StudyCanada HomeUniversities in CanadaCarleton UniversityData Science (Collaborative Specialization)
Specializations and variants
M.A.Sc. Biomedical Engineering with Collaborative Specialization in Data Science (5.0 credits)
M.Eng. Biomedical Engineering with Collaborative Specialization in Data Science (5.0 credits)
M.Sc. in Chemistry with Collaborative Specialization in Data Science (5.0 credits)
Master of Cognitive Science with Collaborative Specialization in Data Science (5.0 credits)
M.A. Communication with Collaborative Specialization in Data Science (5.0 credits)
M.C.S. Computer Science with Collaborative Specialization in Data Science (5.0 credits)
M.A.Sc. Digital Media with Collaborative Specialization in Data Science (5.0 credits)
M.Sc. Economics with Collaborative Specialization in Data Science (4.0 credits)
M.A.Sc. Electrical and Computer Engineering with Collaborative Specialization in Data Science (5.0 credits)
M.Eng. Electrical and Computer Engineering with Collaborative Specialization in Data Science (4.5 credits)
M.A. Geography with Collaborative Specialization in Data Science (5.0 credits)
M.Sc. Geography with Collaborative Specialization in Data Science (5.0 credits)
M.Sc. Health Sciences with Collaborative Specialization in Data Science (5.5 credits)
M.A. History with Collaborative Specialization in Data Science (4.5 credits)
M.A. International Affairs with Collaborative Specialization in Data Science (5.0 credits)
M.Sc. Physics Particle Physics with Collaborative Specialization in Data Science (5.0 credits)
Master of Public Policy and Administration with Collaborative Specialization in Data Science (advanced entry, 5.0 credits)
M.A. Sociology with Collaborative Specialization in Data Science (5.0 credits)
Other information gathered
Program Requirements Students enrolled in the Collaborative Program in Data Science must meet the requirements of their respective home units as well as those of the Collaborative Program. The requirements of the Collaborative Program do not, however, add to the number of credits students are required to accumulate by their home unit and the credit value of the degree remains the same. Consult the individual programs for detailed program requirements. M.Sc. Biology with Collaborative Specialization in Data Science (5.0 credits)
Other information gathered
Program Requirements Students are expected to complete the master's program within the maximum limits outlined in the Section 13.2 of the General Regulations section of this Calendar. M.A.Sc. Aerospace Engineering with Collaborative Specialization in Data Science (5.0 credits) M.A.Sc. Materials Engineering with Collaborative Specialization in Data Science (5.0 credits) M.A.Sc. Mechanical Engineering with Collaborative Specialization in Data Science (5.0 credits) Requirements: 1. 0.5 credit in: 0.5 DATA 5000 [0.5] Introduction to Data Science 2. 0.5 credit from data science elective courses: 0.5 MECH 5103 [0.5] 3D Machine Vision: From Robots to the Space Station MECH 5205 [0.5] Building Performance Simulation MECH 5504 [0.5] Guidance, Navigation and Control MECH 5506 [0.5] Neuro and Fuzzy Control MECH 5508 [0.5] System Modelling, Dynamics and Control MECH 5509 [0.5] Nonlinear Systems Analysis & Controls MAAJ 5026 [0.5] System Modelling, Dynamics and Control MAAJ 5027 [0.5] Nonlinear System Analysis and Controls MAAJ 5028 [0.5] 3D Machine Vision: From Robots to the Space Station MAAJ 5055 [0.5] High-Performance Parallel Scientific Computing MAAJ 5105 [0.5] Non-Linear Optimization MAAJ 5251 [0.5] Guidance, Navigation and Control MAAJ 5252 [0.5] Neuro and Fuzzy Control SYSC 5001 [0.5] Simulation and Modeling SYSC 5004 [0.5] Optimization for Engineering Applications SYSC 5202 [0.5] Applications in Biomedical Image Processing SYSC 5303 [0.5] Interactive Networked Systems and Telemedicine SYSC 5405 [0.5] Pattern Classification and Experiment Design COMP 5112 [0.5] Algorithms for Data Science COMP 5204 [0.5] Computational Aspects of Geographic Information Systems COMP 5306 [0.5] Data Integration COMP 5703 [0.5] Algorithm Analysis and Design COMP 5704 [0.5] Parallel Algorithms and Applications in Data Science 3. 1.5 credits in courses offered by the OCIMAE. 1.5 4. Participation in the Mechanical and Aerospace Engineering seminar series 5. 2.5 credits in: 2.5 MECH 5909 [2.5] M.A.Sc. Thesis (in the Specialization) Total credits 5.0 M.Sc. Physics Medical Physics with Collaborative Specialization in Data Science (5.0 credits)
Other information gathered
M.A. Psychology with Collaborative Specialization in Data Science (5.0 credits) Notes: Students must receive a minimum grade of A in each of the courses included in the Specialization. Courses for each research area are listed on the departmental website: carleton.ca/psychology . Requirements: 1. 1.0 credit in: 1.0 PSYC 5410 [0.5] Foundations of the General Linear Model PSYC 5411 [0.5] Extension of the General Linear Model 2. 0.5 credit in: 0.5 DATA 5000 [0.5] Introduction to Data Science 3. 0.5 credit in PSYC at the 5000 level, excluding the professional development courses listed in Item 4 and excluding the elective statistics courses listed below. 0.5 4. 0.5 credit from the following professional development courses: 0.5 PSYC 5000 [0.5] Introduction to Program Evaluation PSYC 5002 [0.5] Ethics in Psychology PSYC 5003 [0.5] Open Science and Methodological Improvements PSYC 5004 [0.5] Knowledge Mobilization PSYC 5802 [0.5] Special Topics: Professional Development PSYC 5903 [0.5] Practicum in Psychology 5. Completion of: 0.0 PSYC 5906 [0.0] Pro-Seminar in Psychology 6. 2.5 credits in: 2.5 PSYC 5909 [2.5] M.A. Thesis (in the area of Data Science, which must be defended at an oral examination) Total Credits 5.0 Master of Public Policy and Administration with Collaborative Specialization in Data Science (7.0 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
| Requirements: | ||
| 1. 0.5 credit in approved coursework | 0.5 | |
| 2. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 3. 4.0 credits in: | 4.0 | |
| BIOL 5909 [4.0] | M.Sc. Thesis (in the specialization, including successful oral defence) | |
| Total Credits | 5.0 |
Pathway 2
| Requirements: | ||
| 1. 0.5 credit in: | 0.5 | |
| BIOM 5010 [0.5] | Introduction to Biomedical Engineering | |
| 2. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 3. 1.0 credit in BIOM (BMG) courses | 1.0 | |
| 4. 0.5 credit in elective courses taken either at Carleton University or University of Ottawa with the approval of the OCIBME Director or Associate Director | 0.5 | |
| 5. 2.5 credits in: | 2.5 | |
| BIOM 5909 [2.5] | M.A.Sc. Thesis (in the specialization) | |
| 6. 0.0 credit in: | 0.0 | |
| BIOM 5800 [0.0] | Biomedical Engineering Seminar | |
| Total Credits | 5.0 |
Pathway 3
| Requirements - by coursework: | ||
| 1. 0.5 credit in: | 0.5 | |
| BIOM 5010 [0.5] | Introduction to Biomedical Engineering | |
