CENG514 DATA MINING
| Course Code: | 5710514 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Computer Engineering |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Prof.Dr. PINAR KARAGÖZ |
| Offered Semester: | Fall or Spring Semesters. |
Course Objectives
The objectives of this course are as follows:
- to learn basic data mining techniques
- to apply the basic data mining techniques for a given problem
- to understand basics of data preprocessing
- to learn the concepts in datawarehousing
- to learn about specific techniques for domains including web mining, social networks and recommender systems.
Course Content
Introduction to data mining and data warehousing, overview of relational data model and query languages, data warehousing and OLAP technology, data preparation, association rule mining, data mining and data confidentiality, classification and prediction, clustering, data mining under uncertainty, mining complex types of data, Web mining, multi-relational data mining.
Course Learning Outcomes
At the end of this course, students will be able to:
- understand the fundamental data mining techniques and apply them for a given problem
- understand basics of data preprocessing
- understand the fundamentals of datawarehousing
- have knowledge about specific techniques for domains including web mining, social networks and recommender systems.
- Compare and assess data mining techniques and determine the appropriate technique for a given problem.
Program Outcomes Matrix
| Contribution | |||||
| # | Program Outcomes | No | Yes | ||
| 1 | Competence in fundamental and advanced knowledge of hardware and software Proficiency in problem solving. | ✔ | |||
| 2 | The ability to follow the contemporary technical development, and Initiative and aptitude for self-directed learning. | ✔ | |||
| 3 | They are capable of designing, and conducting experiments at advanced level. | ✔ | |||
| 4 | The ability to design and implement systems involving hardware, software, and the interaction between the two through challenging projects. | ✔ | |||
| 5 | Analyze and compare relative merits of alternative software design, algorithmic approaches and computer system organization, with respect to a variety of criteria relevant to the task (e. g. efficiency, scalability, security). | ✔ | |||
| 6 | Strong interpersonal skills needed for working effectively in small, diverse groups on medium to large scale technical projects. | ✔ | |||
| 7 | Strong oral communication skills essential for effectively presenting technical material to an audience and strong written communication skills and the ability to write technical documents that include specification, design, and implementation of a major project. | ✔ | |||