Data Science vs Computer Science Degree Programs: Explaining the Difference for 2025
Co-Founder and Chief Data Scientist
Many individuals considering a career in technology often struggle to decide between pursuing a data science or a computer science degree. Though both fields offer strong job prospects, the skills, education, and career paths differ significantly, requiring students to consider their interests and long-term goals carefully. Computer science jobs are projected to grow 5.13% annually, creating 356,700 openings per year, while data science will expand 36%, adding 20,800 jobs annually from 2023 to 2033. While data science is expanding rapidly, computer science offers a much larger volume of job opportunities due to its broader applications across industries.
This article, prepared by experts in career planning, will provide a comprehensive comparison between data science and computer science programs. It will help you understand which path best aligns with your skills and aspirations.
Key Things You Should Know About Data Science vs Computer Science Degree Programs
- Education requirements. Both fields typically require a bachelor’s degree, with advanced degrees or certifications beneficial for higher-level positions. Certifications and boot camps can supplement these degrees, providing specific skills for specialized tasks.
- Industry demand. Data science jobs have reached 192,710 professionals, with a projected 36% growth from 2023 to 2033, leading to about 20,800 job openings annually. Computer science jobs are also growing, with a workforce of 2.32 million and a 5.13% annual growth rate.
- Diverse work settings. Data scientists are employed in industries like computer systems design and insurance, while computer scientists work across sectors such as education, healthcare, and finance, with their expertise in system development and IT infrastructure.
- Competitive salary. Data scientists earn a median annual salary of $160,772, with salaries ranging from $127,000 to $206,000. Computer scientists have a median salary of $188,033, with a range of $145,000 to $231,000.
- Diverse career paths. Data science graduates can pursue roles like data analyst, machine learning engineer, and business intelligence developer. Computer science graduates can work as software developers, cybersecurity analysts, or cloud solutions architects.
Table of Contents
- What’s the difference between the job roles and responsibilities of a data scientist and a computer scientist?
- How do the education requirements for data science and computer science compare for 2025?
- How do the coursework and curriculum differ between data science and computer science programs?
- What certifications are available for data science professionals compared to computer science professionals?
- How do the costs of pursuing a degree in data science compare to computer science degrees?
- What are the typical career paths for data science graduates compared to computer science graduates?
- Is an online accelerated degree a viable option for building a career in computer science?
- Can niche specializations, like game development, influence technology career paths?
- What are the key differences in the skills required for data science vs. computer science?
- What internship and hands-on learning opportunities do these programs offer?
- Is an Advanced Degree Essential for Success in Data Science vs. Computer Science?
- How can interdisciplinary education boost career advancement in technology?
- How much do data science vs computer science professionals earn?
- What types of industries or sectors hire data scientists vs. computer scientists?
- How can emerging fields like health informatics expand career opportunities?
- What is the job outlook for data science vs. computer science graduates?
- Other Things You Should Know About Data Science vs Computer Science Degree Programs
What’s the difference between the job roles and responsibilities of a data scientist and a computer scientist?
A data scientist focuses on analyzing and interpreting complex data. They use statistical techniques, machine learning models, and data visualization tools to identify patterns, trends, and actionable insights. Here are the common job roles and responsibilities of a data scientist:
- Collecting, cleaning, and processing large datasets.
- Performing statistical analysis to extract insights.
- Building predictive models using machine learning algorithms.
- Communicating findings to stakeholders through reports and visualizations.
- Using tools like Python, R, SQL, and data visualization platforms.
- Collaborating with business teams to solve data-driven problems.
- Continuously improving models based on new data inputs.
On the other hand, computer scientists focus on building the technological frameworks and software systems that power modern computing environments. Below are the typical job roles and responsibilities of a computer scientist:
- Designing and developing software applications and systems.
- Writing and testing code in languages like Java, C++, or Python.
- Creating algorithms to optimize computing processes.
- Researching emerging technologies to improve system efficiency.
- Working on operating systems, databases, and network protocols.
- Ensuring software security, scalability, and performance.
- Collaborating with engineers and IT teams to implement solutions.
