| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Explain the theory of basic concepts in data mining.
- Prepare a data warehouse from an operational database/databases.
- Explain the tasks of data mining.
- Explain the components of data mining.
- Explain the relationship between decision processes and business intelligence.
- Distinguish between data mining and other analytical methods and business intelligence solutions.
- Develop data mining projects in accordance with CRISP-DM and other data mining methodologies.
- Identify methods suitable for the data and the objective.
- Explain the statistical infrastructure of data mining.
- Apply linear regression analysis, logistic regression analysis, K-nearest neighbor, K-means clustering analysis, hierarchical clustering methods, decision trees, and association rules using data mining software.
- Use open-source data mining software.
- Implement and manage non-complex data mining projects.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course provides an overview of the definition of data mining, its application areas, techniques, and models. The course also covers the stages of data mining, which include defining the goal, creating a suitable dataset for the goal, cleaning and preprocessing the data, reducing and transforming the data, selecting the data mining learning algorithm, evaluating the model, interpreting the discovered knowledge, and examining data mining learning algorithms. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to data mining |
|
2nd Week |
Data mining tasks |
|
3rd Week |
Data mining tasks |
|
4th Week |
Data mining components |
|
5th Week |
Data mining components |
|
6th Week |
Comparison of data mining and other analytical methods |
|
7th Week |
Comparison of data mining and other analytical methods |
|
8th Week |
Midterm exam |
|
9th Week |
Decision processes, business intelligence, and data mining |
|
10th Week |
Data mining methodology |
|
11th Week |
Classification of data mining methods |
|
12th Week |
Statistical infrastructure of data mining |
|
13th Week |
Data mining methods and applications |
|
14th Week |
Data mining methods and applications |
|
| Recommended or Required Reading | Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. MIT Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer Series in Statistics.
Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). The Morgan Kaufmann Series in Data Management Systems. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Practice
- Presentation
- Experiment
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
25 |
| Practice |
1 |
20 |
| Project |
1 |
10 |
| Total(%) |
|
55 |
| Contribution of In-term Studies to Overall Grade(%) |
|
55 |
| Contribution of Final Examination to Overall Grade(%) |
|
45 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
2 |
28 |
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1 |
1 |
| Homework |
|
|
|
| Project |
1 |
20 |
20 |
| Performance Practice |
1 |
10 |
10 |
| Total Work Load | 152 |
| Total Work Load / 30 (h) | 5.06 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