| 2. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 3. 2.0 credits in BIOM (BMG) courses | 2.0 | |
| 4. 2.0 credits in elective courses at either Carleton University or University of Ottawa with the approval of the OCIBME Director or Associate Director | 2.0 | |
| 5. 0.0 credit in: | ||
| BIOM 5800 [0.0] | Biomedical Engineering Seminar | |
| Total Credits | 5.0 |
Pathway 4
| Requirements | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 0.5 credit in: | 0.5 | |
| CHEM 5810 [0.5] | Seminar | |
| 3. 0.5 credit in: | 0.5 | |
| CHEM 5804 [0.5] | Modern Scientific Communication | |
| 4. 0.5 credit in CHEM at the graduate level, which may include up to 0.5 credit in another discipline, with permission of the department. | 0.5 | |
| 5. 3.0 credits in: | 3.0 | |
| CHEM 5909 [3.0] | M.Sc. Thesis (in the specialization) | |
| Total Credits | 5.0 |
Pathway 5
| Requirements - Thesis pathway (5.0 credits) | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 0.5 credit in: | 0.5 | |
| CGSC 5100 [0.5] | Issues in Cognitive Science | |
| 3. 0.5 credit in: | 0.5 | |
| CGSC 5101 [0.5] | Experimental Methods and Statistics | |
| 4. 1.0 credit in CGSC or other approved courses, from two different cognitive disciplines, selected in consultation with the graduate supervisor. | 1.0 | |
| 5. 2.5 credits in: | 2.5 | |
| CGSC 5909 [2.5] | M. Cog. Thesis (The thesis must be approved as fulfilling the data science requirement and be supervised by a faculty member working in a data science related field.) | |
| 6. Preparation of research for presentation at the Carleton Cognitive Science Spring Conference. | ||
| Total Credits | 5.0 |
Pathway 6
| Requirements - Coursework pathway (5.0 credits) | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 1.0 credit in: | 1.0 | |
| COMS 5101 [1.0] | Foundations of Communication Studies | |
| 3. 0.5 credit in: | 0.5 | |
| COMS 5605 [0.5] | Approaches to Communication Research | |
| 4. 0.5 credit in: | 0.5 | |
| COMS 5225 [0.5] | Critical Data Studies | |
| 5. 0.5 credit from: | 0.5 | |
| COMS 5203 [0.5] | Communication, Technology, Society | |
| COMS 5221 [0.5] | Science and the Making of Knowledge | |
| COMS 5224 [0.5] | Internet, Infrastructure, Materialities | |
| 6. 2.0 credits in electives | 2.0 | |
| Total Credits | 5.0 |
Pathway 7
| Requirements - Thesis pathway (5.0 credits) | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 2.0 credits in course work. Course work must include a minimum of 1.5 credits of OCICS courses in at least three different research areas. See OCICS course listing by research areas. | 2.0 | |
| 3. 2.5 credits in: | 2.5 | |
| COMP 5905 [2.5] | M.C.S. Thesis (M.C.S. Thesis must be in an area of Data Science and requires approval from the Institute of Data Science. Each candidate submitting a thesis will be required to undertake an oral defence of the thesis.) | |
| Total Credits | 5.0 |
Pathway 8
| Requirements: | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 1.5 credit from core courses: | 1.5 | |
| ITEC 5002 [0.5] | Fundamentals of Information Technology Research | |
| ITEC 5200 [0.5] | Entertainment Technologies | |
| ITEC 5201 [0.5] | Computer Animation Technologies | |
| ITEC 5202 [0.5] | Visual Effects Technologies | |
| ITEC 5203 [0.5] | Game Design and Development Technologies | |
| ITEC 5204 [0.5] | Emerging Interaction Techniques | |
| ITEC 5205 [0.5] | Design and Development of Data-Intensive Applications | |
