Computer Science vs Data Science: Comparing Degrees, Careers, and Salaries

 

Estimated Reading Time: 7 minutes

Key Takeaways

• Computer science focuses on building computational systems, algorithms, and software, while data science extracts insights from data using statistics and machine learning
• CS degrees emphasize programming languages, systems design, and software engineering; DS degrees prioritize statistical analysis, data visualization, and predictive modeling
• Both fields offer lucrative career paths with six-figure salaries projected for 2025, with data science roles trending slightly higher due to AI and big data demands
• Software engineering and data science require different skill sets: SE uses C++/Java for building systems; DS uses Python/R for analytics and modeling
• Computer science degrees provide broader foundational skills applicable across multiple tech sectors, while data science programs often build on CS fundamentals

Introduction

The computer science vs data science debate has become increasingly relevant as students and professionals navigate their educational and career choices in tech. Both fields offer exceptional opportunities, but they serve fundamentally different purposes: computer science centers on building computational systems and software, while data science focuses on extracting meaningful insights from data.

If you're weighing your options between these two high-demand paths, you're not alone. This guide breaks down the essential differences between CS and DS degrees, explores career trajectories, and examines salary outlooks including computer science salaries 2025. We'll also compare related fields like computer science vs IT degree and software engineering vs data science to help you make an informed decision about your future in technology.

What Is Computer Science?

Computer science is the comprehensive study of computers and computational systems, focusing on theoretical foundations like algorithms, programming, and software engineering to design efficient systems. This discipline forms the backbone of modern technology, from the apps on your phone to the servers powering the internet.

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Core Foundations

The theoretical foundations of computer science include:

• Data structures and algorithms that optimize how information is stored and processed
• Programming languages such as C++, Java, and Python
• Systems design principles for building scalable software
• Computational logic and problem-solving methodologies

Career Paths in Computer Science

Typical career paths for computer science graduates include software development and systems architecture. These professionals write code, debug complex systems, and build applications or frameworks that millions of people use daily.

These roles are particularly well-suited for logical thinkers who enjoy coding and systematic problem-solving. The work combines creativity with precision, requiring both innovative thinking and attention to detail.

For a comprehensive look at opportunities in this field, check out our section on best tech jobs for computer science graduates later in this article.

What Is Data Science?

Data science uses statistics, machine learning, and data analysis to derive insights and predictions from datasets, enabling organizations to make data-driven decisions. This interdisciplinary field has exploded in importance as companies recognize the competitive advantage of understanding their data.

Core Curriculum and Tools

Data science programs cover essential topics including:

• Statistics and probability theory for understanding data patterns
• Machine learning models for predictive analytics
• Data visualization tools such as Tableau and Power BI
• Data cleaning and preprocessing techniques
• Programming with Python (pandas, NumPy), R, SQL, and TensorFlow

Common Data Science Roles

Common roles in this field include data analyst, machine learning engineer, and business intelligence specialist. These positions involve analyzing datasets, building predictive models, and presenting actionable insights to stakeholders.

Growing Demand

Demand for data scientists is surging due to big data needs across industries. Interestingly, there's significant skills overlap in programming and mathematics with computer science vs data science backgrounds. Many successful data scientists actually start with CS degrees before specializing in analytics and machine learning.

Computer Science vs Data Science: Degree Comparison

Understanding the fundamental differences between these degrees is crucial for making the right educational choice. Computer science degrees prioritize theory and systems, covering algorithms, software engineering, and hardware architecture. In contrast, data science focuses on data analytics and modeling, emphasizing statistics and machine learning applications.

Key Differences in Curriculum

Curriculum Focus:

• Computer Science: Programming fundamentals, algorithms, systems design, and computational theory
• Data Science: Statistics, machine learning, data visualization, and analytical modeling

Skillsets Developed:

• Computer Science: Software engineering principles, computational logic, and system architecture
• Data Science: Statistical analysis, data mining, and predictive analytics

Example Coursework

Computer Science courses typically include:

• Data structures and algorithms
• Compiler design and optimization
• Computer networking and distributed systems

Data Science courses typically include:

• Regression models and statistical inference
• Predictive analytics and machine learning
• SQL database management and data warehousing

Practical Projects

Computer Science students work on:

• Building mobile or web applications from scratch
• Optimizing system performance and resource allocation

Data Science students work on:

• Creating interactive dashboards for business intelligence
• Developing predictive models for forecasting trends

Program Availability

Computer science typically offers broader foundational skills through established undergraduate programs. Data science is often pursued as a postgraduate specialization, though undergraduate programs are emerging rapidly to meet market demand.

