Currently offered course(s)

  • DATA 5000 : Introduction to Data Science ( Winter 2017 )
    The course covers topics relevant to data science: working with data, exploratory data analysis, data mining, machine learning. The concepts are illustrated using the R language. Students also receive introduction to IBM Watson Analytics and IBM SPSS Modeler.

Past courses

  • COMP 5900 : Mining Software Repositories ( Fall 2016 )
    This course will introduce the methods and tools of mining software repositories and artifacts used by software developers and researchers. Students will learn to extract and abstract data from software artifacts and repositories, such as source code, version control systems and revisions, issue-tracking systems, and mailing lists and discussions. Students will also learn about various techniques of analyzing this data in order to identify meaningful relationships, patterns and trends, to recover behaviours and software development processes from evidence, or to empirically test hypotheses about software development.
  • COMP 3004 : Object-Oriented Software Engineering ( Fall 2016 )
    Theory and development software systems. Computer ethics. Possible topics include: software development processes, requirement specification, class and scenario modeling, state modeling, UML, design patterns, traceability.
  • DATA 5000 : Introduction to Data Science ( Winter 2016 )
    The course covers topics relevant to data science: working with data, exploratory data analysis, data mining, machine learning. The concepts are illustrated using the R language. Students also receive introduction to IBM Cognos Workspace and IBM SPSS Modeler.
  • COMP 5900 : Mining Software Repositories ( Fall 2015 )
    This course will introduce the methods and tools of mining software repositories and artifacts used by software developers and researchers. Students will learn to extract and abstract data from software artifacts and repositories, such as source code, version control systems and revisions, issue-tracking systems, and mailing lists and discussions. Students will also learn about various techniques of analyzing this data in order to identify meaningful relationships, patterns and trends, to recover behaviours and software development processes from evidence, or to empirically test hypotheses about software development.