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Description of Individual Course Units| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | ENM106 | Data mining | Elective | 1 | 2 | 6 |
| | Level of Course Unit | | Second Cycle | | Objectives of the Course | | 1-promoting the use and introduce the Data Miner 2-ability to gain large-scale databases analysis | | Name of Lecturer(s) | | Doç.Dr. Didem Güleryüz | | Learning Outcomes | | 1 | Mathematics, science, and engineering issues related to their own branches of sufficient knowledge; theoretical and practical information in these fields in modeling and solving engineering problems ability to. | | 2 | A complex system, process, device or product under realistic constraints and conditions, ability to design it to meet specific requirements; for this purpose, the ability to apply the methods of modern design. (Realistic constraints and conditions depending on the nature of the design, economics, environmental issues, sustainability, manufacturability, ethics, health, security, social and political problems, as they contain the items.) | | 3 | A complex system, process, device or product under realistic constraints and conditions, ability to design it to meet specific requirements; for this purpose, the ability to apply the methods of modern design. (Realistic constraints and conditions depending on the nature of the design, economics, environmental issues, sustainability, manufacturability, ethics, health, security, social and political problems, as they contain the items.) | | 4 | Engineering application of modern techniques and tools that are required for the development, selection and the ability to use; the ability to use effectively information technologies. | | 5 | Discipline and ability to work effectively in multidisciplinary teams; individual study skills. | | 6 | Designing experiments for the study of engineering problems, experiment, data collection, analyzing and interpreting the results of skill | | 7 | Turkish ability to communicate effectively orally and in writing; at least one foreign language. |
| | Mode of Delivery | | Normal Education | | Prerequisites and co-requisities | | None | | Recommended Optional Programme Components | | None | | Course Contents | | This course of statistical data mining, machinery, cars and includes the basics in terms of the data base. The course consists of three parts. First section for data mining, statistics and begin learning about the basics of my approach to the machine in the second section, Online analytical processing, the relationship rules and grouping for such things as basic data mining and algorithms. The last part of the course text mining, with filter, link analysis and biological areas focuses on research in areas such as mining. | | Weekly Detailed Course Contents | |
| 1 | "Introduction and General Definitions
| | | | 2 | Data Mining Applications | | | | 3 | Data Mining used in ready | | | | 4 | introduction of programs-Spreadsheet programs on data mining | | | | 5 | Preparation of data analysis (steps) | | | | 6 | OLAP | | | | 7 | Classification and Clustering | | | | 8 | Midterm Exam | | | | 9 | Data Mining statistics | | | | 10 | Data Mining, Artificial Intelligence | | | | 11 | Data Mining Neural Network | | | | 12 | Security ; Community Rules | | | | 13 | Data Mining other mining | | | | 14 | techniques-Web and text Mining, Data Mining-Industrial Applications | | | | 15 | | | |
| | Recommended or Required Reading | | Gökhan Schwartz, the concept and the Basic Data-Mining, Daisy publishing (2008)
Pang-Ning Tan, Michael Steinbach, Vipin Kumar, introduction to Data Mining, Addison Wesley, (2005).
Business intelligence and data mining, Sadi Universe sugar, Cinius Publications | | Planned Learning Activities and Teaching Methods | | | Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 30 | | End Of Term (or Year) Learning Activities | 70 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
| | Workload Calculation | |
| Midterm Examination | 1 | 1 | 1 | | Final Examination | 1 | 2 | 2 | | Quiz | 3 | 1 | 3 | | Attending Lectures | 14 | 3 | 42 | | Project Preparation | 1 | 20 | 20 | | Project Presentation | 1 | 1 | 1 | | Project Design/Management | 1 | 10 | 10 | | Individual Study for Homework Problems | 3 | 6 | 18 | | Individual Study for Mid term Examination | 6 | 3 | 18 | | Individual Study for Final Examination | 12 | 3 | 36 | | Individual Study for Quiz | 6 | 3 | 18 | |
| Contribution of Learning Outcomes to Programme Outcomes | | LO1 | | | | 2 | | | 4 | | 3 | | 3 | | | LO2 | | 4 | | 2 | 5 | | 5 | 4 | | | 3 | | | LO3 | 4 | 5 | | 4 | 4 | 4 | 4 | | 4 | | 4 | | | LO4 | 3 | 4 | | 5 | 5 | 5 | 4 | | | 4 | 3 | 3 | | LO5 | | 4 | | 5 | 5 | | | | | 4 | | | | LO6 | 4 | | | 4 | | 5 | 4 | | | | 5 | 5 | | LO7 | | | | | | 4 | | 3 | | 5 | | |
| | * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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