|The objective is to derive insights from data.
|The objective is to solve problems using computer systems by applying computing principles.
|Has a narrow scope and fewer career paths in the industry.
|Has a wide scope and many possible career paths.
|Centered around math and statistics.
|Centered around software engineering.
|Mainly studied at the post-graduate level.
|Offered at all levels of higher education.
|Possible job titles are data scientist, data engineer, business analyst, and data analyst.
|Possible job titles are software engineer, networking engineer, web developer, game developer, game designer, UX designer, app developer, etc.
Deciding between data science vs computer science is a tough choice, given the high demand for experts in both fields. What makes you a better fit as a data scientist or computer scientist? Is one of these careers objectively better? These are the questions we’ll be answering here.
Generally, the work done in data science is geared toward understanding data, while computer science has a broader scope with many subfields, each with a distinct focus.
Despite this key difference, there are similarities in the work done in data science and computer science, as well as crossover in the skills needed to succeed. We examine these similarities and differences below to help you make a decision.
Before we compare the differences between computer science and data science, let’s first look a little deeper at what these two titles mean:
Broadly speaking, computer science is a field where computers, and the systems that run on them, are studied. This is done to improve how these systems work or to use these systems to solve problems.
A person studying computer science will need to learn about software development, operating systems, databases, computer hardware, artificial intelligence, networking, cybersecurity, and more. They will need to understand both the theoretical and practical aspects of these subfields.
With this knowledge, computer scientists have a wide range of career opportunities that include, but are not limited to, working as:
Data science is a multidisciplinary field whose primary goal is analyzing data to acquire helpful insights. Using data science, an organization can make sense of internal and external data to get ideas on how to improve one or more aspects of their business.
Math and statistics are at the heart of data science, but to be an effective data scientist in today’s world you will need additional skills including programming, software development, artificial intelligence, and algorithm development. Domain-specific knowledge is also important.
Qualifications in data science can lead to a career as a:
All these careers are data-centric, but an advantage to studying data science is that these roles exist in all industries and businesses. Understanding certain aspects of computer science can also be an important asset in data science.
Data science and computer science have different goals on paper, but careers in these fields have certain parallels. These are explained below.
Skills that both data scientists and computer scientists require include programming, advanced mathematics, database management, machine learning, deep learning, and cloud computing.
Data scientists and computer scientists may focus their attention on different aspects of these skills. For example, computer scientists may learn many programming languages, while data scientists will focus on Python and R.
Many of the tools used by data scientists are used by computer scientists too. The Python language and packages such as Pandas, NumPy, TensorFlow, and Scikit-learn are used by both. Other common tools include database management systems, IDEs, SQL, and remote computing systems.
Artificial Intelligence/Machine Learning
AI and ML have a huge presence in both computer science and data science. For example, large language models such as ChatGPT must be trained on massive amounts of data. The development and improvement of these algorithms require a deep understanding of computer science principles.
Careers in both fields are considered to be on the cutting edge today. Tech companies such as Facebook, TikTok, and Google were built using computer science, but they rely on data science to make sense of the massive feedback they aggregate, which helps to improve the platforms and generate more revenue.
Understanding the Core Business/Organization
Both need to have a firm understanding of the organization. For data scientists, they need to know what data is most relevant to them and what insights are potentially useful from a business perspective. For computer scientists, they need to leverage that knowledge to develop tools or help the organization solve existing challenges.
Roles in data science and computer science are some of the highest paying in the world today. In countries like the US, careers in either command six-figure base salaries. The pay in both careers also gets significantly higher with seniority.
Level of Difficulty
Careers in data science and computer science are perceived to have a high degree of difficulty. This perception is not uncommon for careers in STEM, where courses test skills such as advanced mathematics, statistics, programming, problem-solving, and algorithmic thinking. Some roles even require advanced degrees.
Now that we’ve run through these similarities, let’s discuss the significant differences between the two fields.
As stated earlier, the objective of data science is to gain useful insights from data. In computer science, on the other hand, the goal is typically to understand, build, and improve computing systems.
Skills and Education
In the learning process, some skills are not taught in both fields. Data scientists have to take more advanced courses in statistics, while computer scientists have to understand aspects of computer systems, like hardware and operating systems, that are not relevant to most data science work.
