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Learning Outcomes: Master of Science in Data Mining

Outcome

Courses

Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process f or Data Mining, including the business understanding phase, the data understanding phase, the expl or at or y data analysis phase, the modeling phase, the evaluation phase, and the deployment phase.

Stat 521
Stat 522
Stat 523
Stat 525

Be proficient with leading data mining software, including WEKA, Clementine by SPSS, and the R language.

Stat 521    Stat 522
Stat 523
    Stat 525
Stat 526
    Stat 527
CS 580

Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor, multiple regression, and neural networks.

Stat 521    Stat 522
Stat 523
    Stat 525
Stat 526
    Stat 527
Stat 570

Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.

Stat 526
Stat 527
Stat 529

Understand the mathematical statistics foundations of the algorithms outlined above.

Stat 416    Stat 575
Stat 567
    Stat 551
Stat 455
    Stat 570