No official source ranks artificial intelligence above data science, or the reverse. Statistics Canada's Classification of Instructional Programs (CIP Canada 2021 Version 1.0) treats them as two separate program classes: "Artificial intelligence" is class 11.0102 inside the computer and information sciences series, and "Data science, general" is class 30.7001 inside the multidisciplinary and interdisciplinary studies series. The definitions show what each field is meant to teach, which is a more useful comparison than a ranking.
Key takeaways
- CIP Canada 2021 and the American CIP 2020 are the Canadian and American versions of one jointly developed revision, so the two countries compare education data on the same structure.
- The artificial intelligence class (11.0102) sits under computer and information sciences, while data science (30.7001) sits under multidisciplinary studies.
- The AI definition centers on computer simulation of human learning and reasoning, and the data science definition centers on analysis of large-scale data with applied statistics and computer science.
- Machine learning is named as a course topic in the data science and data analytics classes but not in the AI class definition.
- A classification code describes program content; it says nothing about quality, pay, or prestige.
Where the two classes sit in the classification
CIP Canada 2021 has 50 two-digit series, 454 four-digit subseries, and 2,119 six-digit instructional program classes. Statistics Canada and the U.S. National Center for Education Statistics built the common structure together, and Canada added ten classes of its own for distinctly Canadian content. The two fields land in different branches:
| Code | Class title | Series |
|---|---|---|
| 11.0102 | Artificial intelligence | 11, Computer and information sciences and support services |
| 11.0701 | Computer science | 11, Computer and information sciences and support services |
| 27.0501 | Statistics, general | 27, Mathematics and statistics |
| 30.7001 | Data science, general | 30, Multidisciplinary/interdisciplinary studies |
| 30.7101 | Data analytics, general | 30, Multidisciplinary/interdisciplinary studies |
| 30.7102 | Business analytics | 30, Multidisciplinary/interdisciplinary studies |
The series description for 30 says its programs derive from two or more distinct programs and give a cross-cutting focus on a subject that no single discipline covers. That wording fits data science, which combines statistics and computing.
What each definition says
The artificial intelligence class covers programs that focus on the symbolic inference, representation, and simulation by computers and software of human learning and reasoning, and on computer modeling of human motor control and motions. Its listed course topics are computing theory, cybernetics, human factors, natural language processing, and applicable aspects of engineering and technology.
The data science class covers programs focused on analysis of large-scale data sources from the perspectives of applied statistics, computer science, data storage, data representation, data modeling, mathematics, and statistics. Its listed courses include computer algorithms, programming, data management, data mining, information retrieval, machine learning, mathematical modeling, quantitative analysis, statistics, and visual analytics.
The data analytics classes are written as applications of the other field: general data analytics and business analytics both "prepare individuals to apply data science" to generate insights or solve business problems.
What the wording suggests, and what it does not
Two readings follow from the text, and both come with limits. The AI definition reads like a description of the classic research field, with symbolic reasoning and human-like simulation, and it does not mention machine learning, which dominates current AI practice. The classification is a statistical standard revised only periodically, so its wording can lag behind how programs are marketed. The second reading is that data science is defined by its method (analysis of large data with statistics and computing), while AI is defined by its goal (simulating aspects of human thinking). That contrast matches the usual distinction between the fields but is an interpretation of two paragraphs, not a ruling by Statistics Canada.
The classification also does not settle which field is better. A statistics class (27.0501) lists regression analysis, Bayesian statistics, and sampling, and a computer science class (11.0701) covers computer theory and system design. A student, employer, or researcher choosing between programs still has to read the course list.
Checklist for reading a program against these definitions
- Find the CIP class a program is reported under, if the school publishes it, and read the full class definition.
- Compare the course list with the definition: machine learning and statistics courses point toward the data side; reasoning, language, and robotics courses point toward the AI side.
- Check which faculty houses the program, since the same course can appear in computer science, statistics, or business.
- Treat a classification as a label for statistics, not an assessment of quality.
This article covers an education-statistics standard and gives general information only. It does not rank programs or recommend careers.
FAQ
Is there an official ranking of AI versus data science?
No. The classification names and defines program classes and does not rank them. Any "superior field" claim depends on the criterion chosen, such as the work a person wants to do.
Why is data science not under computer science in CIP?
Statistics Canada lists it in series 30, for programs that combine two or more disciplines. Its definition names applied statistics, computer science, and mathematics together.
Does CIP Canada differ from the American CIP?
Statistics Canada says CIP Canada 2021 and CIP 2020 are the Canadian and American versions of the same revision, with ten Canada-only classes added for Canadian content.
Does the AI class include machine learning?
The class definition does not list it among its course topics. Programs in the class may still teach it, and the definition may simply reflect an older view of the field.
Related reading
- A comparison of the two fields
- How AI differs from computer science
- Institutions offering AI and data science programs
- Where AI tools enter data science work
More in AI Basics.
Last verified: October 2026
Sources
- Introduction to the Classification of Instructional Programs (CIP) Canada 2021 Version 1.0, Statistics Canada, 2022
- Classification of Instructional Programs (CIP) Canada 2021 Version 1.0, classification structure file, Statistics Canada, 2022