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University of Toronto Faculty of Information Sessional Lecturer Fall Term 2025 (September - December) INF2167H - R for Data Science Course Description: Data science is a fast-growing field and new
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of types of data, descriptive statistics, probability distributions, hypothesis testing, correlation, linear regression and analysis of variance will be covered. The course will be using the R software
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
ups for special events and exams; p) cleans escalators and elevator interior and exterior; q) locks and unlocks doors as directed; and r) other related duties as required. Qualifications: Minimum
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
ups for special events and exams; p) cleans escalators and elevator interior and exterior; q) locks and unlocks doors as directed; and r) other related duties as required. Qualifications: Minimum
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involving multiple funding sources. Core responsibilities include revenue monitoring, reconciliation, reporting, and ensuring accuracy in financial records. The position also requires the preparation of A/R
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of textiles and fashion and the context surrounding the 3 R's, reduce, reuse and recycle. Estimated Course Enrolment: 12 students Estimated TA Support: None Class Schedule: Tuesdays 2:00pm - 4:00pm (In Person
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drafting related communications Advanced proficiency with M365, survey tools and data analysis software (e.g., NVivo, SPSS, R) Excellent communications skills (written and verbal), with ability to work
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analysis of experimental data with additional insights into data mining and evaluation of machine learning. The course will use the R statistical package. However, the main principles of data analysis
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13–15, W 15–11, R 12–14 (lecturers are responsible for two each week, schedule TBD). Weekly Teaching Team meeting tentatively M 16–17 (to be confirmed). Weekly Studio Debrief Meeting TBD. NOTE
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: Lectures M 13–14, T 9–10, F 11–12 (lecturers are expected to attend two of three each week) Tutorials (Studios) T 12–14, W 11–13, W 14–16, R 11–13 (lecturers are responsible for two each week, schedule TBD