Data Science vs. Data Analytics vs. AI: Which Master’s Degree Is Right for You?

Key Takeaways

• Data analytics answers specific questions with existing data, while data science covers the full data life cycle. Business analytics turns those findings into recommendations for leadership.
• A machine learning degree focuses on how models learn, while an AI degree covers the full intelligent system.
• The U.S. Bureau of Labor Statistics projects 35% growth in data scientist employment through 2035, with a median annual wage of $120,230.
• Computer and information research scientists, a role closely tied to AI, earn a median annual wage of $140,300 as of May 2025.
• The right degree depends on how you like to work with data. Data analysts who want to move into data scientist roles are a natural fit for a data science master’s.

Data science, data analytics, business analytics, artificial intelligence (AI) and machine learning (ML) all involve working with data, and the terms sometimes get used interchangeably. But the master’s degrees behind them are not interchangeable. Each field asks a different question of data, produces a different kind of output and points toward a different set of roles.

If you’re considering a master’s degree in one of these areas, you probably already know you want to work with data. The real question is which kind of data work fits your background and where you want your career to go.

This guide compares all five fields side by side. You’ll find plain-language definitions, an at-a-glance comparison of how each field uses data, typical coursework, common job titles and career outlook information. You’ll also find “choose this path if” criteria to help you narrow your options.

How Are Data Science, Data Analytics, Business Analytics, AI and Machine Learning Different?

Because these five fields share tools and vocabulary, it helps to start with a clear definition of each one before comparing them side by side.

Definitions adapted from Techopedia.

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Data Science Degree Breakdown

A master’s degree in data science prepares you to work across the full data life cycle, from collecting and cleaning raw data to building models and communicating what the results mean. Programs combine statistics, programming and machine learning with the communication and problem-solving skills needed to tackle real-world challenges in a wide range of data-rich industries.

Typical topics include, but are not limited to:

• Research design
• Data engineering
• Data visualization
• Machine learning
• Data cleansing/auditing
• Statistical analysis and programming
• Predictive modeling
• Text mining

Ideal for: Professionals already working in data-related or analytical roles, as well as curious professionals who want to deepen their understanding of structured and unstructured data and use it to solve real-world problems.

Common career paths: Data scientist, data engineer, data analyst

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Data Analytics Degree Breakdown

A master’s degree in data analytics focuses on examining existing data to answer specific questions and guide decisions. Programs train you in the concepts and techniques for organizing, cleaning, analyzing and visualizing large amounts of data, along with the tools analysts use to do that work.

The four types of data analytics

Data analytics programs typically cover four types of analysis. Each answers a different question:

• Descriptive analytics answers what happened. An example is a monthly report comparing customer orders with the previous month.
• Diagnostic analytics answers why it happened. An example is digging into the data to find out why support requests spiked after a product update.
• Predictive analytics answers what is likely to happen. An example is using historical trends to forecast next quarter’s demand.
• Prescriptive analytics answers what should happen next. An example is recommending the staffing or inventory levels that best meet that forecasted demand.

Typical topics include, but are not limited to:

• Fundamentals of data analytics
• Statistics for data analysis
• Data mining and analytics
• Programming in Python
• R for data analysts
• Advanced data visualization
• SQL for data analysis

Ideal for: Mid-career IT professionals and people with a computer science background who want to specialize in analytics.

Common career paths: Data analyst, business intelligence (BI) analyst, reporting analyst

Business Analytics Degree Breakdown

A master’s degree in business analytics prepares you to apply data analysis directly to business decisions. You learn to extract data to explain trends, predict future performance and identify the best course of action. You also learn to translate those findings into recommendations that stakeholders and leadership can act on. Data analytics centers on the analysis itself, while business analytics centers on what the organization should do with the results.

Typical topics include, but are not limited to:

• Data analysis and business concepts
• Statistics
• Data management
• Prescriptive analysis
• Financial analytics
• Operations and supply chain analytics
• Technical communication

Ideal for: Professionals who want to combine specialized analytics skills with business knowledge, especially those who enjoy turning data trends into actionable goals for leadership.

Common career paths: Business analyst, management analyst, operations analyst

Artificial Intelligence Degree Breakdown

A master’s degree in artificial intelligence prepares you to design, build and deploy intelligent systems that can analyze information and act on it with varying degrees of autonomy. These programs give you the technical foundation to apply AI techniques to real-world problems, with an emphasis on doing so ethically and responsibly. Machine learning focuses on the algorithms that learn from data, while AI looks at the full system, which can include rules-based logic, language processing, neural networks and robotics.

Typical topics include, but are not limited to:

• Artificial intelligence frameworks
• Machine learning
• Statistical modeling
• Natural language processing (NLP)
• Neural networks
• Deep learning foundations
• Human-computer interaction
• Computational aspects of robotics

Ideal for: Professionals who want to build AI-based tools and intelligent systems that automate data-driven decision-making across a wide variety of industries.

Common career paths: AI engineer, computer vision engineer, AI researcher

Machine Learning Degree Breakdown

A master’s degree in machine learning takes a deeper look at the mathematics and algorithms that allow computers to learn from data. Coursework typically explores the theory behind machine learning and AI, then teaches you to apply specific ML tools to a variety of real-world problems. Because machine learning is a subset of artificial intelligence, the two degrees often overlap. That said, an ML program spends more time on how models learn and less on the broader systems those models power.