For those aiming to transition from either of these fields into leadership, understanding how to become a CTO is essential. As a Chief Technology Officer, professionals oversee the technology strategy of an organization, often using the skills gained as data scientists or computer scientists to lead technological innovation.
How do the education requirements for data science and computer science compare for 2025?
Both fields generally require a strong foundation in mathematics, programming, and problem-solving. A bachelor's degree is typically the starting point for both careers, with data science focusing on analysis and machine learning and computer science on algorithms and software development. Among data science professionals, 50.7% hold a bachelor's degree, while 66.1% of those in computer science have attained the same level of education. A fast track bachelor degree online program can be an efficient way for students to quickly earn their degree while balancing other commitments.
For advanced positions, candidates often pursue a master's or even a Ph.D. in their respective fields. Data science degree programs emphasize deep analytical skills, including data mining and big data technologies. While certifications and boot camps can supplement both degrees. Certifications and boot camps supplement both data science and computer science degrees by providing specialized skills and hands-on experience.
How do the coursework and curriculum differ between data science and computer science programs?
The coursework and curriculum in data science and computer science degree programs differ in focus, subjects, and practical applications.
Coursework
Data science emphasizes practical data manipulation and analysis, while computer science focuses on the broader fundamentals of software development and computational theory.
Data science coursework focuses on data collection, analysis, and visualization, with classes in statistics, machine learning, and database management. Students also learn programming languages like Python, R, and SQL for data manipulation, often working on real-world projects. Many students pursue accelerated online degree programs to complete their studies and coursework more quickly while gaining hands-on experience.
Meanwhile, computer science coursework covers a broader range of topics, including algorithms, computer architecture, operating systems, and networking. Students learn programming languages like Java, C++, and Python, with an emphasis on computational logic and software design, along with theoretical courses like discrete mathematics.
Curriculum
The curriculum in data science degree programs blends computer science, statistics, and domain knowledge. Students are trained to collect, clean, analyze, and interpret large datasets. Core curriculum areas include:
- Mathematics (Linear Algebra, Calculus)
- Statistics and Probability
- Programming for Data Science
- Data Mining and Warehousing
- Machine Learning and Artificial Intelligence
- Ethics in Data Science
- Capstone Projects
Some programs offer electives in fields like finance, healthcare, or marketing to give students domain-specific expertise.
Computer science curricula focus more heavily on computing theory and software engineering. Computer science programs may offer specializations in areas such as game development, cybersecurity, or cloud computing. Key curriculum areas include:
- Discrete Mathematics
- Computer Architecture
- Operating Systems
- Algorithms and Complexity
- Programming Paradigms
- Artificial Intelligence
- Software Engineering Principles
- Systems Security
What certifications are available for data science professionals compared to computer science professionals?
Both data science and computer science professionals can pursue several respected certifications. Data science certifications focus on data handling, modeling, and analytics, while computer science certifications emphasize software development, systems, and security.
Below are the most recognized certifications available for data science and computer science professionals, which you can obtain through online certificate programs.
Certifications for Data Science Professionals
Data science certifications help individuals validate their expertise in handling data, building models, and deriving insights. Popular certifications include:
- Microsoft Certified: Azure Data Scientist Associate: This certification tests skills in applying Azure Machine Learning to train, evaluate, and deploy models. It's ideal for professionals working with Microsoft’s cloud platform.
- Google Professional Data Engineer: Offered by Google Cloud, this certification validates the ability to design data processing systems, analyze data, and ensure data quality and security on Google Cloud infrastructure.
- IBM Data Science Professional Certificate: This beginner-friendly certification covers essential topics like data visualization, machine learning, Python, and SQL.
- Certified Analytics Professional (CAP): CAP is a vendor-neutral certification focusing on the analytics process, from framing business problems to deploying models. It's recognized across various industries.
- Cloudera Data Platform Generalist Certification: This certification evaluates understanding of fundamental concepts related to the Cloudera platform, including big data processing and analytics tools.
- SAS Certified Data Scientist: The certification covers advanced analytics, machine learning, and programming using SAS tools. It’s valuable for those working in industries reliant on statistical analysis.
