Addressing Ethical Dilemmas in Data Science
( 30 Modules )

Module #1
Introduction to Ethical Dilemmas in Data Science
Overview of the importance of ethics in data science and the types of ethical dilemmas data scientists face

Module #2
The Role of Data Science in Society
Exploring the impact of data science on society and the responsibilities that come with it

Module #3
Ethical Frameworks for Data Science
Introduction to common ethical frameworks and principles for data science, such as fairness, transparency, and accountability

Module #4
Case Studies in Ethical Dilemmas
Real-world examples of ethical dilemmas in data science, including bias in facial recognition and privacy concerns in predictive policing

Module #5
The Importance of Ethics in Data Science Teams
Strategies for incorporating ethical considerations into data science teams and organizational cultures

Module #6
Understanding Bias in Machine Learning
Introduction to types of bias in machine learning, including data bias, algorithmic bias, and societal bias

Module #7
Identifying and Mitigating Bias
Techniques for identifying and mitigating bias in data and algorithms, including data augmentation and bias metrics

Module #8
Fairness in Machine Learning
Introduction to fairness metrics and techniques, including demographic parity and equalized odds

Module #9
Case Studies in Bias and Fairness
Real-world examples of bias and fairness in data science, including credit scoring and college admissions

Module #10
Best Practices for Fair and Unbiased Models
Guidelines for developing and deploying fair and unbiased models in data science

Module #11
Introduction to Data Privacy
Overview of data privacy principles and regulations, including GDPR and CCPA

Module #12
Privacy-Preserving Data Science
Techniques for preserving privacy in data science, including anonymization, encryption, and differential privacy

Module #13
Security in Data Science
Introduction to security threats and best practices in data science, including data breaches and insider threats

Module #14
Case Studies in Privacy and Security
Real-world examples of privacy and security breaches in data science, including Equifax and Cambridge Analytica

Module #15
Best Practices for Privacy and Security
Guidelines for ensuring privacy and security in data science workflows and organizational cultures

Module #16
Introduction to Human Subjects Research
Overview of human subjects research regulations and guidelines, including IRBs and informed consent

Module #17
Ethical Considerations in A/B Testing
Ethical considerations in A/B testing, including informed consent and avoiding harm

Module #18
Case Studies in Human Subjects Research
Real-world examples of human subjects research in data science, including Facebooks emotional contagion study

Module #19
Best Practices for Human Subjects Research
Guidelines for conducting ethical human subjects research in data science

Module #20
Collaborating with Domain Experts and Stakeholders
Strategies for collaborating with domain experts and stakeholders in human subjects research

Module #21
Introduction to Transparency in Data Science
Overview of transparency principles and techniques in data science, including model interpretability and explainability

Module #22
Transparency in Model Development
Strategies for developing transparent models, including model interpretability and visualization

Module #23
Transparency in Model Deployment
Techniques for deploying transparent models, including model explanation and accountability

Module #24
Case Studies in Transparency and Accountability
Real-world examples of transparency and accountability in data science, including the Google AI Principles

Module #25
Best Practices for Transparency and Accountability
Guidelines for ensuring transparency and accountability in data science workflows and organizational cultures

Module #26
Ethical Considerations in Emerging Technologies
Exploring ethical considerations in emerging technologies, including AI, blockchain, and IoT

Module #27
Global Perspectives on Ethics in Data Science
Examining global perspectives on ethics in data science, including cultural and regional differences

Module #28
The Future of Ethics in Data Science
Future directions for ethics in data science, including the role of governments, organizations, and individuals

Module #29
Personal and Professional Development in Ethics
Strategies for personal and professional development in ethics, including training and community engagement

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Addressing Ethical Dilemmas in Data Science career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Addressing Ethical Dilemmas in Data Science ( 30 Modules )

Addressing Ethical Dilemmas in Data Science
( 30 Modules )