| EUROPEAN UNION RELATIONS | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP4336 | Introduction to Data Mining | Spring | 3 | 0 | 3 | 6 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
| Language of instruction: | English |
| Type of course: | Non-Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
| Recommended Optional Program Components: | None |
| Course Objectives: | In this course, data mining algorithms and computational paradigms that are used to extract useful knowledge, extract patterns and regularities in databases, and perform prediction and forecasting will be discussed. Supervised and unsupervised learning approaches will be covered with a focus on pattern discovery and cluster analysis. |
|
The students who have succeeded in this course; 1. Be able to understand Data Gathering and Pre-processing 2. Become familiar with Frequent Item Set Detection 3. Be able to understand Association Rule Mining 4. Be able to understand Classifiers, and their benefits 5. Be able to use Clustering 6. Be able to understand Clustering Evaluation |
| 1.Introduction to Basic Concepts 2.Data Exploration 3.Classification 4.Clustering 5.Dimensionality Reduction 6.Frequent Item Set Mining 7.Association Rule Mining |
| Week | Subject | Related Preparation |
| 1) | Introduction to Basic Concepts | None |
| 2) | Data Exploration: Summary Statistics, Visualization, OLAP and Multi-dimensional Data Analysis | None |
| 3) | Data Pre-Processing, Transformation, Normalization, Standardization | None |
| 4) | Classification and Regression: Model Selection and Generalization, Decision Trees, Performance Evaluation | None |
| 5) | Classification: Bayesian Decision Theory, Parametric Classification, Naive Bayes Classifier, Instance-Based Classifiers | |
| 6) | Classification | None |
| 6) | Classification and Regression: Artificial Neural Networks, Support Vector Machines | |
| 7) | Midterm I | Review of all topics covered so far |
| 8) | Clustering: Partitioning and Hierarchical Algorithms | None |
| 9) | Clustering: Density-Based Algorithms | |
| 10) | Cluster Evaluation, Comparing Clusterings | None |
| 11) | Midterm II | none |
| 12) | Dimensionality Reduction | none |
| 13) | Frequent Item Set Mining | none |
| 14) | Association Rule Mining | none |
| Course Notes / Textbooks: | Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar |
| References: | Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber and Jian Pei |
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 2 | % 20 |
| Project | 1 | % 20 |
| Midterms | 2 | % 20 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 16 | 32 |
| Project | 5 | 15 |
| Homework Assignments | 6 | 12 |
| Midterms | 8 | 28 |
| Final | 6 | 26 |
| Total Workload | 155 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | To be able to examine, interpret data and assess ideas with the scientific methods in the area of EU studies. | 2 |
| 2) | To be able to inform authorities and institutions in the area of EU studies, to be able to transfer ideas and proposals supported by quantitative and qualitative data about the problems. | 2 |
| 3) | To be introduced to and to get involved in other disciplines that EU studies are strongly related with (political science, international relations, law, economics, sociology, etc.) and to be able to conduct multi-disciplinary research and analysis on European politics. | 3 |
| 4) | To be able to evaluate current news on European Union and Turkey-EU relations and identify, analyze current issues relating to the EU’s politics and policies. | 2 |
| 5) | To be able to use English in written and oral communication in general and in the field of EU studies in particular. | 1 |
| 6) | To have ethical, social and scientific values throughout the processes of collecting, interpreting, disseminating and implementing data related to EU studies. | 1 |
| 7) | To be able to assess the historical development, functioning of the institutions and decision-making system and common policies of the European Union throughout its economic and political integration in a supranational framework. | 2 |
| 8) | To be able to evaluate the current legal, financial and institutional changes that the EU is going through. | 2 |
| 9) | To explain the dynamics of enlargement processes of the EU by identifying the main actors and institutions involved and compare previous enlargement processes and accession process of Turkey. | 2 |
| 10) | To be able to analyze the influence of the EU on political, social and economic system of Turkey. | 2 |
| 11) | To acquire insight in EU project culture and to build up project preparation skills in line with EU format and develop the ability to work in groups and cooperate with peers. | 2 |
| 12) | To be able to recognize theories and concepts used by the discipline of international relations and relate them to the historical development of the EU as a unique post-War political project. | 3 |