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Master’s in Artificial Intelligence vs. Data Science

Key Takeaways:

  • A master’s in artificial intelligence is ideal for those interested in automation, machine learning, and creating human-like systems—often leading to roles in research, robotics, and advanced software engineering.

  • A master’s in data science focuses on extracting insights from structured and unstructured data using statistical methods, leading to careers in analytics, business intelligence, and prediction modeling.

  • AI programs are typically more math- and theory-heavy, while data science programs are often more application-focused and flexible in prerequisites.

  • Salaries are strong for both degrees, with machine learning engineers and AI specialists sometimes earning slightly more due to specialization.

  • Choosing between the two comes down to career goals: research and innovation vs. business-driven problem-solving.

https://ischool.syracuse.edu/what-is-data-science/

Over the next five years, big data and artificial intelligence specialists will be among the fastest-growing jobs in the world.1 Driven by advances in artificial intelligence (AI) and the exponential growth of data, these careers are in high demand across countless industries—from healthcare to business to government services.2,3

If you’re interested in a career in one of these fields, a graduate degree can help you get there. But should you earn a master’s in artificial intelligence or a master’s in data science?

While there is overlap between AI and data science, they are distinct fields with different purposes. Data science focuses on extracting valuable insights from large datasets. AI focuses on creating machines that are capable of performing tasks with human-like intelligence.

In this article, we’ll explore the distinctions between artificial intelligence vs data science, as well as potential career paths and job outlooks. Then, we’ll compare the curriculum and prerequisites for master’s programs in artificial intelligence and data science to help you choose the right program for you.

What Is a Master’s in Artificial Intelligence?

The field of artificial intelligence focuses on creating algorithms and computational models that mimic human cognition. It develops computer systems that can learn, reason, solve problems, or make decisions. 

If designing intelligent systems interests you as a career path, a master’s in artificial intelligence can equip you with the theoretical knowledge and practical skills to get there. Core courses often include:

  • Neural Networks

  • Machine Learning

  • Reinforcement Learning

  • Ethics and Responsible AI

You can also tailor your AI program to align with your specific interests or career goals. Common specializations include:

  • Natural Language Processing (NLP):

    Enables computers to understand, interpret, and generate human language. You see this machine learning model in action when you interact with search engines, chatbots, or translation apps.

  • Robotics:

    Combines AI with mechanical engineering to create autonomous machines that perceive and interact with their environment. You can find robots in a variety of industries: delivering packages, assembling products, and even assisting in surgeries.

  • Computer Vision:

    Trains systems to interpret and analyze visual information, like recognizing patterns and features in images or videos. Computer vision powers technologies such as facial recognition and augmented reality.

  • Deep Learning:

    Teaches computers to learn from experience, using multiple layers of processing (like the human brain) to make more complex decisions. For example, deep learning helps your favorite streaming app know what you’d like to watch next.

https://ischool.syracuse.edu/what-is-data-science/

What Is a Master’s in Data Science?

Data science uses analytical tools and techniques to transform raw data into actionable insights. This multidisciplinary field combines mathematics, statistics, computer science, and specific domain expertise.

In healthcare, for example, data scientists analyze patient data and medical research to forecast disease progression and inform treatment plans. In finance, banks use data science to reduce fraud and optimize their credit scoring models to make better lending decisions.

If you’re interested in the power of data to inform decision-making, consider a master’s in data science. You can expect to take courses that are both theoretical and practical. Core courses typically include:

  • SQL

  • Python

  • Data Mining

  • Statistics

  • Data Visualization

A data science program will teach you how to combine technical knowledge with analytical thinking. You’ll develop key skills like:

  • Data Wrangling:

    Cleaning, structuring, and enriching raw data to prepare it for analysis or machine learning.

  • Statistical Modeling:

    Using statistical techniques to uncover patterns and identify relationships in data sets.

  • Predictive Analytics:

    Combining statistical modeling and machine learning to forecast future trends and behaviors.

  • Data Storytelling:

    Translating complex analysis into clear, compelling presentations for non-technical decision makers.

AI vs Data Science Curriculum & Core Skills

Both AI and data science programs explore aspects of computer science, require analytical thinking, and develop your technical skills. Yet each program’s curriculum reflects its distinct goals. Let’s compare AI vs data science programs.

Area

Master’s in AI

Master’s in Data Science

Focus

Human-like intelligence, decision-making

Insight from data, predictive analysis

Common Tools

PyTorch, TensorFlow, MATLAB

Python, R, SQL, Tableau

Key Concepts

Neural nets, automation, robotics

Data wrangling, modeling, business analytics

Math Intensity

Very High
(Calculus, Linear Algebra, Statistics)

High
(Statistics, Probability, Linear Algebra)

A master’s in AI tends to be more theory-heavy with its emphasis on algorithm development and cognitive modeling. Expect to explore advanced topics like neural networks, reinforcement learning, and robotics—all of which require a strong grasp of calculus, linear algebra, and statistics.

Students of AI also learn the common tools of the trade:

  • PyTorch:

    A popular open-source library for prototyping deep learning models

  • TensorFlow:

    An open-source deep learning framework for more advanced modeling and visualization

  • MATLAB:

    A programming and numerical computing platform for developing algorithms and systems

Meanwhile, a master’s in data science focuses more on application—using computational tools and machine learning models to solve real-world problems. Expect to learn how to build data dashboards, create predictive models, and present insights to business stakeholders. Data science is also mathematically rigorous, especially in statistics, but it generally requires less theoretical math than a master’s in AI.

