CSCI 452: Data Mining
Spring 2019

Meeting times

Section 1:

  • MF 10-10:50PM Roddy Hall Rm 136

  • W 10-11:50PM Roddy Hall Rm 136/Mac Lab


An introduction to data mining, including data cleaning, the application of statistical and machine learning techniques to discover patterns in data, and the analysis of the quality and meaning of results. Machine learning topics may include algorithms for discovering association rules, classification, prediction, and clustering. Lab assignments provide practice applying specific techniques and analyzing results. An independent project provides students with the opportunity to guide a project from data selection and cleaning through to presentation of results. Pre-requisite: CSCI 362 and statistics (MATH 235, MATH 333 or MATH 335) or permission of instructor


Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar. Pearson, Second Edition, 2018. ISBN 978-0133128901

  • Midterm: 20%

  • Final: 20%

  • Labs and assignments: 30%

  • Project: 30%

Not attending labs may result in overall grade reduction (see attendance)

Grading will be on a 100 point scale, with 93%=A, 90%= A-, 87%=B+, 83%= B, etc. You must complete all exams, labs, and assignments in order to pass the course.

Additional Information

2018 — Stephanie Schwartz — Millersville University