Specializations and variants
Other information gathered
Program Requirements Course Categories The following categories of courses are used in defining the program requirements in the Bachelor of Data Science program. Free Electives All courses offered by the Faculty of Arts and Social Sciences, the Faculty of Public and Global Affairs, the Sprott School of Business and the Faculty of Science except for courses in the Prohibited Courses category. Free electives can include COMP, CSEC, DATA, MATH and STAT courses. Prohibited Courses The following courses cannot be used for credit in the B.D.S. Please note that any courses cross-listed with those on the list are also prohibited : BIOL 3604 [0.5] Statistics for Biologists BUSI 1401 [0.5] Foundations of Information Systems BUSI 2401 [0.5] Introduction to Data Analytics BUSI 2402 [0.5] Business Applications Development BUSI 3400 [0.5] Database Design CGSC 1005 [0.5] Computational Methods in Cognitive Science COMP 1001 [0.5] Introduction to Computational Thinking for Arts and Social Science Students COMS 3001 [0.5] Quantitative Research in Communication CRCJ 3001 [0.5] Quantitative Methods in Criminology ECON 1401 / MATH 1401 [0.5] Elementary Mathematics for Economics I ECON 1402 / MATH 1402 [0.5] Elementary Mathematics for Economics II ECON 2210 [0.5] Introductory Statistics for Economics ECON 3001 [0.5] Mathematical Methods of Economics ECON 4001 [0.5] Mathematical Analysis in Economics ECON 4002 [0.5] Statistical Analysis in Economics ECON 4004 [0.5] Operations Research: Linear Programming Models ECON 4706 [0.5] Econometrics I ECON 4707 [0.5] Econometrics II ECON 4713 [0.5] Time-Series Econometrics GEOG 2006 [0.5] Introduction to Quantitative Research ECOR 2606 [0.5] Numerical Methods GEOG 3003 [0.5] Quantitative Geography MATH 1009 [0.5] Mathematics for Business MATH 1119 [0.5] Linear Algebra: with Applications to Business NEUR 2001 [0.5] Introduction to Research Methods in Neuroscience NEUR 2002 [0.5] Introduction to Statistics in Neuroscience NEUR 3001 [0.5] Data Analysis in Neuroscience I NEUR 3002 [0.5] Data Analysis in Neuroscience II PSCI 2702 [0.5] A Statistical Toolkit for Political Scientists PSYC 2001 [0.5] Introduction to Research Methods in Psychology PSYC 2002 [0.5] Introduction to Statistics in Psychology PSYC 3000 [1.0] Design and Analysis in Psychological Research SOCI 2004 [0.5] Data Literacy for Social Sciences SOCI 3008 [0.5] Data Analysis for Social Sciences SOCI 4102 [0.5] Multiple Regression Analysis SOWK 3001 [0.5] Introduction to Research Methods in Social Work SYSC 2510 [0.5] Probability, Statistics and Random Processes for Engineers all 0000-level courses and all courses in BIT, IMD, IRM, MPAD, NET, OSS, PLT and ITEC except for the following: BIT 1000 , BIT 1001 , BIT 1100 , BIT 1101 , BIT 1200 , BIT 1201 , BIT 2000 , BIT 2004 (no longer offered), BIT 2005 (no longer offered), BIT 2007 (no longer offered), BIT 2100 (no longer offered), BIT 2300 (no longer offered), MPAD 2400 , MPAD 2501 (no longer offered), MPAD 3300 , MPAD 3501 , MPAD 4001 , MPAD 4501 , MPAD 4502 , MPAD 4503 , MPAD 4504 . Data Science B.D.S. Honours (20.0 credits)
Program pathways and conditions
These tables reproduce the source requirements for the indicated period. Official interpretation is the responsibility of the university.
Pathway 1
| A. Credits Included in the Major CGPA (13.5 credits) | ||
| 1. 1.5 credits in: | 1.5 | |
| MATH 1007 [0.5] | Elementary Calculus I | |
| MATH 1104 [0.5] | Linear Algebra for Engineering or Science | |
| MATH 2007 [0.5] | Elementary Calculus II | |
| 2. 1.0 credit in: | 1.0 | |
| DATA 3200 [0.5] | Communication Skills for Data Scientists | |
| PHIL 2106 [0.5] | Information Ethics | |
| 3. 5.5 credits in: | 5.5 | |
| COMP 1405 [0.5] | Introduction to Computer Science I | |
| COMP 1406 [0.5] | Introduction to Computer Science II | |
| COMP 1805 [0.5] | Discrete Structures I | |
| COMP 2109 [0.5] | Introduction to Security and Privacy | |
| COMP 2401 [0.5] | Introduction to Systems Programming | |
| COMP 2402 [0.5] | Abstract Data Types and Algorithms | |
| COMP 2404 [0.5] | Introduction to Software Engineering | |
| COMP 2406 [0.5] | Fundamentals of Web Applications | |
| COMP 2804 [0.5] | Discrete Structures II | |
| COMP 3105 [0.5] | Introduction to Machine Learning | |
| COMP 4107 [0.5] | Neural Networks | |
| 4. 2.0 credit in: | 2.0 | |
| DATA 1517 [0.5] | Data Modelling I | |
| DATA 1519 [0.5] | Data Modelling II | |
| DATA 2500 [0.5] | Data Wrangling in R | |
| DATA 3500 [0.5] | Statistical Programming in R | |
| 5. 2.0 credits in: | 2.0 | |
| STAT 1500 [0.5] | Introduction to Statistical Computing | |
| STAT 2210 [0.5] | Inferential Data Science Foundations I | |
| STAT 3553 [0.5] | Regression Modeling (Honours) | |
| STAT 4601 [0.5] | Data Mining I (Honours) | |
| 6. 1.0 credit from: | 1.0 | |
| COMP 4010 [0.5] | Introduction to Reinforcement Learning | |
| COMP 4102 [0.5] | Computer Vision | |
| COMP 4115 [0.5] | Introduction to Natural Language Processing | |
| COMP 4116 [0.5] | Multiagent Systems |
Courses
These courses were identified on the program source page. Their presence does not mean they are all required in every pathway.
Sources and references
Reference year : 2026-27
Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.
Source reference : https://calendar.carleton.ca/undergrad/undergradprograms/datascience/