Computer Science vs IT Degree

Many students also wonder about the computer science vs IT degree distinction. While related, these fields serve different purposes in the tech ecosystem.

What Is an IT Degree?

IT degrees emphasize applied computing, network administration, and technical support. The focus is on hands-on management of hardware, software, and security systems rather than theoretical computer science.

Key Contrasts

Unlike CS's deep focus on programming and algorithmic theory, IT prioritizes practical operations like troubleshooting networks, managing infrastructure, and implementing security protocols. IT professionals keep systems running smoothly but typically don't design the underlying software architecture.

Career Outcomes

Computer science graduates pursue innovative development roles in software engineering, AI, and system architecture. IT graduates lead to support and administration positions with less emphasis on algorithms but strong industry demand for operations, help desk management, and network administration.

Software Engineering vs Data Science

The software engineering vs data science comparison highlights two distinct but equally valuable career paths within tech.

Role Comparison

Software engineering builds and maintains software systems using programming and design principles. Engineers create the applications, platforms, and systems that power our digital world.

Data science extracts insights via analysis and modeling, turning raw data into actionable business intelligence.

Skills and Tools

Software Engineering requires:

• Programming languages like C++, Java, and JavaScript
• Debugging and testing frameworks
• Linux/Unix systems knowledge
• Version control systems like Git

Data Science requires:

• Python and R for statistical computing
• Statistical analysis and hypothesis testing
• Data visualization tools like Tableau
• Machine learning frameworks like TensorFlow and PyTorch

Workflow Differences

Software Engineering workflow:

• Write and review code
• Test functionality and performance
• Deploy applications to production environments

Data Science workflow:

• Clean and preprocess raw data
• Build and train predictive models
• Visualize insights for stakeholders

Pros and Cons

Software Engineering:

Pros: Stable demand across industries, creative problem-solving opportunities, clear career progression

Cons: Rapid technology changes require constant learning, debugging can be time-consuming

Data Science:

Pros: High growth trajectory, impactful analytics that drive business decisions, interdisciplinary work

Cons: Data cleaning can consume 60-80% of project time, requires strong mathematical foundation

Both career paths require exceptional problem-solving abilities but differ fundamentally in their outputs: software engineers create systems and applications, while data scientists produce insights and predictions.

Best Tech Jobs for Computer Science Graduates

Computer science degrees open doors to some of the best tech jobs for computer science graduates in today's market. Here are the top career paths:

Software Engineer

Software engineers build applications across web, mobile, and desktop platforms. This role requires strong programming skills and offers high growth potential in technology firms, startups, and enterprises.

DevOps Engineer

DevOps engineers manage deployment pipelines and cloud infrastructure. Cloud computing skills are essential, and prospects remain strong as companies migrate to AWS, Azure, and Google Cloud platforms.

Cybersecurity Analyst

Cybersecurity analysts protect systems from threats and vulnerabilities. Professional certifications like CISSP and CEH boost entry opportunities in this rapidly expanding field.

AI Developer

AI developers design algorithms and intelligent systems, with significant overlap in data science and machine learning. This emerging field combines computer science fundamentals with advanced mathematical modeling.

Entry Requirements and Growth

Entry into these roles typically requires a computer science degree, with robust growth across software development, cloud computing, and artificial intelligence sectors. Major employers include technology giants, financial institutions, healthcare companies, and government agencies.

 

A CS degree from South Dakota Mines equips you with hands-on projects and practical experience that prepares you for these competitive career paths.

Salary Outlook: Computer Science Salaries 2025

Understanding computer science salaries 2025 helps you plan your financial future and make informed career decisions.

Projected Median Salaries

In 2025, computer science roles command six-figure US base salaries, with software engineers and senior positions earning substantial compensation due to persistent talent shortages. Data science roles edge slightly higher, with median salaries around $130,000 or more, driven by explosive demand for analytics capabilities.

