Information Package / Course Catalogue
| Course Code | : MIS523 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 6 |
Text mining applications for social science studies with respect to conceptual integration of consciously selected methods, systematic optimization of algorithms and workflows, and methodological reflections relating to empirical research. In an exemplary study, he introduces workflows to analyze a corpus of around 600,000 newspaper articles on the subject of “democratic demarcation” in Germany. He provides a valuable resource for innovative measures to social scientists and computer scientists in the field of applied natural language processing. Contents • Qualitative Data Analysis in a Digital World • Computer-Assisted Text Analysis in the Social Sciences • Integrating Text Mining Applications for Complex Analysis
Text mining applications for social science studies with respect to conceptual integration of consciously selected methods, systematic optimization of algorithms and workflows, and methodological reflections relating to empirical research. In an exemplary study, he introduces workflows to analyze a corpus of around 600,000 newspaper articles on the subject of “democratic demarcation” in Germany. He provides a valuable resource for innovative measures to social scientists and computer scientists in the field of applied natural language processing. Contents • Qualitative Data Analysis in a Digital World • Computer-Assisted Text Analysis in the Social Sciences • Integrating Text Mining Applications for Complex Analysis
| 1. | Students will gain the knowledge and skills to learn and apply the basic concepts of multivariate data analysis. |
| 2. | Students will learn multivariate data preprocessing methods |
| 3. | Students will be able to analyze multivariate data using statistical techniques |
| 4. | Students will learn statistical learning methods. |
| 5. | Students will have knowledge about regression methods. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %40 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 15 | 3 | 3 | 90 |
| Assignment | 2 | 4 | 4 | 16 |
| Reading | 1 | 0 | 1 | 1 |
| Individual Work | 15 | 1 | 1 | 30 |
| Midterm Examination | 1 | 3 | 1 | 4 |
| Final Examination | 1 | 4 | 5 | 9 |
| TOTAL WORKLOAD (hours) | 150 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | |
OÇ-1 | 5 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |
OÇ-2 | 4 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
OÇ-3 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | |
OÇ-4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | |||
OÇ-5 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 5 | |