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: | DATS5103 | ||||
| Course Name: | Data-Privacy-Ethics and Scientific Research | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Compulsory Courses | ||||
| Course Level: |
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| Mode of Delivery: | E-Learning | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): |
Doç. Dr. OKAN YAŞAR |
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| Course Assistants: |
Course Objective and Content
| Course Objectives: | The aim of this course is to equip students with a critical understanding of the ethical and privacy challenges in data science and scientific research. Students will explore both theoretical and practical aspects of data privacy, understand global data protection regulations, and examine the ethical implications of data handling. Through case studies and interactive discussions, the course will emphasize the development of ethical reasoning skills and the application of these principles to real-world scenarios in the context of scientific research. |
| Course Content: | 1. Introduction to Data Privacy and personal data protection concepts 2. Ethical frameworks and principles in data science research 3. Regulations and compliance: GDPR, HIPAA, and other global data protection laws 4. Data governance, stewardship, and ethical data management practices 5. Case studies of data breaches, ethical dilemmas in data science, and best practices in scientific research |
Learning Outcomes
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The students who have succeeded in this course;
1) Demonstrate an understanding of key issues relating to data privacy and ethics in data science. 2) Apply ethical principles and data protection laws to data science research projects. 3) Critically analyze the impact of data breaches and understand strategies for data protection. 4) Develop data governance strategies that align with ethical standards and regulatory requirements. 5) Reflect on and discuss ethical dilemmas in scientific research and propose solutions. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to the course; Importance of Data Privacy and Ethics in Research | |
| 2) | Personal data and sensitivity; Dimensions of privacy in digital contexts | |
| 3) | Overview of ethical frameworks; Utilitarianism, deontology, virtue ethics in data context | |
| 4) | Data protection laws; In-depth analysis of GDPR and its implications | |
| 5) | Privacy enhancing technologies and methods of anonymization | |
| 6) | Data stewardship and governance; Roles and responsibilities | |
| 7) | Ethical decision-making models and their application in data science | |
| 8) | Midterm Exam | |
| 9) | Review of Midterm Exam; Introduction to case study analysis | |
| 10) | HIPAA and health data; Privacy in different sectors | |
| 11) | International data protection and cross-border data transfers | |
| 12) | Machine learning ethics; Bias, fairness, and accountability | |
| 13) | Data breaches and security incidents; Prevention and response | |
| 14) | Research ethics in scientific inquiry; Ethical publishing and peer review | |
| 15) | Guest lecture/Workshop on ethical dilemmas in Data Science | |
| 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 | 2 | 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 | 2 |
| 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 | 14 | 42 |
| Midterms | 8 | 72 |
| Final | 8 | 108 |
| Total Workload | 222 | |