Data Science(ISI 524)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| ISI 524 | 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 |
1. An introduction to data mining and predictive analytics 2. Data preprocessing, exploratory data analysis 3. Dimension-reduction methods, univariate statistical analysis 4. Multivariate statistics, preparing to model the data 5. Simple linear regression, multiple regression 6. Model building 7. k-nearest neighbor algorithm, decision trees 8. Logistic regression, naïve bayes and Bayesian networks 9. Midterm exam 10. Model evaluation techniques 11. Graphical evaluation of classification models 12. Hierarchical and k-means clustering, measuring cluster goodness 13. Association rules, ensemble methods 14. Student presentations |
| Course Learning Outcomes | |
| Teaching and Learning Methods | |
| 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 | An introduction to data mining and predictive analytics |
| 2 | Data preprocessing, exploratory data analysis |
| 3 | Dimension-reduction methods, univariate statistical analysis |
| 4 | Multivariate statistics, preparing to model the data |
| 5 | Simple linear regression, multiple regression |
| 6 | Model building |
| 7 | k-nearest neighbor algorithm, decision trees |
| 8 | Logistic regression, naïve bayes and Bayesian networks |
| 9 | Midterm exam |
| 10 | Model evaluation techniques |
| 11 | Graphical evaluation of classification models |
| 12 | Hierarchical and k-means clustering, measuring cluster goodness |
| 13 | Association rules, ensemble methods |
| 14 | Student presentations |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 50 |
| Contribution of final exam to overall grade | 1 | 50 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 3 | 30 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 20 |
| Project | 0 | 0 |
| 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 |
| Make-up | 0 | 0 |
| Toplam | 4 | 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 | |||||
| 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 | ||