Certifications for Computer Science Professionals
Computer science certifications focus on software development, systems, and security. Widely recognized certifications include:
- CompTIA Security+: This entry-level certification tests knowledge of network security, threats, and risk management. It is ideal for those pursuing cybersecurity roles.
- AWS Certified Solutions Architect – Associate: This certification validates expertise in designing and deploying applications on Amazon Web Services. It benefits software developers and systems engineers.
- Cisco Certified Network Associate (CCNA): CCNA focuses on networking fundamentals, IP services, and security principles. It's suitable for professionals managing enterprise networks.
- Oracle Certified Java Programmer: This certification is for developers who specialize in Java programming. It validates proficiency in coding, debugging, and deploying Java applications.
- Certified Ethical Hacker (CEH): CEH certification is essential for professionals interested in cybersecurity. It teaches ethical hacking techniques and defense strategies.
- Microsoft Certified: Azure Solutions Architect Expert: This certification is ideal for architects designing cloud solutions on Microsoft Azure. It includes knowledge of networking, security, and infrastructure.
How do the costs of pursuing a degree in data science compare to computer science degrees?
Data science degrees tend to be more costly than computer science degrees on average. The cost of pursuing a bachelor's degree in data science varies widely, influenced by factors such as institution type, residency status, and delivery format. Annual tuition and fees can range from approximately $6,930 for in-state students at public institutions to $34,177 for out-of-state students, averaging around $66,272 for the entire degree. Online programs may offer more affordable options, with tuition rates as low as $3,835 per six-month term, depending on the institution.
Similarly, the cost of a bachelor's degree in computer science also exhibits significant variability. Annual tuition and fees can range from approximately $6,000 to over $40,000, depending on factors like institution type and residency status. Online programs may provide more cost-effective alternatives, with some degrees available for under $16,000 in total. For students weighing a cybersecurity and data science programs comparison, cost is just one of several important considerations—others include career outcomes, curriculum focus, and technical skill development.
Fortunately, 33.2% of all undergraduates receive financial aid in the form of state or local grants and scholarships, helping to reduce the financial burden. On average, an undergraduate receives $4,116 from state and local aid, which can help cover tuition, books, and other essential expenses.
What are the typical career paths for data science graduates compared to computer science graduates?
Data science and computer science degree graduates can pursue different career paths based on their skills and education focus. Each field offers specialized roles tailored to specific skill sets, technical expertise, and industry demands.
Career Paths for Data Science Graduates
Data science degree graduates typically build careers focused on analyzing data, building predictive models, and extracting insights. Below are the most common jobs they ca pursue after graduation.
1. Data Analyst
Data analysts focus on interpreting structured datasets to identify trends, patterns, and key business insights. They often use tools like SQL, Excel, and visualization platforms to present findings in an understandable way to stakeholders.
Median Salary: $82,640
2. Data Scientist
Data scientists apply advanced statistical techniques and machine learning algorithms to solve complex problems. They work with both structured and unstructured data and are responsible for building predictive models and driving data strategy.
Median Salary: $122,738
3. Machine Learning Engineer
Machine learning engineers specialize in developing and deploying machine learning models in production environments. They optimize algorithms and ensure models scale efficiently to meet organizational needs.
Median Salary: $128,769
4. Business Intelligence (BI) Developer
BI developers create and manage analytics tools and dashboards. Their job is to deliver clear, actionable business insights through reporting platforms, helping organizations stay competitive.
Median Salary: $111,882
5. Data Engineer
Data engineers build and maintain the infrastructure that supports data collection and storage. They design pipelines to ensure data is clean, reliable, and easily accessible for analysis.
Median Salary: $129,716
Career Paths for Computer Science Graduates
Computer science degree graduates typically pursue careers focused on software development, systems management, and technology innovation. Here are the most common career paths they can expect to secure after graduation.
1. Software Developer
Software developers design, code, test, and maintain software applications. They may work on desktop, mobile, or web platforms, creating everything from operating systems to business applications. Their problem-solving skills and programming knowledge make them essential in nearly every industry.
