ECE Focus Area: Data Mining (Computer)

Storage-Grid computing centerBig Data AnalysisData Visualization

Overview

Data mining is the process of analyzing enormous sets of data and extracting meaning or useful information from it using computer algorithms and/or software tools. Data mining can be used to predict behavior and future trends allowing business to make knowledge-driven decisions.

Data mining tasks include data summarization, clustering, classification, prediction, and dependency analysis. Data mining relies heavily on algorithms and statistical methods to uncover patterns and create models of the data.

Data mining can benefit a broad spectrum of industries helping them to increase profits by reducing costs and/or raising revenue. Students pursuing this EFA could literally work in any organization that stores data and is interested in putting that data to good use.

Students interested in the Data Mining Elective Focus Area are encouraged to consider the track and EFA course selection suggestions listed below when completing their plan of study form.

EFA Requirements

Suggested Options

Track

Computer Track: accelerated curriculum, standard curriculum

Breadth Elective
(select one)

ECE:3400 Linear Systems II
ECE:3540 Communication Networks
ECE:3600 Control Systems

Depth Elective
(select one)

ECE:5330 Graph Theory and Combinatorial Optimization
ECE:5450 Pattern Recognition
ECE:5460 Digital Signal Processing
ECE:5480 Digital Image Processing

5000-Level ECE
Electives
(select two)

       All depth electives listed above and
ECE:5320 High Performance Computer Architecture
ECE:5800 Fundamentals of Software Engineering
ECE:5820 Software Engineering Languages and Tools
ECE:5830 Software Engineering Project

Technical Electives
(select three)

CS:2230 Computer Science II: Data Structures (Required)
       All breadth, depth and 5000-level ECE electives listed above, and
ECE:5995 Contemporary Topics in ECE
CS:6421 Knowledge Discovery
CS:4400 Database Systems
CS:4420 Artificial Intelligence
CS:4460 Introduction to Computational Linguistics
CS:4440 Web Mining
CS:4980Topics in Computer Science II
MATH:4040 Matrix Theory*
STAT:4520 Bayesian Statistics
STAT:5543 Intro to Statistical Methods

Additional Electives
(select two)
Any of the above OR courses selected in consultation with advisor.

     * A Mathematics minor can be earned by completing one qualifying Math course.

Advising Notes

  1. If you have special interest in a particular data mining domain it is recommend that you also take one or two courses that provide background in that domain. Introductory courses in bioinformatics, business, or marketing, are examples of domains where data mining is often applied.

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