Data Mining 

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
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