| ITEC 5206 [0.5] | Data Protection and Rights Management | |
| ITEC 5207 [0.5] | Data Interaction Techniques | |
| ITEC 5208 [0.5] | Virtual Reality and 3D User Interfaces | |
| ITEC 5209 [0.5] | Empirical Research Methods in HCI | |
| ITEC 5210 [0.5] | Applied Deep Learning | |
| ITEC 5920 [0.5] | Special Topics in Digital Media | |
| 4. 0.5 credit in electives, which may include up to 0.5 credit from a 4000-level course, or a 0.5 credit graduate course from another discipline, with permission from their graduate supervisor or the Associate Director of Graduate Studies in the School. | 0.5 | |
| 5. 2.5 credits in: | 2.5 | |
| ITEC 5909 [2.5] | Master's Thesis (in the specialization) | |
| Total Credits | 5.0 |
Pathway 9
| Requirements - Coursework pathway (4.0 credits) | ||
| 1. 1.5 credits in: | 1.5 | |
| ECON 5020 [0.5] | Microeconomic Theory | |
| ECON 5021 [0.5] | Macroeconomic Theory | |
| ECON 5027 [0.5] | Econometrics I | |
| 2. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 3. 0.5 credit in: | 0.5 | |
| ECON 5708 [0.5] | Economic Data Science - Analytics | |
| 4. 0.5 credit in: | 0.5 | |
| ECON 5029 [0.5] | Economics Research Paper | |
| on a data science related topic. | ||
| 5. 0.5 credit from: | 0.5 | |
| ECON 5055 [0.5] | Financial Econometrics | |
| ECON 5709 [0.5] | Economic Data Science - Applications | |
| ECON 5712 [0.5] | Micro-Econometrics | |
| ECON 5713 [0.5] | Time-Series Econometrics | |
| 6. 0.5 credit in: a 5000-level elective in ECON, COMP, DATA, MATH, or STAT with permission of the M.Sc. Supervisor, which may also include an additional course from the list above in #5. | 0.5 | |
| Total Credits | 4.0 |
Pathway 10
| Requirements - by Thesis (5.0 credits) | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 0.5 credit from data science elective courses: | 0.5 | |
| SYSC 5001 [0.5] | Simulation and Modeling | |
| SYSC 5004 [0.5] | Optimization for Engineering Applications | |
| SYSC 5101 [0.5] | Design of High Performance Software | |
| SYSC 5103 [0.5] | Software Agents | |
| SYSC 5104 [0.5] | Methodologies For Discrete-Event Modeling And Simulation | |
| SYSC 5201 [0.5] | Computer Communication | |
| SYSC 5207 [0.5] | Distributed Systems Engineering | |
| SYSC 5303 [0.5] | Interactive Networked Systems and Telemedicine | |
| SYSC 5306 [0.5] | Mobile Computing Systems | |
| SYSC 5401 [0.5] | Adaptive and Learning Systems | |
| SYSC 5405 [0.5] | Pattern Classification and Experiment Design | |
| SYSC 5407 [0.5] | Planning and Design of Computer Networks | |
| SYSC 5500 [0.5] | Designing Secure Networking and Computer Systems | |
| SYSC 5703 [0.5] | Integrated Database and Cloud Systems | |
| 3. 1.5 credits in courses | 1.5 | |
| 4. 2.5 credits in: | 2.5 | |
| SYSC 5909 [2.5] | M.A.Sc. Thesis | |
| in the area of data science (each candidate submitting a thesis will be required to undertake an oral defence of the thesis) | ||
| Total Credits | 5.0 |
Pathway 11
| Requirements - by Project (4.5 credits) | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 1.0 credit from data science elective courses: | 1.0 | |
| SYSC 5001 [0.5] | Simulation and Modeling | |
| SYSC 5004 [0.5] | Optimization for Engineering Applications | |
| SYSC 5101 [0.5] | Design of High Performance Software | |
| SYSC 5103 [0.5] | Software Agents | |
| SYSC 5104 [0.5] | Methodologies For Discrete-Event Modeling And Simulation | |
| SYSC 5201 [0.5] | Computer Communication | |
| SYSC 5207 [0.5] | Distributed Systems Engineering | |
| SYSC 5303 [0.5] | Interactive Networked Systems and Telemedicine | |
| SYSC 5306 [0.5] | Mobile Computing Systems | |
| SYSC 5401 [0.5] | Adaptive and Learning Systems | |
| SYSC 5405 [0.5] | Pattern Classification and Experiment Design | |
| SYSC 5407 [0.5] | Planning and Design of Computer Networks | |
| SYSC 5500 [0.5] | Designing Secure Networking and Computer Systems | |
| SYSC 5703 [0.5] | Integrated Database and Cloud Systems | |
| 3. 2.5 credits in courses, which may include up to an additional 0.5 credit in project | 2.5 | |
| 4. 0.5 credit in: | 0.5 | |
| SYSC 5900 [0.5] | Systems Engineering Project | |
| in the area of data science | ||
| Total Credits | 4.5 |
Pathway 12
| Requirements: | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 0.5 credit in: | 0.5 | |
| GEOG 5000 [0.5] | Approaches to Geographical Inquiry | |
| 3. 2.5 credits in: | 2.5 | |
| GEOG 5909 [2.5] | M.A. Thesis (in the specialization and including oral examination of the thesis) | |
| 4. 0.5 credit in: | 0.5 | |
| GEOG 5905 [0.5] | Masters Research Workshop | |
| 5. 1.0 credit in approved graduate-level electives | 1.0 | |
| 6. In addition to the formal requirements, M.A. students are required to attend the Departmental Seminar series, and the Graduate Field Camp. | ||
| Total Credits | 5.0 |
Pathway 13
| Requirements: | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 0.5 credit in: | 0.5 | |
| GEOG 5001 [0.5] | Modeling Environmental Systems | |
| 3. 0.5 credit in: | 0.5 | |
| GEOG 5905 [0.5] | Masters Research Workshop | |
| 4. 0.5 credit in Physical Geography selected from: | 0.5 | |
| GEOG 5002 [0.5] | Quantitative Analysis for Geographical Research | |
| GEOG 5103 [0.5] | Hydrologic Principles and Methods | |
| GEOG 5104 [0.5] | Advanced Biogeography | |
| GEOG 5107 [0.5] | Field Study and Methodological Research | |
| GEOG 5303 [0.5] | Geocryology | |
| GEOG 5307 [0.5] | Soil Resources | |
| GEOG 5803 [0.5] | Seminar in Geomatics | |
| GEOG 5804 [0.5] | Geographic Information Systems | |
| GEOG 5900 [0.5] | Graduate Tutorial | |
| up to 0.5 credit in GEOG or GEOM at the 4000 level, with departmental approval | ||
| 5. 3.0 credits in: | 3.0 | |
| GEOG 5906 [3.0] | M.Sc. Thesis (in the specialization and including oral examination of the thesis) | |
| 6. In addition to the formal requirements, M.Sc. students are required to attend the DGES Departmental Seminar series, and the Graduate Field Camp. | ||
| Total Credits | 5.0 |
Pathway 14
| Requirements (5.5 credits): | ||
| 1. 0.5 credit in: | 0.5 | |
| HLTH 5903 [0.5] | Current Topics in Interdisciplinary Health Sciences | |
| 2. 0.5 credit from: | 0.5 | |
| HLTH 5902 [0.5] | Seminars in Interdisciplinary Health Sciences for MSc | |
| or elective, approved by Thesis Supervisor and Graduate Advisor | ||
| 3. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 4. 0.0 credit in: | 0.0 | |
| HLTH 5906 [0.0] | Research Seminar Presentation for MSc | |
| HLTH 5905 [0.0] | Final Research Seminar Presentation for MSc | |
| 5. 4.0 credits in: | 4.0 | |
| HLTH 5909 [4.0] | MSc Thesis (in the specialization) | |
| 6. Twice-yearly meetings with the thesis Graduate Advisory Committee, with students meeting a level of progress as determined by the Committee. | ||
| Total Credits | 5.5 |
Pathway 15
| Requirements: | ||
| 1. 0.5 credit in: | 0.5 | |
| HIST 5003 [0.5] | Historical Theory and Method | |
