| Learning Outcomes |
|
| 1 - Knowledge |
| Theoretical - Conceptual |
1) Defines the concepts of data security, privacy, and ethics.
|
2) Explains the implications of KVKK and GDPR regulations on data-science applications.
|
3) Describes the theoretical basis of cryptography, anonymization, and differential privacy.
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4) Defines the principles of risk management within data-security and privacy frameworks.
|
| 2 - Skills |
| Cognitive - Practical |
1) Applies appropriate security algorithms for data protection.
|
2) Implements anonymization and differential privacy methods on real datasets.
|
3) Analyzes security vulnerabilities and proposes mitigation strategies.
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4) Performs data-security applications using R, Python, or similar environments.
|
| 3 - Competences |
| Communication and Social Competence |
1) Communicates data-security policies clearly to non-technical stakeholders.
|
2) Promotes a culture of security and privacy within team environments.
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3) Actively participates in ethical decision-making processes.
|
| Learning Competence |
1) Follows emerging security technologies and privacy protocols.
|
2) Learns up-to-date threat models and legal frameworks.
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3) Applies acquired knowledge to new data-science projects.
|
| Field Specific Competence |
1) Integrates security layers into data-science and machine-learning projects.
|
2) Employs differential privacy, homomorphic encryption, and SMPC techniques where appropriate.
|
3) Develops secure data-processing workflows considering ethical, legal, and technical dimensions.
|
| Competence to Work Independently and Take Responsibility |
1) Takes responsibility for addressing data-privacy violations.
|
2) Designs secure data-management policies.
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3) Conducts independent security testing and auditing activities.
|
FENERBAHÇE UNIVERSITY INFORMATION PACKAGE / COURSE CATALOGUE
