|
|
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
|
|