| 2. 1.5 credits in HIST at the graduate level of which only 0.5 credit may be taken in a designated public history course; with departmental permission, up to 0.5 credit of courses with historical content may be taken from another unit at Carleton University, at the University of Ottawa, or at another accredited institution. | 1.5 | |
| 3. 0.5 credit in: | 0.5 | |
| HIST 5706 [0.5] | Digital History | |
| 4. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 5. 0.5 credit in: | 0.5 | |
| HIST 5900 [0.5] | Directed Research | |
| 6. 1.0 credit in: | 1.0 | |
| HIST 5908 [1.0] | M.A. Research Essay (in the specialization) | |
| Total Credits | 4.5 |
Pathway 16
| Requirements - thesis pathway: | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 1.0 credit in: | 1.0 | |
| INAF 5016 [0.5] | Statistical Analysis for International Affairs | |
| INAF 5017 [0.25] | International Policymaking in Canada: Structure and Process | |
| INAF 5018 [0.25] | Law and International Affairs | |
| 3. 0.5 credit in Economics, successfully completed by the end of the second term from: (see Note 1, below) | 0.5 | |
| INAF 5009 [0.5] | Economics of Development | |
| INAF 5205 [0.5] | Economics of Conflict | |
| INAF 5214 [0.5] | Economics for National Security, Intelligence and Defence | |
| INAF 5308 [0.5] | International Trade and Finance: Theory and Policy | |
| INAF 5703 [0.5] | International Public Economics | |
| 4. 2.0 credits in: | 2.0 | |
| INAF 5909 [2.0] | M.A. Thesis (in the specialization) | |
| 5. 1.0 credit in Field or Elective courses (See Notes 2 and 3, below) | 1.0 | |
| 6. Successful completion of second language proficiency examination (See Note 4, below) | ||
| Total Credits | 5.0 |
Pathway 17
| Requirements: | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 0.5 credit in: | 0.5 | |
| PHYS 5002 [0.5] | Statistical Data Analysis Techniques for Physics (or equivalent course in computing physics) | |
| 3. 0.5 credit in: | 0.5 | |
| PHYS 5203 [0.5] | Medical Radiation Physics | |
| 4. 0.5 credits from: | 0.5 | |
| PHYS 5204 [0.5] | Physics of Medical Imaging (for imaging) | |
| PHYS 5206 [0.5] | Medical Radiotherapy Physics (for therapy) | |
| PHYS 5207 [0.5] | Radiobiology (for biophysics) | |
| 5. 0.5 credit in PHYS or PHYJ. With approval of the graduate supervisor, an appropriate graduate-level course outside the department of physics can be used. | 0.5 | |
| 6. 2.5 credits in | 2.5 | |
| PHYS 5909 [2.5] | M.Sc. Thesis (on a data science topic approved by the Data Science governance committee and defended at an oral examination) | |
| 7. Participation in the seminar series of the Ottawa-Carleton Institute for Physics | ||
| Total Credits | 5.0 |
Pathway 18
| Requirements: | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 0.5 credit in: | 0.5 | |
| PHYS 5002 [0.5] | Statistical Data Analysis Techniques for Physics (or equivalent course in computing physics) | |
| 3. 1.5 credit in: | 1.5 | |
| PHYS 5602 [0.5] | Physics of Elementary Particles | |
| PHYS 5701 [0.5] | Intermediate Quantum Mechanics with Applications | |
| PHYS 5702 [0.5] | Relativistic Quantum Mechanics | |
| 4. 2.5 credits in: | 2.5 | |
| PHYS 5909 [2.5] | M.Sc. Thesis (on a data science topic approved by the Data Science governance committee and defended at an oral examination) | |
| 5. Participation in the seminar series of the Ottawa-Carleton Institute of Physics | ||
| Total Credits | 5.0 |
Pathway 19
| Requirements - coursework pathway (standard admission, 7.0 credits) | ||
| 1. 4.0 credits in core courses: | 4.0 | |
| PADM 5120 [0.5] | Modern Challenges to Governance | |
| PADM 5121 [0.5] | Policy Analysis: The Practical Art of Change | |
| PADM 5122 [0.5] | Public Management: Principles and Approaches | |
| PADM 5123 [0.5] | Public Management in Practice | |
| PADM 5125 [0.5] | Qualitative Methods for Public Policy | |
| PADM 5127 [0.5] | Microeconomics for Policy Analysis | |
| PADM 5128 [0.5] | Macroeconomics for Policy Analysis | |
| PADM 5129 [0.5] | Capstone Course | |
| 2. 1.5 credits in data science core courses: | 1.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| PADM 5126 [0.5] | Quantitative Methods for Public Policy | |
| PADM 5218 [0.5] | Analysis of Socio-economic Data | |
| 3. 0.5 credit from data science electives: | 0.5 | |
| COMP 5111 [0.5] | Data Management for Business Intelligence | |
| COMP 5209 [0.5] | Visual Analytics | |
| COMP 5305 [0.5] | Advanced Database Systems | |
| COMP 5306 [0.5] | Data Integration | |
| PADM 5372 [0.5] | Policy Seminar (Data Science Specialization) | |
| PADM 5391 [0.5] | Directed Studies (Data Science Specialization) | |
| 4. 1.0 credit in approved electives (see School website for details) | 1.0 | |
| Total Credits | 7.0 |
Pathway 20
| Requirements - coursework pathway (advanced entry, 5.0 credits) | ||
| 1. 2.5 credits from core courses: | 2.5 | |
| PADM 5120 [0.5] | Modern Challenges to Governance | |
| PADM 5121 [0.5] | Policy Analysis: The Practical Art of Change | |
| PADM 5122 [0.5] | Public Management: Principles and Approaches | |
| PADM 5123 [0.5] | Public Management in Practice | |
| PADM 5125 [0.5] | Qualitative Methods for Public Policy | |
| PADM 5127 [0.5] | Microeconomics for Policy Analysis | |
| PADM 5128 [0.5] | Macroeconomics for Policy Analysis | |
| PADM 5129 [0.5] | Capstone Course | |
| 2. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 3. 0.5 credit from: | 0.5 | |
| PADM 5126 [0.5] | Quantitative Methods for Public Policy | |
| PADM 5218 [0.5] | Analysis of Socio-economic Data | |
| 4. 0.5 credit from data science electives: | 0.5 | |
| COMP 5111 [0.5] | Data Management for Business Intelligence | |
| COMP 5209 [0.5] | Visual Analytics | |
| COMP 5305 [0.5] | Advanced Database Systems | |
| COMP 5306 [0.5] | Data Integration | |
| PADM 5372 [0.5] | Policy Seminar (Data Science Specialization) | |
| PADM 5391 [0.5] | Directed Studies (Data Science Specialization) | |
| 5. 1.0 credit in approved electives (see School website for details) | 1.0 | |
| Total Credits | 5.0 |
Pathway 21
| Requirements - Thesis pathway (5.0 credits): | ||
| 1. 0.5 credit in: | 0.5 | |
| DATA 5000 [0.5] | Introduction to Data Science | |
| 2. 1.0 credit in: | 1.0 | |
| SOCI 5005 [0.5] | Recurring Debates in Social Thought | |
| SOCI 5809 [0.5] | The Logic of the Research Process | |
| 3. 1.0 credit in: | 1.0 | |
| SOCI 5102 [0.5] | Multiple Regression Analysis | |
| SOCI 5104 [0.5] | Advanced Multivariate Analysis | |
| 4. 0.5 credit in SOCI at the graduate level (not including those listed above). May be selected from courses at the 4000-level, with department permission. | 0.5 | |
| 5. 2.0 credits in: | 2.0 | |
| SOCI 5909 [2.0] | M.A. Thesis (in the specialization) | |
| 6.0 An oral examination on the candidate's thesis and program | ||
| Total Credits | 5.0 |
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/grad/gradprograms/datascience/