STAT 2450

Introduction to Data Mining

Lec 01 selected

This course provided as introduction to data mining and R programming, suited for science students. Data mining methods include a vast set of tools developed in different areas for identifying the patterns in data. Students will learn programming methods for manipulating and exploring data through learning the basic ideas of some clustering, regression and classification methods. No prior programming knowledge is assumed.

Sections

Lecture

From the syllabus

From the Fall 2024 syllabus by Orla Murphy. Open the PDF

Grading

40%Assignments (6)
25%Quizzes (6)
30%Final examBelow 50% results in automatic course failure
5%ParticipationBased on in-class exercise submissions

Textbook

  • Introduction to Statistical Learning with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (free)

Course rules

Late work
Late assignments are not accepted unless an extension was approved beforehand.
Missed assessment
No make-up assessments are offered; short absences require advance notice and a Student Declaration within three days.
Student Declaration
Only one Student Declaration form submission per student is accepted in the course.
Independent work
Assignments must be your own work, and quizzes must be completed independently.
Grade inquiries
Grade modifications are not made more than two weeks after an assessment is returned.

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