Median Salary: $111,845
2. Systems Analyst
Systems analysts assess existing IT systems and recommend improvements. They analyze business needs, design technical solutions, and often act as a bridge between business teams and IT departments. Their goal is to ensure systems function efficiently and meet organizational goals.
Median Salary: $89,296
3. Cybersecurity Analyst
Cybersecurity analysts safeguard computer networks and systems from cyber threats. They monitor networks, conduct vulnerability assessments, and implement security protocols. As cyberattacks become more sophisticated, the demand for these professionals continues to rise. Many individuals are opting for accelerated online cybersecurity degree programs to gain the necessary skills and qualifications to enter this high-demand field quickly.
Median Salary: $99,400
4. Cloud Solutions Architect
Cloud solutions architects design and oversee cloud computing strategies. They help companies migrate to cloud platforms and ensure scalability, security, and efficiency. Their expertise in cloud services like AWS, Azure, or Google Cloud is highly valued.
Median Salary: $150,241
5. Full Stack Developer
Full stack developers handle both front-end and back-end development tasks. They work with databases, server-side logic, APIs, and user interfaces to build complete web applications. Their ability to manage entire development cycles makes them versatile team members.
Median Salary: $123,262
Is an online accelerated degree a viable option for building a career in computer science?
Online accelerated programs in computer science integrate theoretical foundations with practice-oriented learning, enabling students to develop core computational skills swiftly. These programs emphasize hands-on project work, advanced programming techniques, and system design through immersive labs and real-world assignments. They are frequently updated to mirror technological advancements and industry trends, providing graduates the practical experience needed to meet modern job requirements. For those aiming to fast-track their education and career, a 1-year computer science degree online represents an efficient pathway to build a competitive skill set.
Can niche specializations, like game development, influence technology career paths?
Computer science programs frequently offer niche tracks that allow students to explore creative and technical specializations, such as game development. These specialized pathways blend core computational theories with practical application in interactive media, equipping graduates with a unique skill set that appeals to innovative industries. In contrast, data science programs remain focused on analytics and statistical modeling without typically incorporating domain-specific creative applications. For professionals interested in merging creativity with technology, exploring a game development degree online can provide a competitive edge in an evolving market.
What are the key differences in the skills required for data science vs. computer science?
Becoming a data scientist or computer scientist requires a strong mix of technical and general skills. Understanding these skill differences is essential for choosing the right educational and career path. A cyber security and computer science programs comparison can also help clarify which discipline aligns better with your goals. Developing the right skill set depends on which direction you choose to pursue.
Technical Skills Required for Data Science
A data science degree graduate requires specific skills to analyze, interpret, and extract insights from data. Below are five essential technical skills:
- Statistical Analysis. Data scientists must understand statistical methods to analyze patterns and trends. This helps in making accurate predictions and informed decisions.
- Machine Learning. Knowledge of machine learning techniques, such as regression, classification, and clustering, is crucial. These techniques enable data scientists to build predictive models.
- Programming (Python, R). Proficiency in languages like Python and R is necessary for data manipulation, analysis, and visualization. Python is particularly popular due to its versatility and libraries.
- Data Wrangling. Data scientists must clean and organize raw data. This includes handling missing values, correcting inconsistencies, and preparing data for analysis.
- Data Visualization. The ability to present data using tools like Tableau, Power BI, or Matplotlib is key. Visualization helps stakeholders understand complex data findings easily.
Technical Skills Required for Computer Science
Computer science professionals focus on building systems and software and solving computational problems. Below are five critical skills:
- Algorithm Design. Computer scientists must design efficient algorithms to solve problems. This includes understanding data structures and optimizing code performance.
- Software Development. Proficiency in software engineering principles is essential. This includes writing clean code, version control, and software testing.
- Programming (Java, C++, Python). Strong knowledge of programming languages like Java, C++, and Python is crucial. These languages are used to build applications, systems, and databases.
- Operating Systems & Networking. Understanding how operating systems work and how networks function is important. This skill ensures systems run smoothly and securely.
- Database Management. Knowledge of database systems such as SQL and NoSQL is necessary. Computer scientists often design, maintain, and optimize databases.