Factors Influencing Compensation

Several factors impact your earning potential:

• Location: Tech hubs like San Francisco, Seattle, and New York offer higher salaries but also higher living costs
• Industry: Technology and finance sectors typically pay premium salaries compared to non-profits or education
• Experience: Senior roles command significantly higher compensation than entry-level positions

Salary Trends

Computer Science Software Engineers: $120,000+ base salary, rising steadily with seniority and specialization

Data Scientists: Slightly higher at $130,000+ average, reflecting the specialized statistical and machine learning expertise required

Trend analysis shows both fields growing, with data science experiencing faster growth post-2020 due to AI adoption and big data initiatives. Computer science maintains a steady upward climb with consistent demand across all tech sectors.

Choosing the Right Path

Making the right choice between computer science vs data science requires honest self-assessment and understanding of market realities.

Key Considerations

Assess Your Interests:

Do you prefer coding and building systems, or analyzing data and extracting insights? Computer science suits those who enjoy creating software architecture, while data science appeals to those fascinated by patterns and predictions.

Evaluate Your Strengths:

• Logic and systematic thinking: Computer science
• Mathematics and statistical analysis: Data science

Both fields require problem-solving abilities, but they apply them differently.

Consider Your Desired Environment:

• Development teams and agile workflows: Software engineering roles
• Business analytics and stakeholder presentations: Data science positions

Market Demand and Future Growth

Computer science offers broad versatility across multiple industries and job functions. Data science is booming with the data explosion, particularly in healthcare, finance, and e-commerce sectors.

Combined or specialized degrees that blend both disciplines can be particularly advantageous. Future growth remains strong for both fields, especially in AI and machine learning applications where the two domains overlap significantly.

Why South Dakota Mines?

South Dakota Mines offers robust CS and Data Science programs distinguished by hands-on laboratories, cutting-edge research in algorithms and machine learning, and comprehensive skill development in Python, statistics, and system design.

Our career services provide valuable internship opportunities through established industry partnerships, resulting in exceptional job placement rates in technology companies nationwide. Students benefit from small class sizes, personalized faculty mentorship, and real-world project experience.

Whether you're drawn to computer science vs data science, South Dakota Mines prepares you with the technical expertise and practical experience employers demand. Request information or apply today to launch your career in these high-demand fields.

Frequently Asked Questions

What's the main difference between computer science and data science?

Computer science focuses on building computational systems, software, and algorithms, while data science concentrates on extracting insights from data using statistics and machine learning. CS emphasizes programming and system architecture; DS emphasizes analytics and modeling.

Can I transition from computer science to data science?

Yes, many data scientists start with computer science degrees. The programming skills, mathematical foundation, and logical thinking from CS provide an excellent base for data science specialization through additional coursework in statistics and machine learning.

Which pays more: computer science or data science?

Both fields offer competitive six-figure salaries in 2025. Data science roles average slightly higher at $130,000+ due to specialized skills, while software engineering positions typically start at $120,000+. Location, experience, and industry significantly impact compensation.

Is data science harder than computer science?

Both fields present challenges in different areas. Computer science requires mastering complex algorithms and system design, while data science demands strong statistical knowledge and mathematical modeling. Your personal strengths determine which feels more difficult.

What's the difference between a CS degree and an IT degree?

Computer science focuses on theoretical foundations, programming, and software development, while IT degrees emphasize practical application, network administration, and technical support. CS leads to development roles; IT leads to operations and support positions.

Do I need a master's degree for data science?

Many data science positions prefer or require graduate degrees due to the specialized statistical and machine learning knowledge needed. However, undergraduate programs are emerging, and CS graduates can enter the field with additional training and certifications.

Conclusion

The computer science vs data science decision ultimately depends on whether you're more excited about building computational systems or extracting insights from data. Computer science excels in creating software, applications, and technological infrastructure, while data science thrives on analytics, predictions, and data-driven decision-making.

Both fields offer rewarding careers with competitive salaries, including impressive computer science salaries 2025 projections in the six-figure range. The overlap in programming and machine learning skills means transitions between fields remain possible throughout your career.

Take time to evaluate your personal goals, assess your strengths in logic versus analytics, and consider which work environment excites you most. Explore South Dakota Mines' comprehensive programs in both computer science and data science to find the perfect educational foundation for your tech career. The future is bright in both fields—choose the path that aligns with your passion and strengths.