Master’s in Data Science vs. Master’s in Artificial Intelligence: How to Choose the Right Tech Degree
| 10 Min Read
Comparing data science vs. artificial intelligence can help shape your career in the tech-driven future. Both the Master of Science (M.S.) in Data Science and Master’s in Artificial Intelligence online programs at Concordia University St. Paul (CSP Global) are designed for working professionals ready to deepen their expertise in computer science, datasets and programming. But each program takes a different approach.
This guide will help you understand the difference between a master’s degree in data science and one in artificial intelligence. You’ll learn how each program compares in terms of curriculum, skills gained and potential career outcomes. And unlike many universities, CSP Global gives students a unique advantage: the ability to pursue either of these degrees fully online with the flexibility to balance school and work.
| Master’s in Data Science vs. Master’s in Artificial Intelligence | ||
| Category | Master’s in Data Science | Master’s in Artificial Intelligence |
| Primary focus | Extracting insights from large datasets using statistics and analytics | Designing intelligent systems that simulate human reasoning |
| Core skills | Data analysis, statistical modeling, data visualization, SQL | Machine learning, deep learning, neural networks, TensorFlow, computer vision |
| Representative coursework | Big data architecture, data visualization, predictive analytics | Deep learning, computer vision, reinforcement learning |
| Hands-on experience | Cleaning and analyzing unstructured data; creating forecasting models | Building intelligent agents; training neural networks and AI applications |
| Ethical focus | Emphasizes responsible data use and interpretation | Covers AI ethics, societal impacts of automation and human-centered design |
| Typical career paths | Data scientist, data analyst, data engineer, BI analyst | AI engineer, machine learning engineer, software development specialist, research scientist |
| Industry applications | Business intelligence, health care analytics, finance, marketing | Robotics, automation, autonomous vehicles, virtual assistants |
| Program delivery | 100% online, flexible format | 100% online, flexible format |
| Median salary | $112,590 | $140,910 |
What Do You Learn in Data Science vs. AI?
Data science is the study of extracting insights from raw data using statistical methods, programming languages and advanced analytics. It focuses on collecting, cleaning and interpreting datasets to guide real-world decisions across industries. In contrast, artificial intelligence is the design of intelligent systems that simulate human intelligence through algorithms and neural networks.
While both fields involve machine learning and data modeling, their goals differ. Data science aims to find patterns and make predictions using structured and unstructured data. Artificial intelligence is about building models that can independently learn, reason and respond, like self-improving software.
You can see the overlap clearly once you look at real projects. A data scientist might pull customer comments into a workflow and sort through the patterns that surface. An AI specialist could apply the same techniques to teach a chatbot to respond in a natural way. They both depend on algorithms, but the AI side usually pushes harder on building systems that can act on their own and adjust as they go.
These shared foundations, from machine learning to data collection, show how the fields intersect, yet diverge in application. Understanding this distinction helps students choose the right academic and career path based on whether they prefer extracting insights or designing intelligent systems.
Data Science Curriculum
CSP Global’s online M.S. in Data Science prepares students to manage, analyze and interpret large datasets using statistical methods and industry-standard tools. The curriculum emphasizes mastering data manipulation, visualization and forecasting — essential skills for making data-driven decisions in business, healthcare and finance.
Students build technical fluency in programming languages, along with data-focused libraries such as Pandas. These tools are used to clean, organize and explore unstructured data through exploratory data analysis (EDA). From there, students apply statistical analysis techniques to uncover patterns and predict outcomes. Curriculum will include topics such as:
- Big data architecture: Learn how to design and implement scalable systems for processing massive datasets.
- Data visualization: Explore how to turn complex data into clear, actionable visuals for stakeholders.
- Predictive analytics: Build forecasting models to support business intelligence and operational strategy.
Graduates from CSP Global’s program are equipped to solve real-world problems using data — whether it’s optimizing patient outcomes in healthcare or driving customer insights into financial services. The skills gained prepare students for versatile, high-impact roles across the growing field of data science.
Artificial Intelligence Curriculum
The online M.S. in Artificial Intelligence at CSP Global focuses on developing intelligent systems that can learn, adapt and handle tasks that once needed human judgment. Students get hands-on experience designing and implementing machine learning algorithms, working with scalable software frameworks (Hadoop, Spark and NoSQL databases) that support real-world AI applications.
The curriculum combines theoretical foundations with hands-on projects, giving students experience with cutting-edge technologies like TensorFlow. Through labs and simulations, students develop machine learning models that could drive autonomous vehicles, automate robotic systems or power intelligent virtual assistants. Representative courses include:
- Machine Learning and AI: In-depth study of the principles, algorithms and applications of machine learning and AI technologies.
- Artificial Intelligence in Computer Vision: Delves into state-of-the-art AI techniques, deep learning architectures and advanced methodologies used in solving complex vision tasks, such as facial recognition or object tracking.
- Reinforcement Learning: Covers algorithms like Q-learning, policy gradients and deep reinforcement learning, advancing with applications in robotics, gaming and autonomous systems..
CSP Global’s program also spends time considering what it means to design AI responsibly. Students look at how intelligent systems affect people and communities, and how to evaluate those impacts before putting a model into use. With a mix of technical depth and human-centered design principles, graduates leave ready to guide AI innovation across a range of industries.
Career Paths: Data Scientist vs. AI Specialist
Graduates of CSP Global’s online M.S. in Data Science or M.S. in Artificial Intelligence step into a wide range of fast-growing roles, and the paths often blend a bit depending on what each student focuses on. For those coming out of the data science program, some of the more common directions include:
- Data scientist: Analyze complex datasets to identify trends, build models and guide strategic decisions.
- Data analyst: Translate raw data into actionable insights for business teams and leadership.
- Data engineer: Design the infrastructure for data storage, processing and retrieval.
- Business intelligence analyst: Develop reports and dashboards to support forecasting and performance tracking.
Graduates of the AI program often pursue roles in a slightly different tack. They include:
- AI engineer: Build and deploy intelligent systems for automation, personalization or decision support.
- Machine learning engineer: Create and refine algorithms that improve with data over time.
- Software development specialist: Integrate AI capabilities into applications or devices.
- Research scientist: Conduct cutting-edge work on AI theory, ethics or applications.
Some roles, like machine learning engineer, are open to both degree types, depending on the candidate’s background in programming, data modeling and intelligent systems. Either way, the job outlook for both tracks is strong.
According to the U.S. Bureau of Labor Statistics, computer and information research scientist roles — which include many AI functions — are projected to grow 20% from 2024 to 2034, while data scientist roles are projected to grow 34%. Salaries are also competitive: Computer and information research scientists earn a median salary of $140,910, while data scientists earn a median salary of $112,590.
Industries such as social media, healthcare and finance increasingly rely on data- and AI-driven roles for fraud detection, forecasting and systems optimization. These skills are critical for digital transformation and organizational agility across sectors.
Which Is Better: Data Science or Artificial Intelligence?
Choosing between data science and artificial intelligence depends on your goals and mindset. If you enjoy data-driven decision-making and interpreting trends, data science may be a better fit. If you find yourself more interested in designing AI systems and creating automation, the artificial intelligence route may feel more in line with your strengths. Both degrees involve working with large datasets and using machine learning models, but the focus in each program ends up taking you in a different direction.
Data science tends to center on predictive modeling, statistical analysis and making sense of the patterns hidden in large datasets. Artificial intelligence leans more toward building intelligent systems and creating adaptive algorithms that can adjust and improve their performance in real time.
Despite their differences, the two fields share a solid base of skills. Both rely heavily on machine learning, programming and strong problem-solving techniques. One advantage here is that CSP Global offers both programs online, so you can explore either path without switching schools. And whether you lean more toward analysis or automation, CSP Global’s flexible curriculum and experienced faculty can help you move forward in your tech career.
How to Choose the Right Degree for You
If you’re weighing a master’s in data science against one in artificial intelligence, it can help to stop and look at what you naturally enjoy doing. Maybe you like working through data analysis projects and using programming languages to figure out what the numbers truly indicate. Or you might be more interested in building systems that echo human reasoning and support AI-powered solutions. Paying attention to what feels engaging usually makes the choice clearer.
Those with a passion for statistics, problem-solving and trend interpretation often thrive in data science roles. On the other hand, learners fascinated by algorithms, automation and frameworks like TensorFlow may be better suited to artificial intelligence. Your long-term career goal can also help guide the choice — research-focused careers may align more with AI, while application and strategy roles often stem from a data science background.
Despite their differences, these disciplines intersect through machine learning, predictive analytics and collaborative use cases. For example, AI supports robotics in manufacturing, while data science powers business intelligence platforms in finance and healthcare. Both require advanced programming, critical thinking and the ability to solve complex problems across industries.
CSP Global makes it easier to decide by offering both degrees fully online. With flexible formats and experienced faculty, students can explore either path — or even both — without transferring schools. Explore CSP Global’s online Master of Science in Data Science and online Master’s in Artificial Intelligence and discover which degree aligns with your goals and strengths.
FAQ
Still unsure? We address the top questions about AI vs. data science to help you make an informed decision about your future in tech.
Is data science harder than AI?
It depends on your background and interests. AI may require more advanced mathematical skills and deeper programming in areas like neural networks and algorithm design. Data science leans heavily on data analysis, statistics and visual interpretation. Both require technical focus, but learners often find one more intuitive than the other, depending on how they prefer to work with information.
Which has more demand: AI or data science?
Both fields are experiencing strong job demand. According to the World Economic Forum, AI and data science are among the fastest-growing job categories worldwide. AI careers are expanding quickly in automation and robotics, while data science jobs remain critical in fields like healthcare, finance and marketing. Demand continues to rise across industries due to ongoing tech growth and digital transformation.
Can data science be replaced by AI?
No, but AI integration can enhance data science workflows. AI tools can automate parts of the data collection, model training, and visualization process. However, data importance remains central to AI development. Data scientists play a key role in structuring and validating the datasets that fuel AI systems, making their expertise irreplaceable in the pipeline.
Is it a career in AI or data science that will lead to a higher salary?
Both offer high earning potential. Data shows that AI salary ranges are typically higher for roles in research, autonomous systems and enterprise-scale AI products. However, data science salaries remain strong, especially for professionals in finance, healthcare and tech. Career earnings also depend on factors like experience, specialization and industry pay trends.