General Skills Required for Data Science and Computer Science
Both fields share certain general skills. However, their application may differ. Here are five overlapping soft skills:
- Problem-Solving Skills. Both data scientists and computer scientists must be adept at breaking down complex problems and finding effective solutions.
- Critical Thinking. Critical thinking helps professionals analyze situations objectively and make informed decisions, whether it's debugging code or interpreting data trends.
- Communication Skills. Clear communication is crucial. Both roles require explaining technical concepts to non-technical stakeholders and collaborating within teams.
- Continuous Learning. The tech industry evolves rapidly. Professionals must stay updated with new tools, frameworks, and methodologies in their respective fields.
- Attention to Detail. A small error in code or data can lead to significant issues. Both careers demand precision and thoroughness in all tasks.
What internship and hands-on learning opportunities do these programs offer?
Both data science and computer science curricula are increasingly incorporating experiential learning through internships, co-operative education, and industry-sponsored projects. These opportunities help students apply theoretical concepts in real-world settings while building professional networks. Many programs also emphasize project-based learning and collaboration with industry experts to ensure graduates are job-ready. In addition, exploring the cheapest online masters in artificial intelligence can provide a cost-effective pathway to further develop advanced technical skills.
Is an Advanced Degree Essential for Success in Data Science vs. Computer Science?
Advanced academic qualifications often serve as catalysts for expanding technical expertise and leadership potential in both fields. In data science, advanced degrees deepen understanding of complex analytical techniques and emerging technologies, while also fostering research collaborations that translate into innovative industry practices. In computer science, advanced studies emphasize rigorous training in systems design, algorithm development, and software architecture, preparing graduates for roles that demand sophisticated problem-solving skills. Balancing investment with potential return, candidates should evaluate program quality, curriculum relevance, and networking opportunities when considering further education. For those mindful of budget constraints, exploring the cheapest online master's in data science may offer a cost-effective pathway to enhance career prospects without sacrificing academic rigor.
How can interdisciplinary education boost career advancement in technology?
Leveraging interdisciplinary education can provide a competitive edge by integrating core technical expertise with emerging sector-specific knowledge. Professionals who pursue specialized training—combining elements from fields such as engineering, healthcare, and business management—are better positioned to adapt to shifting industry demands and complex project environments. Such targeted learning paths enable individuals to cross traditional boundaries and enhance their problem-solving capabilities, opening doors to expanded responsibilities and leadership roles. For instance, engaging in low cost nursing informatics programs online offers a strategic pathway for those seeking to merge clinical insights with advanced technological proficiency.
How much do data science vs computer science professionals earn?
As of 2025, data scientists in the United States have an estimated total median pay of $160,772 per year, comprising an average base salary of $115,618 and additional compensation averaging $45,154. The total pay typically falls between $127,000 and $206,000 annually.
In comparison, computer scientists in the United States have an estimated total median pay of $188,033 per year, with an average base salary of $132,180. The total pay generally ranges from $145,000 to $231,000 annually. Several factors influence the salaries of data science and computer science professionals:
- Geographic Location: Salaries differ by region. In Los Angeles, CA, data scientists' total pay ranges up to $302,166. Similarly, experienced computer scientists in Los Angeles can expect higher compensation, with salaries reaching up to $270,238.
- Industry and Employer: The industry of employment also affects earnings. For instance, data scientists in high-demand sectors like technology and finance often command higher salaries compared to those in other industries.
- Education and Skills: Advanced degrees and specialized skills can lead to increased earnings. Data scientists with a Ph.D. have an estimated total pay of $240,839 per year, including an average base salary of $146,529, making them one of the high paying degrees in the job market.
- Experience: Data scientists with 10–14 years of experience earn around $145,791, and those with over 15 years earn approximately $215,402, meanwhile computer scientists earn an average of $217,765.
What types of industries or sectors hire data scientists vs. computer scientists?
Data scientists are primarily hired in industries that rely heavily on data analysis, strategic decision-making, and technical consulting. The industries that employ the highest percentage of data scientists include:
- Computer systems design and related services (0.9%)
- Management of companies and enterprises (0.69%)
- Management, scientific, and technical consulting services (0.69%)
- Insurance Carriers (0.95%)
- Scientific research and development services (1.10%)
Conversely, computer scientists are hired across a broader range of industries, including education, healthcare, finance, and technology services. Their expertise is essential for system development, software engineering, and IT infrastructure.
