| BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, THESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP5101 | Data Mining | Fall | 3 | 0 | 3 | 8 |
| 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: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assoc. Prof. TEVFİK AYTEKİN |
| Course Lecturer(s): |
Assoc. Prof. TEVFİK AYTEKİN |
| Recommended Optional Program Components: | None |
| Course Objectives: | This course provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth |
|
The students who have succeeded in this course; I. Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase. II. Be proficient with leading data mining software, including WEKA III. Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor IV. Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues. V. Understand the mathematical statistics foundations of the algorithms outlined above VI. Evaluate current research and advanced topics in data mining. |
| 1. Frequent Item Set Detection 2. Association Rule Mining 3. Clustering 4. Classification |
| Week | Subject | Related Preparation |
| 1) | Introduction to Data Mining | None |
| 2) | Frequent Item Set Mining | None |
| 3) | Various frequent item set algorithms: Apriori, FPGrowth | None |
| 4) | Association Rule Mining | None |
| 5) | Classification | None |
| 6) | Bayesian classification | None |
| 7) | Midterm Exam 1 | Review all the topics |
| 8) | Rule based classification | None |
| 9) | Cluster Analysis | None |
| 10) | k-means | None |
| 11) | k-medoids | None |
| 12) | Hierarchical clustering | None |
| 13) | Cluster quality | None |
| 14) | Combining Multiple Clusterings | None |
| Course Notes / Textbooks: | Data Mining Concepts and Techniques Jiawei Han and Micheline Kamber Morgan Kaufman |
| References: |
| Semester Requirements | Number of Activities | Level of Contribution |
| Project | 5 | % 10 |
| Midterms | 1 | % 40 |
| Final | 1 | % 50 |
| 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 | 14 | 56 |
| Project | 16 | 48 |
| Midterms | 3 | 15 |
| Final | 7 | 35 |
| Total Workload | 196 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | 1. To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems. | 2 |
| 2) | To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management. | 5 |
| 3) | To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained. | 5 |
| 4) | Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards. | 3 |
| 5) | To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice. | 4 |
| 6) | Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively. | 4 |
| 7) | Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications. | 4 |