The students who have succeeded in this course;
I. Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase.
II. Be proficient with leading data mining software, including WEKA
III. 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
IV. Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.
V. Understand the mathematical statistics foundations of the algorithms outlined above
VI. Evaluate current research and advanced topics in data mining. |
|
Program Outcomes |
Level of Contribution |
1) |
Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. |
4 |
1) |
Being able to independently carry out a work that requires expertise in the field. |
4 |
1) |
To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. |
4 |
1) |
To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. |
4 |
1) |
To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines, |
4 |
1) |
To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. |
5 |
2) |
To be able to comprehend the interdisciplinary interaction with which the field is related. |
4 |
2) |
To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. |
5 |
2) |
To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary. |
4 |
2) |
To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes. |
4 |
2) |
To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. |
5 |
3) |
To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. |
5 |
3) |
Being able to lead in environments that require the resolution of problems related to the field. |
4 |
3) |
To be able to solve the problems encountered in the field by using research methods. |
5 |