Course Descriptions

Data, Faculty of Arts and Sciences

DATA: Data Science

DATA 301 (3) Introduction to Data Analytics
Techniques for computation, analysis, and visualization of data using software. Manipulation of small and large data sets. Automation using scripting. Real-world applications from life sciences, physical sciences, economics, engineering, or psychology. No prior computing background is required. Credit will be granted for only one of COSC 301, DATA 301 or DATA 501. [3-2-0]
Prerequisite: Third-year standing.
Equivalency: COSC 301.
DATA 311 (3) Machine Learning
Regression, classification, resampling, model selection and validation, fundamental properties of matrices, dimension reduction, tree-based methods, unsupervised learning. [3-2-0]
Prerequisite: both of (one of STAT 230 or 75% in either APSC 254, BIOL 202, or PSYO 373) and (one of COSC 111 or APSC 177).
DATA 405 (3) Modelling and Simulation
Numeric dynamic systems models and emphasis on discrete stochastic systems. State description of models, common model components, entities. Common simulation language. Simulation using algebraic languages. Simulation methodology: data collection, model design, output analysis, optimization, validation. Elements of queuing theory, relationship to simulation. Applications tocomputer systems models. Credit will be granted for only one of COSC 405, DATA 405, COSC 505, or DATA 505. [3-2-0]
Prerequisite: A score of 60% or higher in COSC 221 and a score of 60% or higher in COSC 222.
Equivalency: COSC 405.
DATA 407 (3) Sampling and Design
Planning and practice of data collection. Pros and cons of both observational and experimental data. Survey samples: random sampling; bias and variance; unequal probability sampling; systematic, multistage, and stratified sampling; ratio and regression estimators. Experimental design: simple one-way comparisons; designs with randomization restrictions including blocking, split-plots, nested and repeated measures designs. Credit will be granted for only one of DATA 407 or STAT 507. [3-1-0]
Prerequisite: One of STAT 230, PSYO 372, BIOL 202, ECON 327.
DATA 410 (3) Regression and Generalized Linear Models
Regression, linear models, generalized linear models, additive models, generalized additive models, mixed models. Theory and numerical performance. Credit will be granted for only one of DATA 410 or STAT 538. [3-2-0]
Prerequisite: DATA 311.
DATA 419 (3-9) d Topics in Data Science
Advanced or specialized topics in data science. Consult the unit for the specific topic to be offered in any given year. This course may be taken more than once for credit with different topics. [3-2-0]
Prerequisite: Fourth-year standing.
DATA 421 (3) Network Science
Graphs and complex networks in scientific research. Probabilistic and statistical models. Structures, patterns, and behaviors in networks. Algorithmic and statistical methods. (online/mobile) social networks and social media platforms. Social influence, information diffusion, and viral marketing. Sentiment analysis and opinion mining. Data privacy. Search engines and recommendation systems. Credit will be granted for only one of COSC 421, DATA 421 or DATA 521. [3-2-0]
Prerequisite: Third-year standing.
Equivalency: COSC 421.
DATA 448 (3/6) d Directed Studies in Data Science
Investigation of a specific topic as agreed upon by the student and the faculty supervisor. Completion of a project and an oral presentation are required.
Prerequisite: Third-year standing in the Data Science major or Honours, and permission of the unit head.
DATA 449 (6) Honours Thesis
Students will undertake a research project as agreed upon by the student, supervising faculty member, and unit head. A written thesis and a public presentation (poster or seminar) are required. Restricted to students in the B.Sc. Data Science Honours Program.
Prerequisite: Fourth-year standing and permission of the unit head.
DATA 500 (3) Communication and Consulting in Data Science
Effective consulting practices, ethical considerations, methodology selection, data preparation, effective software development. Credit will be granted for only one of DATA 500 or STAT 400 when the subject matter is of the same nature.
DATA 501 (3) Data Analytics
Techniques for computation, analysis, and visualization of data using software. Manipulation of small and large data sets. Automation using scripting. Real-world applications from life sciences, physical sciences, engineering, or psychology. Credit will be granted for only one of COSC 301, DATA 301 or DATA 501.
DATA 505 (3) Modelling and Simulation
Simulation methodology: data collection, model design, output analysis, optimization, validation. Credit will be granted for only one of COSC 405, DATA 405, COSC 505, or DATA 505.
DATA 521 (3) Network Science
Graphs and complex networks in scientific research. Probabilistic and statistical models. Structures, patterns, and behaviors in networks. Algorithmic and statistical methods. (online/mobile) social networks and social media platforms. Social influence, information diffusion, and viral marketing. Sentiment analysis and opinion mining. Data privacy. Search engines and recommendation systems. Credit will be granted for only one of COSC 421, DATA 421 or DATA 521.

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