| 1 | Introduction to Data Mining: To help students understand the concept of data mining and grasp its basic terminology and principles. |
| 2 | Data Preprocessing Skills: To teach students the steps of data preprocessing and develop their ability to clean, transform, and prepare real-world data. |
| 3 | Data Visualization Competence: To enable students to visualize data effectively, analyze data distributions, and create data stories through visual analysis. |
| 4 | Classification and Clustering Methods: To help students understand classification and clustering algorithms and develop competence in analyzing data using these methods. |
| 5 | Fundamentals of Deep Learning: To ensure students understand artificial neural networks and deep learning concepts and learn the basic architectures and applications of deep learning. |
| 6 | Natural Language Processing and Text Mining: To introduce students to NLP techniques and enable them to carry out basic text mining applications. |
| 7 | Ethics and Privacy in Data Mining: To make students aware of ethical issues in data mining, emphasizing data security and privacy, and to develop their ability to work within ethical guidelines. |