BÄ°L713 - DATA MINING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| DATA MINING | BÄ°L713 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Preparing and/or Presenting Reports Project Design/Management | |||||
| Instructor (s) | Department Responsible (bbm-bologna@cs.hacettepe.edu.tr) | |||||
| Course objective | The aim of this course is to learn the fundamentals of data processing and data mining. The students will gain understanding on how to extract interesting patterns on large datasets and the latest research problems in this area will be discussed. | |||||
| Learning outcomes |
| |||||
| Course Content | Introduction to data mining pipeline, data preprocessing and cleaning, classification methods, clustering, association rule mining, series analysis and sequence mining, graph mining, web mining | |||||
| References | ? P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 ? J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006 ? I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction To Data Mining Pipeline |
| Week 2 | Data Preprocessing And Cleaning |
| Week 3 | Classification Methods |
| Week 4 | Clustering |
| Week 5 | Commonly Used Patterns Mining |
| Week 6 | Association Rule Mining |
| Week 7 | Series Analysis |
| Week 8 | Sequence Mining |
| Week 9 | Web Mining |
| Week 10 | Midterm exam |
| Week 11 | Data mining applications |
| Week 12 | Data mining tools |
| Week 13 | Student presentations |
| Week 14 | Student presentations |
| Week 15 | Study of final exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 1 | 10 |
| Project | 1 | 20 |
| Seminar | 0 | 0 |
| Midterms | 1 | 20 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 3 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 8 | 4 | 32 |
| Presentation / Seminar Preparation | 1 | 30 | 30 |
| Project | 1 | 60 | 60 |
| Homework assignment | 0 | 0 | 0 |
| Midterms (Study duration) | 1 | 26 | 26 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total Workload | 26 | 163 | 230 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge. | X | ||||
| 2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency. | X | ||||
| 3. Graduates of a computer science program need to understand how theory and practice influence each other. | X | ||||
| 4. Graduates need to think at multiple levels of detail and abstraction. | X | ||||
| 5. Students will be able to think critically, creatively and identify problems in their research. | X | ||||
| 6. Graduates should have been involved in at least one substantial project. | X | ||||
| 7. Graduates should realize that the computing field advances at a rapid pace. | X | ||||
| 8. Graduates should conduct research in an ethical and responsible manner. | X | ||||
| 9. Graduates should have good command of technical terms in both Turkish and English. | X | ||||
| 10. Graduates should understand the full range of opportunities available in computing. | X | ||||
| 11. Graduates should understand that computing interacts with many different domains. | X | ||||
| 12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest