ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Data Science (Master) (with Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code: | DATS5025 | ||||
| Course Name: | Social Network Analysis and Data-driven Journalism | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | E-Learning | ||||
| Course Coordinator: | Araş. Gör. KAZIM TİMUÇİN UTKAN | ||||
| Course Lecturer(s): | |||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | This course aims to equip students with a comprehensive understanding of Social Network Analysis (SNA) and its pivotal role in modern data-driven journalism. The students will explore the interdisciplinary nature of SNA, its methodological approaches, and how it can uncover complex societal and communicative networks. The course will stress the importance of critical thinking and ethical considerations when analyzing data for stories. By the end of the course, students will be able to leverage SNA to tell impactful stories and derive insightful narratives from large datasets. |
| Course Content: | 1. Introduction to Social Network Analysis (SNA) and its applications in journalism. 2. Data collection methods for SNA in the context of news and societal trends. 3. The role of metrics and algorithms in understanding network structures and patterns. 4. Case studies of data-driven journalism projects utilizing SNA techniques. 5. Ethical considerations and privacy concerns in SNA applied to journalism. |
Learning Outcomes
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The students who have succeeded in this course;
1) - Ability to apply SNA methods to investigate social structures in journalistic research. 2) - Proficiency in using SNA software and tools to collect and analyze network data. 3) - Understanding of key network metrics and their interpretations in the context of news stories. 4) - Capability to critically assess the ethical implications of data usage in journalism. 5) - Development of a portfolio of data-driven stories using social network analysis techniques. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Social Network Analysis; its history and relevance in journalism. | - |
| 2) | Core concepts and terminology of network theory. | - |
| 3) | Basic network metrics - centrality measures, clustering, and communities. | - |
| 4) | Data sourcing for SNA in journalism, focusing on public and private databases. | - |
| 5) | Tools and software for network analysis and visualization. | - |
| 6) | Understanding algorithms for network pattern detection and analysis. | - |
| 7) | Exploring network dynamics over time – growth, evolution, and decay. | - |
| 8) | Midterm Exam. | - |
| 9) | Review of Midterm and introduction to advanced SNA techniques. | - |
| 10) | Applying SNA to current events and viral trends. | - |
| 11) | Case studies: successful integration of SNA in investigative reporting. | - |
| 12) | Crafting narratives and storytelling with data. | - |
| 13) | Big data, privacy, and ethical considerations in SNA for journalism. | - |
| 14) | Interactive and multimedia aspects of data journalism. | - |
| 15) | Student presentations of SNA projects in journalism. | - |
| 16) | Final Exam | - |
Sources
| Course Notes / Textbooks: | Herhangi bir ders kitabı bulunmamaktadır. There is no textbook. |
| References: | Güncel makaleler, kitaplar kullanılacaktır. Current articles and books will be used. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes | |||||
| 1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 | 3 | 3 | 3 | 3 |
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 | 3 | 3 | 3 | 3 |
| 3) Students who complete this program; Recognize the mathematics and code in application processes | 3 | 3 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 | 3 | 3 | 3 | 3 |
| 5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 | 3 | 3 | 3 | 3 |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 |
| 3) | Students who complete this program; Recognize the mathematics and code in application processes | 3 |
| 4) | Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 |
| 5) | Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 40 |
| Final | 1 | % 60 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Workload |
| Course Hours | 16 | 32 |
| Midterms | 8 | 86 |
| Final | 8 | 24 |
| Total Workload | 142 | |