Academic Calendar 2022-2023

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CISC 372 Advanced Data Analytics

CISC 372  Advanced Data Analytics  Units: 3.00  

Inductive modelling of data, especially counting models; ensemble approaches to modelling; maximum likelihood and density-based approaches to clustering, visualization. Applications to non-numeric datasets such as natural language, social networks, Internet search, recommender systems. Introduction to deep learning. Ethics of data analytics.

Learning Hours: 120 (36 Lecture, 84 Private Study)  
Requirements: Prerequisite Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a 'Pass' (obtained in Winter 2020) in (CISC 271 and [3.0 units in STAT or STAT_Options]).  
Offering Faculty: Faculty of Arts and Science