| Course Type | Elective Course |
|---|
| Course Level | Master's Degree Without Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- will be able to explain the theory of the basic concepts of data mining
- will be able to prepare the data warehouse from operational database(s)
- will be able to explain the data mining tasks
- will be able to explain the components of data mining
- will be able to explain the relationship between decision processes and business intelligence
- will be able to differentiate data mining methods from the other analytical methods and business intelligence solutions
- will be able to prepare data mining projects according to the CRISP-DM and the other data mining methodologies
- will be able to identify the proper data mining method according to the data and the goals
- will be able to explain the statistical infrastructure of data mining
|
| Mode of Delivery | E-Learning |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course provides students with a general knoıwledge on data mining techniques, and analyzing data sets and commenting on the results. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to data mining |
|
2nd Week |
Data mining tasks |
|
3rd Week |
Data mining tasks |
|
4th Week |
Components of data mining |
|
5th Week |
Components of data mining |
|
6th Week |
Comparison of data mining and the other analytical methods |
|
7th Week |
Comparison of data mining and the other analytical methods |
|
8th Week |
Midterm |
|
9th Week |
Decision processes, business intelligence and data mining |
|
10th Week |
Methodology of data mining |
|
11th Week |
Classification of data mining methods |
|
12th Week |
Statistical infrastructure of data mining |
|
13th Week |
Data mining methods and their applications |
|
14th Week |
Data mining methods and their applications |
|
| Recommended or Required Reading | 1. Hand, David, Mannila, Heikki and Smyth, Padhraic. Principles of Data Mining. MIT Press, London, 2001.
2. Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition). Springer Series in Statistics, 2009.
3. Witten, Ian H. and Frank, Eibe. Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), The Morgan Kaufmann Series in Data Management Systems), 2005. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Problem Solving
- Presentation
- Experiment
- Practice
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Assignment |
1 |
20 |
| Total(%) |
|
20 |
| Contribution of In-term Studies to Overall Grade(%) |
|
20 |
| Contribution of Final Examination to Overall Grade(%) |
|
80 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
|
|
|
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
12 |
168 |
| Preparation for Final exam |
1 |
65 |
65 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
|
|
|
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
1 |
30 |
30 |
| Total Work Load | 307 |
| Total Work Load / 30 (h) | 10.23 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
