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DSCI 401 - Data Mining (3).
Description: This course will provide a broad overview of data mining concepts, methodologies, implementation techniques, and programming. It will include fundamental topics such as decision trees and random forests, association rule mining, clustering, advanced regression techniques, Bayesian methods, and neural networks. Additional topics may include data cleaning and techniques for text and web mining. Prerequisite: DSCI 201, CSCI 208, and MATH 544. Notes: Offered in fall.
3.000 Credit hours
3.000 Lecture hours

Levels: Undergraduate
Schedule Types: Independent Study/Research, Lecture

Mathematics Department

Prerequisites:
Prereq for DSCI 401

General Requirements:
Course or Test: CSCI 208
Minimum Grade of D-
May not be taken concurrently.
and
Course or Test: DSCI 210
Minimum Grade of D-
May not be taken concurrently.
and
Course or Test: MATH 544
Minimum Grade of D-
May not be taken concurrently.

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Release: 8.7.2.4