Data Science vs Computer Science: Which Career Is Better in 2024?

Whether beginning your career in 2024 or deciding on a college major, the robust growth potential in both data science and computer science presents an exciting (yet challenging) choice for your future path. According to industry insights, although these two career paths share some interdisciplinary aspects, transitioning between them can be challenging once you’ve built substantial experience in one.

Therefore, it’s better to take your first step with as much care and information as possible. To help guide you, we’ve created this article to explain both fields, highlight their similarities, and outline the key differences you should consider.

Before we jump in, let’s take a brief look at what these intriguing disciplines offer:

Feature	Data Science	Computer Science
Primary Objective	Derive actionable insights and predictions from data.	Solve complex problems using computational and algorithmic principles.
Industry Scope	Relatively narrow but growing rapidly with increasing demand for data-driven, decision-making roles.	Broad scope with established and diverse career paths across multiple technology domains.
Core Focus	Heavy emphasis on mathematics, statistics, and data analysis skills.	Centered around software engineering, algorithms, and hardware design.
Educational Levels	Primarily studied at the post-graduate level; some undergraduate programs are emerging.	Widely available at all educational levels, from associate’s to doctoral degrees.
Job Demand (2023–24)	High demand for roles in machine learning, AI, and data analytics across industries.	Consistently strong demand, especially in software development, cloud computing, and cybersecurity.
Skills Required	Statistical analysis, data mining, machine learning, data visualization, programming (Python, R).	Programming (Python, Java, C++), system design, database management, algorithm development, and problem-solving.
Common Job Titles	Data Scientist, Data Engineer, Business Analyst, Data Analyst, Machine Learning Engineer.	Software Engineer, Network Engineer, Web Developer, Game Developer, UX Designer, Systems Architect, Cybersecurity Specialist.
Career Path Flexibility	Opportunities to move into AI, business intelligence, or machine learning roles.	High flexibility, allowing transitions between roles in software development, networking, design, etc.
Industry Growth Areas	Artificial Intelligence, biomedical data, financial technology, and customer analytics.	Cloud computing, IoT, mobile development, network security, and augmented reality.
Salary Outlook	Competitive salaries, especially for specialized roles in ML and AI.	Consistently high across most roles, particularly in software engineering, cybersecurity, and cloud services.

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.

Data science is an umbrella term consisting of multiple fields focused on extracting insights and knowledge from both structured and unstructured data. By analyzing this data, organizations can derive valuable insights to make informed business decisions, improve customer experiences, optimize operations, and drive growth.

Central to data science is a strong foundation in mathematics and statistics, but modern data scientists also rely on:

• Programming Skills: Proficiency in languages like Python, R, and SQL for data manipulation and analysis.
• Machine Learning and AI: Leveraging algorithms to make data-driven predictions or classify information.
• Data Visualization: Presenting complex data in an understandable and actionable format using tools like Tableau or Matplotlib.
• Software Development and Data Engineering: Ensuring data is properly managed, stored, and accessed.
• Domain Knowledge: Understanding the specific industry (such as healthcare, finance, or marketing) to contextualize data insights.

As mentioned, data scientists play essential roles across multiple industries, taking on positions such as:

• Data Scientist: Developing and applying models to interpret data and predict trends.
• Data Engineer: Designing and optimizing data infrastructure for data collection and transformation.
• Data Analyst: Analyzing data sets to help inform business strategies and decisions.
• Market Analyst: Identifying and analyzing market trends to aid product development or marketing strategies.
• Business Intelligence Analyst: Creating data-driven reports and dashboards to support business operations.

An advantage of data science is its applicability across industries—whether in finance, healthcare, retail, or technology, data-driven insights are invaluable.

Computer science, broadly, is the study of computers and computational systems, covering both the theoretical foundations and practical applications of computing. From assembly language to developing complex ML algorithms, computer scientists explore everything related to software and hardware that drive computing systems. The goal is often to improve how these systems operate or to design solutions to computational problems across various domains.