Additionally, the growing tech landscape has created opportunities beyond traditional roles—for example, entry level cybersecurity jobs no experience are increasingly available in sectors like finance, healthcare, and government, offering a starting point for those with foundational IT knowledge or relevant certifications.
How can emerging fields like health informatics expand career opportunities?
Emerging interdisciplinary fields are carving out distinctive career tracks by blending technical acumen with specialized industry knowledge. Health informatics, for example, requires a nuanced understanding of data analytics combined with healthcare systems, enabling professionals to optimize patient care and operational efficiency. This integration not only leverages the core competencies found in both data science and computer science but also opens pathways to roles in clinical data management, digital health strategy, and innovative health IT solutions. For further insights, refer to the health informatics job outlook.
What is the job outlook for data science vs. computer science graduates?
According to the U.S. Bureau of Labor Statistics (BLS), the employment of data scientists is projected to grow at 36% from 2023 to 2033, significantly outpacing the average growth rate for all occupations. This surge is expected to result in approximately 20,800 job openings annually over the decade, driven by the increasing reliance on data-driven decision-making across various industries.
The job outlook for computer science degree graduates is highly promising, with strong growth in employment opportunities and competitive salaries. As of 2022, the workforce for computer science professionals reached 2.32 million, growing at a rate of 5.13% from the previous year. The most common occupations for graduates include software development, computer systems management, and other specialized computing roles.
Here’s What People Have to Say About Getting a Degree in Data Science vs. Computer Science
I chose computer science because I wanted strong technical skills, but after taking a few data science courses, I realized how much I enjoy working with data to solve real problems. Both fields gave me different ways to think and solve problems, which has been valuable in my career. I’m glad I explored both paths before deciding where to specialize. — Jennifer
Going into data science felt intimidating at first because of all the math, but the projects made it worth it. I loved how every assignment connected to real business questions, and I came out with skills I could use right away. It’s a great fit if you like using data to make decisions. — Rhea
I started in computer science because I wanted to learn how things work behind the scenes—like software and systems—but later, I saw how data science helps you apply that knowledge in new ways. Watching classmates take both routes showed me there’s no “better” option. It depends on whether you’re more excited by building technology or analyzing information. — Gian
Other Things You Should Know About Data Science vs Computer Science Degree Programs
How do the programming languages used in data science differ from those in computer science?
Data science primarily relies on languages like Python, R, and SQL for data analysis, statistics, and machine learning. These languages are designed to handle large datasets, perform statistical operations, and create data visualizations. In contrast, computer science emphasizes languages such as Java, C++, and C#, focusing on software development, system design, and performance optimization. While both fields may use some overlapping languages, the purpose and application of the languages differ significantly based on the discipline's goals.
Are there licensing requirements for data scientists or computer scientists in certain regions or industries?
Most data science and computer science roles do not require formal licensing. However, certain industries, such as healthcare, finance, or cybersecurity, may have specific certification or regulatory requirements depending on the region. For example, working with sensitive data may require compliance with data privacy laws or certifications like CISSP for security. Employers may also prefer candidates with industry-recognized credentials, but these are typically optional. It's important to research the expectations in your desired field.
How long does it typically take to complete a degree in data science versus computer science?
Both data science and computer science degrees usually take about four years to complete at the undergraduate level. Graduate degrees, such as a master’s, typically require one to two additional years. Some universities offer accelerated or online programs that may shorten the timeline. Bootcamps and certificate programs in data science can take a few months but do not provide the depth of a full degree. The time investment depends on the level of education and career goals.
What factors should I consider when choosing between data science and computer science?
The main factors to consider are your career goals, interests, and preferred skill set. If you enjoy working with data, statistics, and extracting insights, data science may suit you. If you prefer building software, understanding systems, and learning core computing principles, computer science is a better fit. Additionally, think about job market demand, salary potential, and long-term growth in each field.
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