Data Science vs. Computer Science: What’s the Difference?
6 January, 2026
Data science and computer science have become two of today’s fastest-growing career paths, driven by rapid advances in technology and expanding industry demand. The growth of big data, cloud computing and large language models (LLMs) such as ChatGPT has expanded the need for data science engineers while computer science continues to be essential for developing software, optimizing algorithms and securing digital infrastructure.
For aspiring technology and engineering leaders, choosing between the two fields can be challenging. Both offer strong career opportunities and are compelling areas of study and practice.
While data science and computer science both emphasize technical skills, they focus on different aspects of technology and problem-solving. Understanding these differences can help you decide which path best matches your interests and long-term goals.
What Is Data Science?
The work of data scientists revolves around developing the algorithms and models that enable data analysts to extract knowledge from structured and unstructured data. These algorithms can vary greatly depending on the needs of an organization, but they can generally handle vast amounts of data, create predictive outputs, support stronger data security and empower large language model development.
Data science is a highly interdisciplinary field, requiring skills from a variety of technical disciplines. Students pursuing a data science engineering degree gain technical knowledge from a variety of areas like computer science, mathematics, business, information technology, ethics, graphic design, communication and more.
What Is Computer Science?
Computer science (CS) can seem somewhat self-explanatory: the study of computer hardware, software and the systems related to them. However, this field is very large and offers a variety of specialization areas.
Some computer science professionals focus on the theoretical aspects of the field, researching how computational systems may be able to solve problems in the real world. Others are more interested in the practical side; they tend to work more closely with the development of hardware and software.
What’s the Difference Between Data Science and Computer Science?
Data science and computer science are closely related, as data scientists leverage some of the same skills and knowledge areas as computer scientists. However, data science is highly interdisciplinary and specialized, while computer science has a broader focus on the foundational aspects of computation.
| Data Science | Computer Science | |
|---|---|---|
| Primary Focus | Building models to extract insights from data | Designing, building, optimizing and maintaining software and systems |
| Professional Applications | Predictive analytics, business intelligence, machine learning solutions and research insights | Software development, application design, systems engineering, cybersecurity, machine learning |
| Key Skills | Statistics, machine learning, data visualization | Programming, algorithms, system design |
| Common Tools | Python, R, SQL, Tableau, Hadoop | Java, C++, Python, Git, Linux, LLMs such as ChatGPT |
| Typical Courses | Statistics, predictive modeling, machine learning, data visualization and ethics | Algorithms, data structures, operating systems, software engineering, databases, networking |
Focus Area and Professional Application
While the two fields share technical foundations, the way they apply that knowledge differs significantly.
How Is Data Science Applied?
Data science emphasizes the creation of models that enable analysis and interpretation of data. It tends to guide the decision-making process in organizations across a variety of industries, especially in business operations, health-care analysis and finance.
Data science professionals may find careers developing predictive analytics models, gathering business intelligence, engineering machine learning solutions and aggregating research insights. Many data science professionals also use large language models, such as ChatGPT, as a model for various machine learning projects.
How Is Computer Science Applied?
Computer science, on the other hand, is typically applied in the technology industry (although it can be cross-disciplinary as well) and focuses on:
- Software development and innovation
- Cybersecurity
- Systems engineering and architecture
- Computation
- Software development
- Application design
Core Skills Required for Success in the Field
The differences in focus are reflected in the skills emphasized in each profession:
- Data science skills: Programming, machine learning, AI, CRM software, database software, deep-learning libraries, distributed computation systems, advanced mathematics (statistics, probability and reasoning, calculus, linear algebra)
- Computer science skills: Programming and software development, algorithm and data structure design, systems and networks, computer architecture, hardware, cybersecurity, software testing and QA
Day-to-Day Tools and Technologies
Computer science uses development frameworks and simulation tools for system testing and other applications. Data science leverages all of these, in addition to business intelligence platforms.
- Tools required for data science: Python, R, SQL, Hadoop, Spark, Tableau, Power BI
- Tools required for computer science: Java, C++, Python, Git, Linux, large language models like ChatGPT and Gemini, cloud platforms and IDEs like Visual Studio
Typical Coursework
What Is the Curriculum for Data Science Programs?
The curriculum of a data science program will offer foundational courses that reflect the field’s focus, required skills and common tools: statistics, predictive modeling, machine learning, data visualization and ethics. Courses in data visualization, AI ethics, or natural language processing can fine tune a data science program even further.
What Is the Curriculum for Computer Science Programs?
Computer science programs run the gamut in terms of coursework, and courses may cover algorithms, data structures, operating systems, software engineering, databases and networking. Following these core areas, students tend to specialize in areas such as mobile app development, robotics or cybersecurity.
Similarities Between Data Science and Computer Science
Data science and computer science can be highly interrelated — data science is often considered to be a subfield of computer science, especially in an academic setting. Data scientists leverage computer science knowledge every day when they write code, build machine learning modules and deploy programming-based solutions. And computer scientists need to at least understand data science to build systems that enable organizations to analyze data efficiently.