Typical topics include, but are not limited to:

• Computational learning theory
• Advanced machine learning
• Foundations of graphical models
• Computer vision
• Natural language processing
• Deep learning
• Reinforcement learning

Ideal for: Professionals who want to understand how machine learning algorithms work and deploy them to generate insights from data.

Common career paths: Machine learning engineer, natural language processing specialist, AI researcher

What Skills Are Needed for Data Science-Related Jobs?

Job responsibilities in these fields vary depending on the organization and the role. Programming and quantitative skills appear across all five, and the ability to communicate findings is essential in nearly every role. Beyond that shared foundation, each field emphasizes a different mix of skills:

Salary and Career Outlook by Field

Salary and job growth vary widely across these five fields. The figures below draw on the U.S. Bureau of Labor Statistics (BLS) and other current sources. Because the BLS doesn’t track every one of these fields as its own occupation, some sections use the closest related occupation BLS does track.

Data Science

Demand for data scientists continues to grow across industries. The U.S. Bureau of Labor Statistics projects employment of data scientists to grow 35% through 2035, much faster than the average for all occupations, with about 24,800 openings projected each year. BLS reports a median annual wage of $120,230 for data scientists, with the top 10% earning more than $199,130.

USD students and alumni work at organizations including Boeing, Cisco, Lionsgate, the National Science Foundation, Northrop Grumman, Qualcomm, Rady Children’s Hospital, SDG&E, Siemens, Stanford University, Starbucks, State Farm, the U.S. Air Force, Viasat and The Walt Disney Company.

Data Analytics

Data analytics roles span many industries and include positions such as health care data analyst, IT systems analyst and digital marketing analyst.

One closely related occupation tracked by the U.S. Bureau of Labor Statistics is operations research analyst. These professionals manage and evaluate data to improve business operations, supply chains, pricing models and marketing. BLS projects employment of operations research analysts to grow 12% through 2035, much faster than the average for all occupations, and reports a median annual wage of $88,940 as of May 2025.

Business Analytics

One closely related occupation tracked by the BLS is management analyst. These professionals analyze financial and other data and recommend ways to improve an organization’s efficiency. BLS projects employment of management analysts to grow 10% through 2035, much faster than the average for all occupations, with about 94,100 openings projected each year. The median annual wage was $101,860 as of May 2025, with the top 10% earning more than $171,640. Pay varies widely based on the position and your experience.

Artificial Intelligence

One closely related occupation tracked by the BLS is computer and information research scientist. BLS expects these professionals’ expertise to be needed in creating new AI technologies, and notes that the role typically requires at least a master’s degree in computer science or a related field. BLS projects employment in this occupation to grow 22% through 2035, much faster than the average for all occupations. The median annual wage was $140,300 as of May 2025, with the top 10% earning more than $230,630.

Machine Learning

Machine learning roles rank among the fastest-growing jobs worldwide. In its Future of Jobs Report 2025, the World Economic Forum ranked AI and machine learning specialists among the three fastest-growing jobs in percentage terms through 2030. Specialty roles include natural language processing specialists, who focus on equipping machines to understand human language.

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How to Choose the Right Program

The right program depends less on which field is growing fastest and more on how you like to work with data. Use the criteria below to find the path that fits your interests and goals.

Choose data science if you:

• Want to work across the full data life cycle, from collecting and cleaning raw data to building predictive models
• Enjoy combining statistics and programming to solve open-ended problems
• Want a broad foundation that spans analytics, data engineering and machine learning
• Already work as a data analyst and want to move into a data scientist role

Choose data analytics if you:

• Like answering specific, well-defined questions with existing data
• Prefer reporting, visualization and interpretation over building models from scratch
• Want to apply tools such as SQL, Python and Excel to day-to-day business problems

Choose business analytics if you:

• Care more about the business decision than the technical method behind it
• Enjoy presenting findings and recommendations to leadership and stakeholders
• Want to specialize in a business function such as finance, operations or supply chain

Choose artificial intelligence if you:

• Want to build systems that act on data, not just analyze it
• Are interested in areas such as natural language processing, neural networks or robotics
• Care about designing intelligent systems that are ethical and socially responsible

Choose machine learning if you:

• Want to understand the mathematics behind how models learn
• Enjoy algorithm design, optimization and experimentation
• Are drawn to specialties such as computer vision, deep learning or reinforcement learning

Once you’ve narrowed your options, review the websites of the programs you’re considering, including their curriculum and admission requirements pages. Then reach out to an enrollment advisor, who can answer your questions about online and on-campus options, tuition and financial aid, the time commitment and whether your background aligns with admission requirements. Advisors can often connect you with a faculty member for more insight into what the program is really like.

If data science is the right fit, explore the online Master of Science in Applied Data Science at the University of San Diego. The program was developed with input from industry advisors and emphasizes applied learning across the full data science pipeline. When you’re ready, talk to a USD advisor to learn more.

Frequently Asked Questions