IDSC4444: Descriptive and Predictive Analytics

2 Credits

Descriptive and Predictive Analytics exposes students to a number of data mining and machine learning methods, including: exploratory methods (such as association rules and cluster analysis), predictive methods (such as K-NN and decision trees), and text mining methods. The course combines theoretical lectures with lab lectures, where the methods are practically implemented using the software R. prereqs: IDSC 3001; non-MIS majors also need IDSC 4110

View on University Catalog

All Instructors

B+ Average (3.472)Most Common: A (39%)

This total also includes data from semesters with unknown instructors.

1488 students
SNWFDCBA
  • 4.01

    /5

    Recommend
  • 3.99

    /5

    Effort
  • 4.22

    /5

    Understanding
  • 3.96

    /5

    Interesting
  • 4.25

    /5

    Activities


      Contribute on our Github

      Gopher Grades is maintained by Social Coding with data from Summer 2017 to Spring 2024 provided by the Office of Institutional Data and Research

      Privacy Policy