(IT 533)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| IT 533 | 2 | 4 | 0 | 0 | 3 | 8 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Compulsory |
| Course Level | Masters Degree |
| Course Instructor(s) | Günce Keziban ORMAN korman@gsu.edu.tr (Email) |
| Assistant | |
| Objective | This class aims at introducing the data mining process to students. This includes the description of data preparation and preprocessing, of various data mining algorithms and of the tools available to assess their results. The class focuses on standard approaches regarding association rules mining, supervised classification and unsupervised classification (clustering). Basic statistical knowledge is necessary to understand the mining algorithms and the quality assessment tools. |
| Content |
W1: Introduction, overview W2: Descriptive Statistics W3: Data Preprocessing W4: Inferential Statistics and its preprocessing tools W5: Code Application 1 W6: Regression W7: Classification1 W8: Classification2 W9: Clustering1, 2 W10: Code Application 2 W11: Project Presentations |
| 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 |
• Data Mining - Practical Machine Learning Tools, 2nd edition, Ian H. Witten & Eibe Frank, Morgan Kaufmann, 2005. • Neural Networks - A Comprehensive Foundation, 2nd edition, Simon Haykin, Pearson/Prentice Hall,1999. • Data Mining: Concepts and Techniques, Jiawei Han & Micheline Kamber, Morgan Kaufmann, 2000. • Applied Statistics and Probabilities for Engineers, 4th edition, D.C. Montgomery & G.C. Runger, John Willey & sons, 2006. • 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, overview |
| 2 | Descriptive Statistics |
| 3 | Data Preprocessing |
| 4 | Inferential Statistics and its preprocessing tools |
| 5 | Code Application 1 |
| 6 | Regression |
| 7 | Classification1 |
| 8 | Classification2 |
| 9 | Clustering1,2 |
| 10 | Code Application 2 |
| 11 | Project 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 | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 50 |
| 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 | 1 | 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 | ||||||
| 11 | X | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 9 | 4 | 36 |
| Working Hours out of Class | 9 | 10 | 90 |
| Assignments | 2 | 5 | 10 |
| Presentation | 1 | 10 | 10 |
| Midterm Examinations (including preparation) | 0 | 0 | 0 |
| Project | 1 | 20 | 20 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 15 | 15 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 1 | 10 | 10 |
| 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 | 191 | ||
| Total Workload / 25 | 7.64 | ||
| Credits ECTS | 8 | ||