TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB206 |
| Name | Data Mining |
| Term | 2020-2021 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | E Elective |
| Mode of study | Uzaktan Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜZİN YÜKSEL |
| Course Instructor |
Prof. Dr. GÜZİN YÜKSEL
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to give students the theoretical background of data mining algorithms and techniques and to give the student the ability to select and apply appropriate data mining techniques for different applications.
Course Content
Data preprocessing, association rule mining, classification, cluster analysis with applications.
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Define basic data mining concepts. |
| LO02 | Learn Data Mining Processes and Knowledge Discovery |
| LO03 | Understand Database Support to Data Mining. |
| LO04 | Apply several algorithms of data mining techniques |
| LO05 | Learn Data Mining in Business |
| LO06 | Determine which data mining technique is appropriate to solve a particular problem |
| LO07 | Apply preprocessing operations on data. |
| LO08 | Design a data mining model. |
| LO09 | Implement a data mining algorithm |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics | 1 |
| PLO02 | - | Emphasize the importance of Statistics in life | 5 |
| PLO03 | - | Define basic principles and concepts in the field of Law and Economics | 0 |
| PLO04 | - | Produce numeric and statistical solutions in order to overcome the problems | 5 |
| PLO05 | - | Use proper methods and techniques to gather and/or to arrange the data | 5 |
| PLO06 | - | Utilize computer systems and softwares | 5 |
| PLO07 | - | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | 4 |
| PLO08 | - | Apply the statistical analyze methods | 5 |
| PLO09 | - | Make statistical inference(estimation, hypothesis tests etc.) | 4 |
| PLO10 | - | Generate solutions for the problems in other disciplines by using statistical techniques | 5 |
| PLO11 | - | Discover the visual, database and web programming techniques and posses the ability of writing programme | 4 |
| PLO12 | - | Construct a model and analyze it by using statistical packages | 5 |
| PLO13 | - | Distinguish the difference between the statistical methods | 4 |
| PLO14 | - | Be aware of the interaction between the disciplines related to statistics | 4 |
| PLO15 | - | Make oral and visual presentation for the results of statistical methods | 3 |
| PLO16 | - | Have capability on effective and productive work in a group and individually | 3 |
| PLO17 | - | Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs | 2 |
| PLO18 | - | Develop scientific and ethical values in the fields of statistics-and scientific data collection | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Data Mining | Reading source books-Application | |
| 2 | Data Mining: A Closer View | Reading source books-Application | |
| 3 | Learning strategies | Reading source books-Application | |
| 4 | Machine learning process steps | Reading source books-Application | |
| 5 | Distance Measures | Reading source books-Application | |
| 6 | k-nearest neighbor algorithm | Reading source books-Application | |
| 7 | k nearest neighbor example | Reading source books-Application | |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes | |
| 9 | Naive Bayes Classification | Reading source books-Application | |
| 10 | Naive Bayes Algorithm and its application | Reading source books-Application | |
| 11 | ID3 and C4.5 Decision Tree Algorithms | Reading source books-Application | |
| 12 | ID3 and C4.5 Decision Tree Algorithms and their applications | Reading source books-Application | |
| 13 | K- Means Algorithm and its Applications | Reading source books-Application | |
| 14 | K- Means Algorithm and its Applications | Reading source books-Application | |
| 15 | Presentations | Reading source books-Application | |
| 16 | Term Exams | Review the topics discussed in the lecture notes | |
| 17 | Term Exams | Review the topics discussed in the lecture notes |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 78 | ||
| Total Workload / 25 (h) | 3,12 | ||
| ECTS | 3 ECTS | ||