A comprehensive computer science curriculum includes:

• Software Development: Learning programming languages and frameworks to build software applications.
• Operating Systems: Understanding how systems manage hardware and software resources.
• Databases: Studying data management, storage, and retrieval systems.
• Computer Hardware: Gaining insight into the physical components that make up computers.
• Artificial Intelligence (AI) and Machine Learning (ML): Applying algorithms that enable machines to learn and perform tasks.
• Networking: Learning how computers communicate within and across networks.
• Cybersecurity: Ensuring the protection of systems and data from unauthorized access and threats.

As a computer science graduate, you’ll have a broad spectrum of career opportunities available to you. Your foundational knowledge will enable you to work in roles such as:

• Software Engineer: Designing, developing, and testing software applications.
• Information Security Analyst: Protecting information and systems against cybersecurity threats.
• Software Developer: Developing and maintaining software applications.
• Web Developer: Building and maintaining websites, focusing on both frontend and backend functionality.
• Mobile App Developer: Specializing in software development for mobile devices.
• Game Developer: Creating interactive games, with an understanding of design, animation, and storytelling.
• Forensic Computer Analyst: Investigating cybercrimes and retrieving data from compromised systems.

As computing technology advances, computer science offers continuously expanding opportunities in fields like quantum computing, blockchain, and robotics.

Despite their interdisciplinary characteristics, there are some distinct differences between the two fields of study. Here they are, discussed in detail:

Key Objectives of Data Science vs Computer Science

Data science is primarily about cleaning and sorting data to extract relevant information. The emphasis is on understanding data patterns, trends, and anomalies to guide decision-making. To successfully execute the responsibilities, data scientists integrate various disciplines, including statistics, data analysis, and machine learning, to interpret complex datasets.

CS, on the other hand, focuses on the design, development, and optimization of algorithms, software, and hardware systems. Computer scientists often work on improving computational efficiency and creating systems that can process data, though they may or may not include data analysis as part of their scope. Data scientists also play a key role in generating performance insights, identifying patterns, and developing algorithms that support developers’ work.

Skills and Education

As discussed, data science requires a diverse skill set, including statistical analysis, programming languages, machine learning, data visualization, and domain knowledge. Depending on the role a data scientist holds, they might have to dive deeper into the skill sets to perform their responsibilities effectively. Data scientists typically have degrees in fields such as statistics, mathematics, computer science, or a related area, often holding advanced degrees (master’s or PhD).

Algorithm design, software development, system architecture, and cybersecurity are the key skill areas that a computer science major must be proficient in. Given their technical background, CS majors can and do pivot to data science fields. Computer scientists usually have degrees in computer science or engineering, with many pursuing further specialization through advanced degrees.

Primary Tools Used

While a multitude of tools and resources are shared by the disciplines, 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.

Data Science Tools:

• Programming Languages: Python, R, SQL
• Data Analysis Libraries: pandas, NumPy, scikit-learn for Python; dplyr and ggplot2 for R
• Machine Learning Frameworks: TensorFlow, Keras, and PyTorch for building models
• Data Visualization Tools: Tableau, Power BI, and Matplotlib
• Big Data Technologies: Apache Hadoop, Spark, and NoSQL databases (e.g., MongoDB)

Computer Science Tools:

• Development Environments: Integrated development environments (IDEs) like Visual Studio, IntelliJ IDEA, and Eclipse
• Version Control: Git for managing code and collaborating in software development
• Database Management Systems: MySQL, PostgreSQL, and Oracle for structured data storage
• Frameworks and Libraries: For web development (e.g., Django, Flask for Python; Angular, React for JavaScript)
• Cloud Platforms: AWS, Google Cloud, and Azure for deploying applications and managing infrastructure

Real-World Application

Data is integral in most industries. Data science plays essential roles in finance for risk assessment, healthcare for predicting patient outcomes, and marketing for customer behavior forecasting. Companies like Amazon and Netflix use it to analyze user behavior for product recommendations. Additionally, banks leverage data science to detect fraud by identifying patterns in transaction data.

In computer science, key areas include software development for mobile and enterprise applications, networking for device communication infrastructure, and cybersecurity, which focuses on protecting systems from cyber threats.

Research Areas

Key research areas in data science include big data analytics for handling large datasets, machine learning algorithms for better prediction accuracy, and natural language processing (NLP) for enhancing machine understanding of human language.

Important research areas in computer science involve algorithm design for efficiency, human-computer interaction (HCI) for improved usability, and quantum computing for advanced computational solutions.

Data science and computer science have different goals on paper, but careers in these fields have certain parallels, as explained below.

Skills

Skills that both data scientists and computer scientists require include programming, advanced mathematics, database management, machine learning, deep learning, and cloud computing. You may further refine your skills by solving data science interview questions.

Data scientists and computer scientists often focus on different aspects of their skills. For example, computer scientists may learn multiple programming languages, while data scientists typically concentrate on Python and R.

Tools and Resources Used

Many of the tools used by data scientists are also utilized by computer scientists. The Python language and packages such as pandas, NumPy, TensorFlow, and scikit-learn are used by both. Other common tools and resources include database management systems, IDEs, SQL interview questions, 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. You also can use AI Interviewer to prepare for your upcoming opportunities.

Tech-Forward

Today, careers in both fields are considered cutting-edge. 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. Data scientists need to know what data is most relevant to them and what insights are potentially useful from a business perspective. On the other hand, computer scientists need to leverage that knowledge to develop tools or help the organization solve existing challenges.

Pay

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.

Here is a detailed discussion of what to consider when choosing one career over the other:

Salary and Job Market Trends

Both fields offer competitive salaries, with data scientists often earning slightly higher due to the demand for data-driven insights. Job market trends indicate a growing need for both roles, driven by increasing reliance on technology and data.

Educational Requirements and Certifications

Data science typically requires a degree in statistics, mathematics, or computer science, often complemented by certifications in data analysis or machine learning. Computer science usually requires a degree in computer science or software engineering, with various certifications available in areas like cloud computing and cybersecurity.

Roles and Responsibilities in Each Career

Data scientists focus on analyzing data, creating predictive models, and visualizing insights. In contrast, computer scientists design and develop software systems, optimize algorithms, and manage networks. Both roles require strong analytical and problem-solving skills.

Growth Potential in Each Field

Both fields offer robust growth potential, but data science is expanding rapidly due to the explosion of big data and analytics needs. Computer science continues to evolve with advancements in areas like artificial intelligence and cybersecurity.

Industry Demand and Job Opportunities

Demand for data scientists is surging in the finance, healthcare, and marketing sectors. Computer science roles are also in high demand, particularly in software development, cybersecurity, and cloud computing.

Career Flexibility and Mobility

Data scientists can transition into roles in business intelligence or analytics, while computer scientists often move into software engineering or IT management. Both careers offer various pathways for specialization and advancement.

Work Environment and Job Satisfaction

Data scientists often work in collaborative settings, analyzing data and presenting findings. Computer scientists may work in diverse environments, from startups to large corporations, focusing on software development or system architecture. Job satisfaction can vary based on company culture and role responsibilities.

Impact of AI and Automation on Career Longevity

AI and automation are reshaping both fields, potentially automating routine data science and software development tasks. However, professionals who adapt to emerging technologies will remain in demand.

Skill Sets for Career Advancement

Both careers require strong programming skills (Python, R, SQL for data science; Java, C++, SQL for computer science), analytical thinking, and familiarity with machine learning. Continuous learning is essential for advancement in both fields.

Can I be a data scientist with a computer science degree?

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, machine learning, and data visualization. Developing additional skills in tools like Python libraries (pandas, scikit-learn) and data visualization platforms (Tableau, Power BI) can make you a well-rounded candidate. Continuous learning and practical experience, like projects or certifications, can further enhance your qualifications.

Which pays more: data science or computer science?

On average, software engineers earn a base salary of $115,728, while data scientists earn $122,886 as their base salary. 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.

Can I pivot my career from computer science to data science?

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.

Is data science more about math or coding?

Data science requires a strong blend of both math and coding skills. While mathematical understanding is essential for developing models, analyzing patterns, and selecting the right algorithms, coding is equally essential for implementing these mathematical concepts, handling large datasets, and automating workflows. In modern data science, robust skills in both areas are crucial, as they work together to enable effective data analysis and model deployment.

Why is computer science important in data science?

Computer science has made significant contributions to data science in the areas of data collection, pre-processing, analysis, and presentation. Computer science 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. All the best!