Data science students learn these common tools:

  • Python:

    The primary programming language used in data science for data wrangling and modeling

  • R:

    A programming language often used for statistical analysis and data visualization

  • SQL:

    A programming language that extracts and manages data stored in relational databases

  • Tableau:

    A popular business intelligence platform used to create dashboards and visual reports

Career Paths & Job Titles

Once you graduate with a master’s in AI or data science, you can pursue a variety of high-demand career paths across many industries.

Graduates interested in an artificial intelligence career often take on roles like:

  • Machine Learning Engineer

  • AI Researcher

  • Robotics Engineer

In these research-intensive AI jobs, you develop new algorithms, neural network architectures, and decision-making models to design intelligent systems. Working at the frontier of AI, you push the boundaries of machine intelligence and build the next generation of smart technologies.

AI jobs are in demand across industries. Tech companies like Google and Meta hire machine learning engineers and AI researchers for their research labs. Automotive companies seek out robotics engineers to build autonomous driving systems. The federal government recruits AI professionals to improve defense technologies and surveillance drones.

On the other hand, graduates interested in data science jobs pursue careers like:

These roles focus on applied problem-solving. They analyze large datasets to extract insights, build predictive models, and inform business strategies.

In the automotive industry, for example, you may find data scientists optimizing supply chains. In the public sector, data analysts may work on census models or public health tracking. In a large hospital, business intelligence specialists might develop dashboards and visualizations that monitor emergency room wait times or patient outcomes by department.

With more experience, data science graduates may advance to a position as a data architect, where they design the overall structure and governance of an organization’s data management systems.

Whether you’re interested in AI jobs or data science jobs, employers in a variety of industries are looking for talented technologists.

AI vs Data Science Salary & Job Outlook

Graduates of both AI and data science master’s programs have a positive job outlook and higher-than-average salaries.

AI professionals working as machine learning engineers in the U.S. earn an average of $155,000 per year.4 Those working at major tech companies or in tech hubs like Silicon Valley tend to earn more than $200,000.5 The top-paying industries for machine learning engineers are:

  • Personal Consumer Services

  • Information Technology

  • Retail and Wholesale4

Data scientists are also well compensated, earning an average salary of $112,590.6 According to Glassdoor, data scientists earning closer to $150,000 per year work in these top-paying industries:

  • Personal Consumer Services

  • Arts, Entertainment, and Recreation

  • Agriculture7

Demand for both fields far outpaces the average. With more businesses and organizations relying on data to inform their decisions, the U.S. Bureau of Labor Statistics projects a 36% growth in the employment of data scientists by 2033.6 In the first quarter of 2025 alone, data scientist roles grew 4.2%.3

AI jobs are growing even faster, driven by breakthroughs in generative AI and automation tools.1 All AI-related roles have increased an average of 25% in the past year—with machine learning engineers experiencing the fastest growth at 41% year-over-year.3

Degree Accessibility & Prerequisites

A master’s in data science and a master’s in artificial intelligence both require a strong academic foundation, including prior experience with math, coding, and statistics. Yet these programs differ in terms of accessibility and specific prerequisites.

Master’s programs in data science tend to be more flexible, attracting students from diverse academic backgrounds. As long as students demonstrate competency in essential technical skills, programs accept applicants with undergraduate degrees in fields like:

  • Mathematics

  • Business

  • Sciences, such as biology or physics

Master’s programs in AI, however, tend to be more selective due to the technically rigorous curriculum. AI program requirements include: 

  • A strong computer science background

  • Proficiency in Python programming 

  • Advanced math skills, such as linear algebra, calculus, and probability

Whichever path you choose, strong quantitative and technical skills will help you succeed.

Choosing the Right Degree for Your Goals

So, how do you choose between a master’s in AI and a master’s in data science? The answer depends on your interests, strengths, and career goals.

If you’re fascinated by how machines learn, think, and make decisions, a master’s in AI may be the right choice for you. You’ll enjoy working on the cutting edge of technology—contributing to innovations in areas like machine learning, robotics, or autonomous systems. With a theory- and math-intensive degree in AI, you’ll be prepared to work in research and development roles like machine learning engineer or AI researcher.

On the other hand, if you love diving into data to uncover patterns that help people make better-informed decisions, a master’s in data science might be a better fit for you. With its focus on applied analytics, this degree will prepare you to solve business problems using data. You’ll have many career opportunities to choose from as a data scientist, data analyst, or business intelligence specialist in almost any industry.

Potential Master’s Programs to Explore

Master’s in Artificial Intelligence

Master’s in Data Science

Conclusion

Both a master’s in artificial intelligence and a master’s in data science prepare you to succeed in high-demand fields with strong career potential. Ultimately, choosing the right program comes down to what you excel in, what excites you most, and where you want to take your career. 

If you have a strong technical background and a desire to push the frontiers of artificial intelligence, consider a master’s in AI. If you want to apply data analysis, modeling, and visualization to solve real-world problems, aim for a master’s in data science. 

Ready to find the right program for you? Explore data science programs or machine learning programs today.

Sources

1 - https://www.weforum.org/publications/the-future-of-jobs-report-2025/ 

2 - https://www.bls.gov/careeroutlook/2023/data-on-display/data-occupations.htm 

3 - https://www.veritone.com/blog/ai-jobs-growth-q1-2025-labor-market-analysis/ 

4 - https://www.glassdoor.com/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm 

5 - https://www.indeed.com/career/machine-learning-engineer/salaries 

6 - https://www.bls.gov/ooh/math/data-scientists.htm 

7 - https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm