Data Science

Carleton University

Mathematics and Statistics: blue ink subject illustration

4 years
Duration (years)
4 years
Carleton Data Science undergraduate admissions
Co-op option
Work experience
Co-op option
Carleton Data Science co-op option
80–85%
Published threshold
Published eligibility threshold: 80–85%. Fall 2027. Carleton’s Ontario high-school guide applies this cut-off to the overall and prerequisite averages.
Carleton 2027 Viewbook · Ontario minimum cut-offs, printed page 58

Deadlines

October 2026

Mon	Tue	Wed	Thu	Fri	Sat	Sun
			1	2	3	4
5	6	7	8	9	10	11
12	13	14		16	17	18
19	20	21	22	23	24	25
26	27	28	29	30	31

Next deadlineThursday, October 15, 2026

Application: January 2027 term, international transcripts
2026 entry
Verbatim: "Deadline for international students to apply for the January 2027 term (students whose transcripts originate outside of Canada or the US)."
Official deadline

Other deadline details

No single OUAC deadline, deadlines are set by each university and sometimes by programOUAC Undergraduate application, applicants who are not Ontario high school students (Group B)2026 entry

No single OUAC deadline, deadlines are set by each university and sometimes by program
No single OUAC deadline
University, program, transcript, portfolio, interview, and audition dates still apply

Full source wording

Verbatim: "There is no specific Undergraduate application deadline if you are a Group B applicant. Deadlines vary at each university and sometimes depend on the program. Visit the university websites or contact the universities directly for their deadlines. If you are interested in a program but the deadline has passed, you may contact the university's admission office for help. Be aware of university-specific deadlines for submitting transcripts and portfolios, if required, and for arranging interviews and auditions. You must ensure the OUAC has enough time to process your choices before the universities' program deadlines." OUAC defines Group B as anyone who does not meet the Group A criteria, e.g. not currently taking an Ontario high school course, a Canadian outside Ontario, an international applicant, a postsecondary transfer student, or a mature applicant.

SourceChecked Aug 14, 2026

Requirements

Admission requirements

High-school prerequisites

MHF4UAdvanced Functions
MCV4UCalculus and Vectors

Complete an Ontario Secondary School Diploma or equivalent with at least six Grade 12 4U or 4M courses.

Application steps

Step 1Check the admission requirements
- Check the published entry route for your applicant category.
Carleton 2026–27 Data Science Ontario admissions calendarCarleton 2026–27 Data Science Ontario admissions calendar
Step 2Apply through OUAC
- Create your application and select this program.
OUACOUAC
Step 3Academic documents listed in Carleton360
- Upload the records requested in Carleton360. If Admissions requests an official physical document, have it sent directly to the office rather than treating an applicant upload as official.
Carleton transcripts and academic documentsCarleton transcripts and academic documents
Step 4English translations for other-language documents
- Records in a language other than English need a certified English translation or notarized translation copy, matching the original's course and grade information.
Carleton translation requirementsCarleton translation requirements

Applicant pathways

2026–27 calendar Ontario high school prerequisites

Ontario high school applicants

Complete an Ontario Secondary School Diploma or equivalent with at least six Grade 12 4U or 4M courses.
Include Advanced Functions and Calculus and Vectors.

Alberta, Nunavut and Northwest Territories

High-school applicants educated in Alberta, Nunavut and Northwest Territories

Complete high school with five courses numbered 30 or 31.
Complete Math 30-1 and Math 31.

British Columbia and Yukon

High-school applicants educated in British Columbia and Yukon

Complete high school with four Grade 12 academic courses.
Complete Pre-Calculus 12 and Calculus 12.

Manitoba

High-school applicants educated in Manitoba

Complete high school with five courses at the 40 level.
Complete Pre-Calculus Math 40S and Calculus 45S.

New Brunswick

High-school applicants educated in New Brunswick

Complete high school with five academic Grade 12 courses.
Complete Pre-Calculus B 120 and Calculus 120.

Newfoundland and Labrador

High-school applicants educated in Newfoundland and Labrador

Complete high school with 10 credits at the 3000 level.
Complete Math 3200 and Calculus 3208.

Nova Scotia

High-school applicants educated in Nova Scotia

Complete high school with five Grade 12 academic or advanced courses.
Complete Pre-Calculus 12 and Calculus 12.

Prince Edward Island

High-school applicants educated in Prince Edward Island

Complete high school with five academic courses at the 611 or 621 level.
Complete Math 621B and Math 611B.

Saskatchewan

High-school applicants educated in Saskatchewan

Complete high school with six courses numbered 30.
Complete Pre-Calculus 30 and Calculus 30.

