CISC351/3.0 Advanced Data Analytics

Calendar description:

Design and implementation of complex analytics techniques; predictive algorithms at scale; deep learning; clustering at scale; advanced matrix decompositions, analytics in the Web, collaborative filtering; social network analysis; applications in specialized domains.
Prerequisites: (APSC 42 or APSC 143 or CISC 101 or CISC 110 or CISC 151 or CISC 121 or previous programming experience) and CISC251 and (STAT263 or STAT options)
Learning Hours: 120 (36L;24Lab;60P)

Course Outline:

Advanved Analytic Algorithms (6 weeks)

Application to non-tabular data (4 weeks) Scaling (1 week) Examples (1 week)

Learning outcomes:

Upon successful completion of this course, a student will be able to:

  1. Design inductive model building algorithms appropriate for datasets of substantial size and complexity with ill-defined requirements
  2. Plan ways to collect data, build models, and interpret results in network datasets
  3. Evaluate the modelling performance of such algorithms, and the implications for the real-world system that the data describes

Possible Textbooks: