CISC251/3.0 Data Analytics

Calendar description:

Introduction to data analytics; data preparation; assessing performance; prediction methods such as decision trees, random forests, support vector machines, neural networks and rules; ensemble methods such as bagging and boosting; clustering techniques such as expectation-maximization, matrix decompositions, and biclustering; attribute selection.
Recommended: Prior exposure to problem solving in any discipline.
Learning Hours: 120 (36L; 24Lab;60P)
Exclusion: CISC/CMPE 333

Course Outline:

Preliminaries (2 weeks)

(4 weeks) Clustering (4 weeks) Applications (2 weeks)

Learning outcomes:

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

  1. Design inductive model building algorithms appropriate for datasets of moderate size and complexity
  2. Evaluate the modelling performance of such algorithms, and the implications for the real-world system that the data describes

Possible Textbooks: