Print and PDF Options

Data Science Collaborative Program
School of Computer Science
613-520-4333

http://carleton.ca/datascience

This section presents the requirements for programs in:

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 Specialization in Data Science (5.0 credits)

Requirements:
1.  0.5 credit in approved coursework0.5
2.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
3.  4.0 credits in:4.0
BIOL 5909 [4.0]
M.Sc. Thesis
Total Credits5.0

M.A.Sc. Biomedical Engineering
with Specialization in Data Science (5.0 credits)

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]
Data Science Seminar
3.  1.0 credit in BIOM (BMG) courses1.0
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 Director0.5
5.  2.5 credits in:2.5
BIOM 5909 [2.5]
M.A.Sc. Thesis
6.  0.0 credit in:0.0
BIOM 5800 [0.0]
Biomedical Engineering Seminar
Total Credits5.0

Master of Cognitive Science
with Specialization in Data Science (5.0 credits)

Requirements - Thesis Option (5.0 credits)
1.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
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 Credits5.0
Requirements - Research Project Option (5.0 credits)
1.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
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.  1.5 credits from:1.5
CGSC 5001 [0.5]
Cognition and Artificial Cognitive Systems
CGSC 5002 [0.5]
Experimental Research in Cognition
CGSC 5003 [0.5]
Cognition and Language
CGSC 5004 [0.5]
Cognition and Conceptual Issues
CGSC 5005 [0.5]
Cognition and Neuroscience
5.  1.0 credit in CGSC or other approved courses selected in consultation with the graduate supervisor.1.0
6.  1.0 credit in:1.0
CGSC 5908 [1.0]
Research Project (Project must be approved as fulfilling the data science requirement and be supervised by a faculty member working in a data science related field.)
7. Preparation of research for presentation at the Cogntive Science Spring Conference.
Total Credits5.0

M.A. Communication
with Specialization in Data Science (5.0 credits)

Requirements - Coursework Option (5.0 credits)
1. 0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
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 electives2.0
Total Credits5.0
Requirements - Research Essay Option (5.0 credits)
1. 0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
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. 1.0 credit in:1.0
COMS 5908 [1.0]
Research Essay
Research Essay on a Data Science topic approved by the Advisory Board representative from Communication in consultation with the graduate Committee of the Institute of Data Science.
6. 1.5 credits in electives.1.5
Total Credits5.0
Requirements - Thesis Option (5.0 credits)
1. 0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
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. 2.0 credits in:2.0
COMS 5909 [2.0]
M.A. Thesis
M.A. Thesis on a Data Science topic approved by the Advisory Board representative from Communication in consultation with the Graduate Committee of the Institute of Data Science.
6. 0.5 credit in electives0.5
Total Credits5.0

M.C.S. Computer Science
with Specialization in Data Science (5.0 credits)

Requirements - Thesis Option (5.0 credits)
1.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
2.  1.0 credit from:1.0
COMP 5100 [0.5]
Topics in Artificial Intelligence
COMP 5101 [0.5]
Distributed Databases and Transaction Processing Systems
COMP 5107 [0.5]
Statistical and Syntactic Pattern Recognition
COMP 5108 [0.5]
Algorithms in Bioinformatics
COMP 5111 [0.5]
Data Management for Business Intelligence
COMP 5112 [0.5]
Algorithms for Data Science
COMP 5204 [0.5]
Computational Aspects of Geographic Information Systems
COMP 5209 [0.5]
Visual Analytics
COMP 5305 [0.5]
Advanced Database Systems
COMP 5306 [0.5]
Data Integration
COMP 5307 [0.5]
Knowledge Representation
COMP 5308 [0.5]
Topics in Medical Computing
COMP 5401 [0.5]
Electronic Commerce Technologies
COMP 5703 [0.5]
Algorithm Analysis and Design
COMP 5704 [0.5]
Parallel Algorithms and Applications in Data Science
3.  1.0 credit in course work1.0
4.  2.5 credits in:2.5
COMP 5905 [2.5]
M.C.S. Thesis
Total Credits5.0

Notes:

  1. Course selections must include a minimum of 1.5 credits of OCICS courses in three different research areas, and must include at least (see OCICS course listing): 0.5 credit in software engineering, 0.5 credit in the theory of computing, and 0.5 credit in either computer applications or computer systems.
  2. M.C.S. Thesis must be in an area of Data Science and requires approval from the Department. Each candidate submitting a thesis will be required to undertake an oral defence of the thesis.
 

M.A. Economics
with Specialization in Data Science (4.0 credits)

Requirements - Coursework option (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]
Data Science Seminar
3.  0.5 credit in:0.5
ECON 5029 [0.5]
Methods of Economic Research
including a research paper on a data science related topic
4.  1.0 credit in ECON approved by the M.A. Supervisor of the Department of Economics, including at least 0.5 credit from ECON 5055, ECON 5361, ECON 5362, ECON 5700, ECON 5712, ECON 57131.0
5.  0.5 credit in Data Science elective (which may be an additional course from the preceding list) approved by the M.A. Supervisor of the Department of Economics0.5
Total Credits4.0
Requirements - Thesis option (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]
Data Science Seminar
3.  1.5 credit in:1.5
ECON 5909 [1.5]
M.A. Thesis
on a data science topic approved by the Data Science governance committee
4.  0.5 credit from:0.5
ECON 5055 [0.5]
Financial Econometrics
ECON 5361 [0.5]
Labour Economics I
ECON 5362 [0.5]
Labour Economics II
ECON 5700 [0.5]
Social and Economic Measurement
ECON 5712 [0.5]
Micro-Econometrics
ECON 5713 [0.5]
Time-Series Econometrics
Total Credits4.0

M.A.Sc. Electrical and Computer Engineering
with Specialization in Data Science (5.0 credits)

Requirements - by Thesis (5.0 credits)
1.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
2.  0.5 credit from data science elective courses:0.5
SYSC 5001 [0.5]
Simulation and Modeling
SYSC 5003 [0.5]
Discrete Stochastic Models
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 5300 [0.5]
Advanced Health Care 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 5404 [0.5]
Multimedia Compression, Scalability, and Adaptation
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 Systems
SYSC 5706 [0.5]
Analytical Performance Models of Computer Systems
3.  1.5 credits in courses1.5
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 Credits5.0
Requirements - by Project (5.0 credits)
1.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
2.  1.0 credit from data science elective courses:1.0
SYSC 5001 [0.5]
Simulation and Modeling
SYSC 5003 [0.5]
Discrete Stochastic Models
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 5300 [0.5]
Advanced Health Care 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 5404 [0.5]
Multimedia Compression, Scalability, and Adaptation
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 Systems
SYSC 5706 [0.5]
Analytical Performance Models of Computer Systems
3.  3.0 credits in courses3.0
4.  0.5 credit in:0.5
SYSC 5900 [0.5]
Systems Engineering Project
in the area of data science
Total Credits5.0
Requirements - by Coursework (5.0 credits)
1.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
2.  1.5 credits from data science elective courses:1.5
SYSC 5001 [0.5]
Simulation and Modeling
SYSC 5003 [0.5]
Discrete Stochastic Models
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 5300 [0.5]
Advanced Health Care 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 5404 [0.5]
Multimedia Compression, Scalability, and Adaptation
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 Systems
SYSC 5706 [0.5]
Analytical Performance Models of Computer Systems
3.  3.0 credits in courses3.0
Total Credits5.0

M. Sc. Geography
with Specialization in Data Science (5.0 credits)

Requirements
1.  0.5 credit in:0.5
DATA 5000 [0.5]
Data Science Seminar
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.  1.0 credit in Physical Geography selected from:1.0
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.  2.5 credits in:2.5
GEOG 5906 [2.5]
M.Sc. Thesis (Thesis must be in the area of Data Science, defended at an oral examination)
Total Credits5.0

M.Sc. Health Sciences
with Specialization in Data Science (5.5 credits)

Requirements (5.5 credits)
1.  1.0 credits in:1.0
HLTH 5901 [0.5]
Advanced Topics in Interdisciplinary Health Sciences
HLTH 5902 [0.5]
Seminars in Interdisciplinary Health Sciences for MSc
2.  0.5 credits in:0.5
DATA 5000 [0.5]
Data Science Seminar
3. Completion of:
HLTH 5905 [0.0]
Final Research Seminar Presentation for MSc
4.  4.0 credits in:4.0
HLTH 5909 [4.0]
MSc Thesis
5. Twice-yearly meetings with the thesis Graduate Advisory Committee, with students meeting a level of progress as determined by the Committee.
Total Credits5.5

Note: The final research seminar presentation must be completed within one month of the thesis defence.

Regulations

See the General Regulations section of this Calendar, as well as regulations pertaining to the specific collaborative programs offering the data science specialization.

Admission

Students who are enrolled in a master's program in one of the participating units may apply to the Data Science governance committee for admission to the Collaborative Program. Admission to the program is determined by the governance committee and will normally take place before the end of October the year of admittance in one of the participating master's programs.

Admission requirements to the Collaborative Master's with Specialization in Data Science are:

  • Registration in the master's program of one of the participating units
  • Approval of a student's program of study by the Data Science governance committee and the student's home department. Students in a thesis program will be expected to choose a thesis topic that is directly related to Data Science. Students in an approved course work program will be required to take some elective courses in designated or approved courses with significant Data Science content.