They also share many practical similarities and have overlapping skill sets. Both fields require programming skills, knowledge of algorithms and problem-solving abilities. Data structures, databases and software tools are used in each field.
These similarities in foundational skills enable technology professionals to pivot from one field to another, provided that they gain the additional specialized knowledge needed for each field. Whether a professional earns a data science or computer science degree, they will gain vital cross-functional abilities that support a variety of technology careers.
Potential Careers in Data Science vs. Computer Science
While many people know that they want to pursue a career in either data science or computer science, they may not know the full extent of the potential career opportunities in these vibrant, high-growth fields.
Potential Roles
Data science includes more than data analysis, and computer science can offer more than software development roles. As such, there is a wide variety of potential career opportunities in both fields.
Jobs in Data Science
Professionals interested in data science may be able to pursue roles such as:
- Data scientist
- Machine learning engineer
- Data analyst
- Business intelligence analyst
- Big Data engineer
Jobs in Computer Science
Those interested in computer science might pursue careers such as:
- Software engineer
- Systems architect
- Cybersecurity specialist
- AI developer
- Computer systems analyst
Computer Scientist vs. Data Scientist
The core careers in both fields are shaped by the differences in focus, skills and applications.
- Data scientists create algorithms that enable analysts to gather insights from datasets, often in business analytics, health care, finance or tech.
- Computer scientists focus on software and system development, algorithm research and computing infrastructure.
Career Outlook & Potential Salary
According to the U.S. Bureau of Labor Statistics, both fields are predicted to grow much faster than the average rate of job growth, with demand being driven by data proliferation, software development needs and AI adoption.
Data Science Salary & Career Outlook
The national average annual salary for data scientists is $112,590 per year, and $174,800 in California.
Data science careers are expected to grow by 34% through 2034.
Computer Science Salary & Career Outlook
On average, computer scientists earn $140,910 per year* on a national level, and $148,200 in California.
Careers in computer science have an anticipated growth rate of 20% through 2034.
*Also includes information research scientists
Salary will vary by experience, responsibilities, location and other factors. The UCLA Samueli School of Engineering does not project job placements and salaries for its graduates.
What Type of Program Is Right for Me?
Choosing which field is right for you can depend on a variety of factors. It is important to remember that a career in either area is not guaranteed, but advanced study can provide aspiring professionals with crucial skills to potentially improve their chances.
Generally, a career in data science suits those who like analyzing information, spotting trends and using data to support better decision-making. Computer science is a good fit for those who enjoy programming, developing algorithms and designing software systems.
- Choose data science if you want to build algorithms, analyze data, make predictions through machine learning and meaningfully contribute to big-picture business objectives. Comfort with statistics, machine learning and data visualization is key.
- Computer science is a better fit if you enjoy designing systems, building software and creating or improving computer technologies. Coding and system architecture are central skills.
- Or, you can potentially combine both areas. For example, machine learning engineers tend to draw on both computer science and data science skills, offering opportunities to work on AI-driven applications.
Additional considerations to keep in mind include preferred industries, work environments and long-term career goals. However, both paths offer flexibility in tasks and opportunities for advanced study or specialization.
Frequently Asked Questions (FAQs)
The national average annual salary for data scientists is $112,590 per year according to the U.S. Bureau of Labor Statistics.
Salary will vary by experience, responsibilities, location and other factors. The UCLA Samueli School of Engineering does not project job placements and salaries for its graduates.
The national average annual salary for computer scientists is $140,910 per year according to the U.S. Bureau of Labor Statistics.
Salary will vary by experience, responsibilities, location and other factors. The UCLA Samueli School of Engineering does not project job placements and salaries for its graduates.
A bachelor’s degree in statistics, computer science, mathematics, physics, engineering or a related field is generally considered an entry-level requirement.
Many professionals also pursue a master’s degree in related fields to gain advanced technical skills that can support potential career growth.
Why Choose UCLA Samueli for Your Data Science Master’s Degree?
The UCLA Samueli School of Engineering offers an online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering (MSOL: DATA SCIENCE ENGR) that prepares students to solve problems and harness data ethically using responsible artificial intelligence through a curriculum of five core courses and four electives.
The UCLA Samueli School of Engineering is a tightly knit community of more than 200 full-timefaculty members, nearly 7,000 undergraduate and graduate students, as well as 50,000 active alumni. Known as the Birthplace of the Internet, UCLA Samueli is also where countless other fields took some of their first steps — from artificial intelligence to reverse osmosis, from mobile communications to human prosthetics. In 2021, UCLA became the first university to win an XPRIZE, with a UCLA Samueli team awarded a $7.5 million grand prize in the NRG COSIA Carbon XPRIZE.
Times Higher Education lists UCLA among the top 10 universities for engineering in the U.S. and top 20 in the world. U.S. News & World Report has ranked the overall Master of Science in Engineering Online program No. 1 in the nation.
Request a program brochure to learn more, or start the application process today.
Request Information
To learn more about the Online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering, contact an enrollment specialist at (424) 443-7385 or fill out the form below to download a free brochure.