Degree requirements

Credits and degree requirements

The Bachelor of Data Science Honours requires 20.0 credits.

References

Program facts

Published threshold80–85%

Carleton’s Ontario high-school guide applies this cut-off to the overall and prerequisite averages.

Co-opCo-op option

Check work experience options

Majors & specializations

Artificial Intelligence

BDS

Study artificial intelligence and computer science methods alongside data science.

Published requirements

Courses

Published requirements in the 2026–27 calendar. Choice and prerequisite rules remain attached to their requirement group.

Data Science B.D.S. Honours (20.0 credits) · A. Credits Included in the Major CGPA (13.5 credits) · 1. 1.5 credits in:3 listed

MATH 1007Elementary Calculus I0.5 credits
MATH 1104Linear Algebra for Engineering or Science0.5 credits
MATH 2007Elementary Calculus II0.5 credits

Data Science B.D.S. Honours (20.0 credits) · A. Credits Included in the Major CGPA (13.5 credits) · 2. 1.0 credit in:2 listed

DATA 3200Communication Skills for Data Scientists0.5 credits
PHIL 2106Information Ethics0.5 credits

Data Science B.D.S. Honours (20.0 credits) · A. Credits Included in the Major CGPA (13.5 credits) · 3. 5.5 credits in:11 listed

COMP 1405Introduction to Computer Science I0.5 credits
COMP 1406Introduction to Computer Science II0.5 credits
COMP 1805Discrete Structures I0.5 credits
COMP 2109Introduction to Security and Privacy0.5 credits
COMP 2401Introduction to Systems Programming0.5 credits
COMP 2402Abstract Data Types and Algorithms0.5 credits
COMP 2404Introduction to Software Engineering0.5 credits
COMP 2406Fundamentals of Web Applications0.5 credits
COMP 2804Discrete Structures II0.5 credits
COMP 3105Introduction to Machine Learning0.5 credits
COMP 4107Neural Networks0.5 credits

Data Science B.D.S. Honours (20.0 credits) · A. Credits Included in the Major CGPA (13.5 credits) · 4. 2.0 credit in:4 listed

DATA 1517Data Modelling I0.5 credits
DATA 1519Data Modelling II0.5 credits
DATA 2500Data Wrangling in R0.5 credits
DATA 3500Statistical Programming in R0.5 credits

Data Science B.D.S. Honours (20.0 credits) · A. Credits Included in the Major CGPA (13.5 credits) · 5. 2.0 credits in:4 listed

STAT 1500Introduction to Statistical Computing0.5 credits
STAT 2210Inferential Data Science Foundations I0.5 credits
STAT 3553Regression Modeling (Honours)0.5 credits
STAT 4601Data Mining I (Honours)0.5 credits

Data Science B.D.S. Honours (20.0 credits) · A. Credits Included in the Major CGPA (13.5 credits) · 6. 1.0 credit from:11 listed

COMP 4010Introduction to Reinforcement Learning0.5 credits
COMP 4102Computer Vision0.5 credits
COMP 4115Introduction to Natural Language Processing0.5 credits
COMP 4116Multiagent Systems0.5 credits
STAT 3504Analysis of Variance and Experimental Design0.5 credits
STAT 3507Sampling Methodology0.5 credits
STAT 4502Survey Sampling (Honours)0.5 credits
STAT 4503Applied Multivariate Analysis (Honours)0.5 credits
STAT 4504Statistical Design and Analysis of Experiments (Honours)0.5 credits
STAT 4604Statistical Computing (Honours)0.5 credits
STAT 4607Bayesian Statistical Analysis (Honours)0.5 credits

Mapped terms

Published first-year registration sequence. Confirm your entry-year requirements before registering.

Published first-year sequence

Fall

One eligible free elective.

COMP 1405Introduction to Computer Science I0.5 credits
Introduces programming and algorithm design for Computer Science students, including testing and problem decomposition.
COMP 1805Discrete Structures I0.5 credits
Introduces logic, sets, proofs, recurrence, automata and graphs through examples from computing.
DATA 1517Data Modelling I0.5 credits
MATH 1007Elementary Calculus I0.5 credits
Introduces limits and derivatives, their applications and the foundations of integration.

1 elective slot

Winter

One eligible free elective.

COMP 1406Introduction to Computer Science II0.5 credits
Develops object-oriented programming for BCS students, including data structures, inheritance, recursion and testing.
DATA 1519Data Modelling II0.5 credits
MATH 1104Linear Algebra for Engineering or Science0.5 credits
Studies linear systems, matrices, vector spaces, eigenvalues and transformations for engineering and science.
STAT 1500Introduction to Statistical Computing0.5 credits