| INTERNATIONAL TRADE AND BUSINESS | |||||
| 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 | Fall | 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) | Has theoretical and practical knowledge on management, business, trade, economy, entrepreneurship, innovation, sustainable development related to International Trade and Business and can use this information | |
| 2) | Can collect data from different sources in the global business world and successfully apply research techniques, use information and communication technologies. | |
| 3) | Can analyze opportunities and threats with strategic thinking skills by using different resources and channels in the ever-changing global business world. | |
| 4) | Can communicate orally and in writing with a good knowledge of English grammar. | |
| 5) | He / she can transfer the knowledge and skills he / she has acquired in the field to the relevant people in written and oral form and evaluate them critically. | |
| 6) | Adopts the principles of business ethics with the awareness of professional responsibility and can apply these principles within the framework of legal rules in the field of global trade and business. | |
| 7) | He / she can collaborate in and out of the field, take responsibility, respect cultural differences and have ethical values. | |
| 8) | Has sufficient awareness of social rights, justice, cultural values, environmental awareness, occupational health and safety. | |
| 9) | With the lifelong learning skill acquired, she/he can identify learning needs and improve herself/himself |