Data Science (DATA) Courses
DATA 5000 [0.5 credit]
Introduction to Data Science
Machine learning; statistics; data science; data ethics; data cleaning; data preparation; data visualization; communication skills to explain advanced analytics. Students work on an interdisciplinary project in data science.
Introduction to Data Science
Machine learning; statistics; data science; data ethics; data cleaning; data preparation; data visualization; communication skills to explain advanced analytics. Students work on an interdisciplinary project in data science.
DATA 5001 [0.5 credit]
Fundamentals in Data Science and Analytics
Ethics in Data Science and Analytics, visualization and knowledge discovery in massive datasets; unsupervised learning: clustering algorithms; dimension reduction; supervised learning: pattern recognition, smoothing techniques, classification.
Fundamentals in Data Science and Analytics
Ethics in Data Science and Analytics, visualization and knowledge discovery in massive datasets; unsupervised learning: clustering algorithms; dimension reduction; supervised learning: pattern recognition, smoothing techniques, classification.
Precludes additional credit for STAT 5703.
DATA 5002 [0.5 credit]
Data Science, Ethics & Society
The ethical, social, political, and environmental implications of data science including the roles and responsibilities of data scientists in contemporary and emerging technological systems and the impact these systems may have at multiple scales, individual, group, institution, across sectors and nation-states.
Data Science, Ethics & Society
The ethical, social, political, and environmental implications of data science including the roles and responsibilities of data scientists in contemporary and emerging technological systems and the impact these systems may have at multiple scales, individual, group, institution, across sectors and nation-states.
Includes: Experiential Learning Activity
Also listed as COMS 5225.
Precludes additional credit for COMS 5225, ITEC 5206.
Also listed as COMS 5225.
Precludes additional credit for COMS 5225, ITEC 5206.
DATA 5900 [0.5 credit]
Special Topics in Data Science
Special topics, not covered by other graduate courses. Details will be available at the time of registration.
Special Topics in Data Science
Special topics, not covered by other graduate courses. Details will be available at the time of registration.
DATA 5901 [0.5 credit]
Directed Studies in DSAAI (Master's)
A course of independent study under the supervision of a faculty member in the Data Science, Analytics, and Artificial Intelligence program.
Directed Studies in DSAAI (Master's)
A course of independent study under the supervision of a faculty member in the Data Science, Analytics, and Artificial Intelligence program.
DATA 5908 [1.5 credit]
Project - MSc
Project - MSc
DATA 5909 [2.5 credits]
Thesis - MSc
Thesis - MSc
DATA 5913 [0.0 credit]
Co-operative Work Term
Co-operative Work Term
Includes: Experiential Learning Activity
Prerequisite(s): registration in the Co-operative Education Option in the Data Science, Analytics, and Artificial Intelligence program.
Prerequisite(s): registration in the Co-operative Education Option in the Data Science, Analytics, and Artificial Intelligence program.
DATA 5918 [1.5 credit]
Project - MIT
Project - MIT
DATA 5919 [2.5 credits]
Thesis - MIT
Thesis - MIT
DATA 5928 [1.0 credit]
Project - MEng
Project - MEng
DATA 5929 [2.5 credits]
Thesis - MASc
Thesis - MASc
DATA 5939 [2.5 credits]
Thesis - MCS
Thesis - MCS
DATA 6901 [0.5 credit]
Directed Studies in DSAAI (PhD)
A course of independent study under the supervision of a faculty member in the Data Science, Analytics, and Artificial Intelligence program.
Directed Studies in DSAAI (PhD)
A course of independent study under the supervision of a faculty member in the Data Science, Analytics, and Artificial Intelligence program.
DATA 6907 [0.0 credit]
Doctoral Comprehensive
Serves as starting point for the PhD literature survey and selection of a research topic. Student submits a document focusing on the problem statement, literature review, and gap analysis for the comprehensive exam. The student defends their submission in an oral exam.
Doctoral Comprehensive
Serves as starting point for the PhD literature survey and selection of a research topic. Student submits a document focusing on the problem statement, literature review, and gap analysis for the comprehensive exam. The student defends their submission in an oral exam.
DATA 6908 [0.0 credit]
Doctoral Proposal
Written proposal outlines a specific problem, connects it to current literature, and presents the hypothesis and goals. Summarizes preliminary results and details the research methodology and validation procedures(s). Proposal is defended in an oral exam, highlighting completed work and what is expected in final dissertation.
Doctoral Proposal
Written proposal outlines a specific problem, connects it to current literature, and presents the hypothesis and goals. Summarizes preliminary results and details the research methodology and validation procedures(s). Proposal is defended in an oral exam, highlighting completed work and what is expected in final dissertation.
DATA 6909 [0.0 credit]
Thesis - PhD
Thesis - PhD
Note: Not all courses listed are offered in a given year. For an up-to-date statement of course offerings for the current session and to determine the term of offering, consult the class schedule at central.carleton.ca.
Summer session: some of the courses listed in this Calendar are offered during the summer. Hours and scheduling for summer session courses will differ significantly from those reported in the fall/winter Calendar. To determine the scheduling and hours for summer session classes, consult the class schedule at central.carleton.ca