Additionally, computer science courses can be taken at all levels of higher education, while data science is mainly offered at the master’s level today.
Familiarity with Specific Tools
There are tools used in computer science that are not used in data science, including programming languages like C++, frameworks for testing software, and web development tools. There are also statistical packages and other data science tools that are not part of the standard computer scientist’s toolkit.
The differences in the education and skillsets of computer scientists and data scientists also mean they are suited for different career paths. Computer scientists work as experts in one or more of its subfields, while data scientists take on data-centric roles in organizations.
As unsatisfactory as it sounds, neither computer science nor data science is an objectively better path, and your selection will depend on your interests and career goals.
Many individuals can thrive in either field because both fields involve working with cutting-edge technology, are currently in high demand, pay better than other paths, and offer many interesting problems to solve.
To find the option that is better for you, weigh carefully what both offer in the short and long term. This makes it easier to embark on the one that aligns more with your aspirations.
Take a look at how some of those interests might align with one or the other:
You like working with large amounts of data:
Modern data science requires working with large amounts of data. If you like the prospect of analyzing and making sense of big data, data science can be an excellent career path for you.
You like data-driven solutions:
The role of data scientists is to come up with data-driven solutions that improve one or more aspects of a business. This will mean coming up with theories, identifying data sources, setting up data pipelines, figuring out what the data says, and how the business can act on it.
You enjoy making predictions:
Looking at past and present trends and making predictions about what’s coming down the road can be a fun career. Data scientists work relentlessly to make their predictions more accurate because even a slight improvement in accuracy can be significant to a business.
You want to understand the world we live in:
Our understanding of fields such as weather forecasting, public health, transportation, sports, government, and even human behavior has greatly improved thanks to our ability to collect and analyze data about them.
You enjoy using software tools more than you enjoy building them:
Data scientists have to use many software packages, but they don’t need to know how to build them. If you don’t like the idea of building software packages from scratch, you may prefer data science.
You’d like to build software and other computer-based systems:
As a computer scientist, your ability to develop computing solutions could be your most in-demand skill. This could be as simple as automating tasks, or as complicated as building the next Facebook. If these are the types of projects you hope to work on, computer science is a good career path for you.
You enjoy solving problems using technical solutions:
Computer scientists take on different problems and figure out how to apply a technical solution to them. Spreadsheet software, dating apps, email clients, and digital drawing software are all examples of technical solutions that solve one or more existing problems.
You don’t enjoy statistics or working with data:
Data science has its fair share of monotonous work while cleaning and exploring data, and advanced statistical concepts can be challenging to understand and implement.
You like mathematics:
Mathematical principles such as binary counting, discrete mathematics, statistics, algebra, matrices, and calculus are all used in computer science. If you enjoy solving mathematical problems, computer science may be perfect for you.
You prefer a broader career scope:
Data science has fewer roles in industry than computer science. Computer scientists can be hired to fill many different shoes because it has more subfields you can specialize in later.
Yes, you can. Computer scientists possess many of the skills needed in data science and can perform many of the required tasks. You may need to add a few more skills to perform certain jobs, and some employers prefer candidates with a master’s degree in data science or statistics.
Software engineers earn a base salary of $115,728, while data scientists earn $122,886 as their base salary on average. This, however, is not the whole story. There are other roles in both fields with lower or higher base salaries. You can find out more on Interview Query’s salaries page.
Yes, you can change your career path from computer science to data science. Many data scientists today have a background in computer science. Depending on the specifics of your situation, you may need to get a master’s degree in data science, but passion and commitment are key.
Math is the most important part of data science. Coding is mainly a tool for implementing mathematical ideas in data science, because computers can do it faster and make fewer mistakes than people. You also need math skills to know when and how to implement specific algorithms.
Computer science has made significant contributions to data science in the areas of data collection, pre-processing, analysis, and presentation. Computer science is what has made it possible to handle the massive amounts of data generated today, and make sense of it.
A career in either data science or computer science can be a challenging, lucrative, and fulfilling endeavor for you. Choosing between the two may seem daunting, but you can leverage your interests to find the option that best fits you.
Interview Query provides more resources to help you decide including information on salaries for data scientists and software engineers at different levels and articles on the latest in data science careers.
Whether you pick data science or computer science, you’ll be in one of the hottest careers today!