Akademik Dersler - İçerik

0 : Does not support 1 : Low-level support 2 : Mid-level support 3 : Top level support

Learning Outcomes	1	2	3	4	5	6	7	8	9	10	11
The student can explain different data types, data structures and data quality concepts.	3	2	1	2	3	-	-	-	-	-	-
The student can analyze data characteristics and correctly apply similarity and dissimilarity measures.	2	3	-	3	3	-	-	-	-	-	-
The student can conduct data preprocessing by applying data cleaning, integration, transformation and reduction methods.	2	3	-	3	3	-	-	-	-	-	-
The student can compute support and confidence measures and apply the Apriori algorithm for frequent pattern mining.	2	3	-	3	3	-	-	-	-	-	-
The student can develop classification models using decision trees, Bayesian methods and rule-based approaches.	3	3	-	3	3	-	-	-	-	-	-
The student can perform data clustering and identify outliers using appropriate analytical techniques.	2	3	-	3	3	-	-	-	-	-	-
The student can apply fundamental data mining processes on real datasets and interpret the results.	-	3	-	3	3	-	1	1	-	-	-