Multiverse apprenticeship(opens new window) can help you get started.

Team Multiverse

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