Data Science(INF 511)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF 511 | Data Science | 1 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | Gülfem ALPTEKİN gulfem@gmail.com (Email) |
| Assistant | |
| Objective | This course aims to introduce students to the data mining process. The main objectives of the course include understanding and applying data preparation and preprocessing techniques, various data mining algorithms, and the tools used to evaluate their results. The course focuses on standard approaches related to association rule mining, supervised classification, and unsupervised classification (clustering). Basic statistical knowledge is required to understand mining algorithms and quality evaluation tools. In this way, the course aims to enable students to produce practical solutions in the field of data analysis. |
| Content |
- data pre-processing - supervised classification - clustering - complex data mining - results validation and quality assessment |
| Course Learning Outcomes |
1. Data preparation 2. Theoretical and practical knowledge of standard data mining algorithms 3. Standard assessment tools |
| Teaching and Learning Methods |
theoretical & practical class assignments |
| References |
1. Data Mining - Practical Machine Learning Tools, 2nd edition, Ian H. Witten & Eibe Frank, Morgan Kaufmann, 2005. 2. Neural Networks - A Comprehensive Foundation, 2nd edition, Simon Haykin, Pearson/Prentice Hall,1999. 3. Data Mining: Concepts and Techniques, Jiawei Han & Micheline Kamber, Morgan Kaufmann, 2000. 4. Applied Statistics and Probabilities for Engineers, 4th edition, D.C. Montgomery & G.C. Runger, John Willey & sons, 2006. 5. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, T. Hastie, R. Tibshirani & J. Friedman, Springer, 2009. |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Introduction |
| 2 | Data preparation |
| 3 | Association rules and a priori algorithm |
| 4 | FP-trees and complex rules |
| 5 | Decision trees and naïve Bayes classifier |
| 6 | Statistical regression and Bayesian networks |
| 7 | Neural networks and other classifiers |
| 8 | Quality assessment on classification results |
| 9 | Classifier comparison |
| 10 | Distance and partitioning |
| 11 | Hierarchical clustering methods |
| 12 | Clustering with grids and density |
| 13 | Model-based processing |
| 14 | Outliers detection |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 2 | 50 |
| Contribution of final exam to overall grade | 1 | 50 |
| Toplam | 3 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 25 |
| Project | 1 | 25 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 2 | 50 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | X | |||||
| 2 | X | |||||
| 3 | X | |||||
| 4 | X | |||||
| 5 | X | |||||
| 6 | X | |||||
| 7 | X | |||||
| 8 | X | |||||
| 9 | X | |||||
| 10 | X | |||||
| 11 | X | |||||
| 12 | X | |||||
| 13 | X | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 13 | 3 | 39 |
| Working Hours out of Class | 13 | 3 | 39 |
| Assignments | 3 | 8 | 24 |
| Presentation | 2 | 2 | 4 |
| Midterm Examinations (including preparation) | 1 | 8 | 8 |
| Project | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 25 | 25 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Bütünleme | 0 | 0 | 0 |
| Total Workload | 139 | ||
| Total Workload / 25 | 5.56 | ||
| Credits ECTS | 6 | ||