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)

055:043 (ECE:3400) Linear Systems II
055:054 (ECE:3540) Communication Networks
055:060 (ECE:3600) Control Systems

Depth Elective
(select one)

055:133 (ECE:5330) Graph Theory and Combinatorial Optimization
055:145 (ECE:5450) Pattern Recognition
055:146 (ECE:5460) Digital Signal Processing
055:148 (ECE:5480) Digital Image Processing

100-Level ECE
Electives
(select two)

       All depth electives listed above and
055:132 (ECE:5320) High Performance Computer Architecture
055:180 (ECE:5800) Fundamentals of Software Engineering
055:182 (ECE:5820) Software Engineering Languages and Tools
055:183 (ECE:5830) Software Engineering Project

Technical Electives
(select three)

22C:021 (CS:2230) Computer Science II: Data Structures (Required)
       All breadth, depth and 100-level ECE electives listed above, and
055:195 (ECE:5995) Contemporary Topics in ECE
22C:141 (CS:6421) Knowledge Discovery
22C:144 (CS:4400) Database Systems
22C:145 (CS:4420) Artificial Intelligence
22C:146 (CS:4460) Introduction to Computational Linguistics
22C:149 (CS:4440) Web Mining
22C:196 (CS:4980) Topics in Computer Science II
22M:127 (MATH:4040) Matrix Theory*
22S:138 (STAT:4520) Bayesian Statistics
22S:102 (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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