Big Data/Data Mining/Machine Learning (Computer)

Data Mining FA


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

Big Data/Data Mining/Machine Learning 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.

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

Students interested in this FA are encouraged to consider the course suggestions listed below when completing their Plan of Study Form.

EE Computer Interest Requirements Suggested Options
Interest Computer Interest
Depth Elective
(Select One)
ECE:5330 Graph Algorithms and Combinatorial Optimization (Same as: IGPI:5331)
ECE:5320 High Performance Computer Architecture (Same as: CS:5610)
ECE:5450Machine Learning(Same as: IGPI:5450)
ECE:4480 Knowledge Discovery (Same as: CS:4480, MSCI:4480)
Breadth Elective
(Select One)
ECE:3400 Linear System II
ECE:3540 Communication Networks
ECE:3600 Control Systems
5000-Level ECE Elective
(Select Two)
All 5000-level depth electives listed above and


ECE:5995 Contemporary Topics in ECE
ECE:5550 Internet of Things
Technical Elective
(Select Three)
All breadth, depth and 5000-level ECE electives listed above and


CS:2230 Computer Science II: Data Structures (Required)
CS:5430 Machine Learning
CS:4400 Database Systems
CS:4420 Artificial Intelligence
CS:4440 Web Mining
CS:4980 Topics in Computer Science II (VARIES BY SEMESTER - Not all sections may be acceptable)
MATH:4040 Matrix Theory
STAT:4520 Bayesian Statistics
STAT:4143 Intro to Statistical Methods
STAT:4580 Data Visualization and Data Technologies
CS:4720 Optimization Techniques (Same as: MATH:4820)
IE:3149 Information Visualization
IE:4172 Big Data Analytics
CS:3700 Elementary Numerical Analysis (Same as: MATH:3800)
Additional Elective
(Select One*)
Any of the above OR course selected in consultation with advisor.


* Students graduating before Fall 2017 are required to select two additional electives.

Advising Notes

  • 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.
  • All Computer Interest students satisfy the requirements for a minor in Computer Science.
  • A minor in Mathematics can be earned by including one qualifying Math course in the FA plan.

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