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DATA305

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Computational Statistical Modelling

Faculty of Science SC - Faculty of Science

Subject

DATA - Data Science

Description

Random variables and their probability models. The Central Limit Theorem and parameter estimation. Statistical modelling of univariate and multivariate data with applications to discrete and continuous data. Data transformations. Introduction to simulation-based inference including randomization and permutation tests.

Prerequisite(s): Data Science 201 or 221; and 3 units from Data Science 211, Computer Science 217, 231 or 235; and 3 units from Statistics 205, 207, 327, Biology 315, Economics 395, Political Science 399, Psychology 300, Sociology 311, Engineering 319, Digital Engineering 319 or Linguistics 560.

Antirequisite(s): Credit for Data Science 305 and Statistics 323 will not be allowed.

Course Attributes

Fee Rate Group(Domestic) - A, Fee Rate Group(International) -A, GFC Hours (3-2)

Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.

Component

LAB

Component

LEC

Units

3

Repeat for Credit

No

Subject code

DATA

Understanding Course Information

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