Overview
Big data/data mining/machine learning concerns the analyses of enormous data sets and the extraction of meaning or useful information from them 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.
Big data/data mining/machine learning tasks include data summarization, clustering, classification, prediction, and dependency analysis. Big data/data mining/machine learning relies heavily on algorithms and statistical methods to uncover patterns and create models of the data.
This area 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:
| Computer Science and Engineering Requirements | Suggested Options |
|---|---|
| Theory Elective (Select one) |
ECE:5450 Machine Learning CS:5430 Machine Learning |
| 5000-Level ECE Elective (Select one) |
ECE:5330 Graph Algorithms and Combinatorial Optimization ECE:5320 High Performance Computer Architecture (Same as: CS:5610) ECE:5415:0001 Contemporary Topics in ECE: Radio Frequency Electronics ECE:5550 Internet of Things |
| 4000-level or above CS Elective (Select one) |
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) CS:4720 Optimization Techniques (Same as: MATH:4820) |
| ECE Elective (Select one) |
All 5000-level ECE electives listed above, and ENGR:2995 Intro to AI & Machine Learning in Engr |
| CS Elective (Select one) |
ll 4000-level and above CS electives listed above, and CS:3700 Elementary Numerical Analysis (Same as: MATH:3800) |
| Additional Electives (Select one 3 s.h. & one ≥2 s.h.) |
MATH:4040 Matrix Theory Any of the above OR course selected in consultation with advisor. |
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.
- A minor in mathematics can be earned by including two qualifying math courses in the FA plan.
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