Foundations of Responsible Data Science
in Data Science FundamentalsWhat you will learn?
Understand foundational ethical principles and their importance in data science.
Recognize key pillars of ethics such as privacy, fairness, and bias in data science applications.
Explain the societal impact of data science decisions and technologies.
Identify major data privacy laws, regulations (e.g., GDPR), and best practices for data protection.
Apply concepts of data anonymization, informed consent, and responsible handling of sensitive data.
Detect and analyze algorithmic bias and its societal consequences.
Understand the role of equity and social justice in data science ethics.
Communicate ethical considerations clearly and responsibly in interdisciplinary and public contexts.
Critically evaluate the human and societal impacts of data science initiatives, emphasizing marginalized communities.
Develop a reflective and responsible approach to ethical challenges throughout the data lifecycle.
About this course
This course provides a foundational understanding of ethical principles that guide responsible data science practice.
You will learn how to recognize and address issues such as bias, privacy risks, algorithmic fairness, transparency, and accountability.
The course highlights real-world challenges and introduces frameworks that help professionals build trustworthy and socially responsible models.
Recommended For
- Students beginning their journey in data science or analytics.
- Entry-level data analysts and aspiring data scientists.
- Professionals who work with customer or user data in any domain.
- Non-technical individuals transitioning into data-related roles.
- Educators introducing data or technology concepts to beginners.
- Managers and team leads overseeing data-driven projects.
- Startup founders and product managers working on data-centric product
- Anyone curious about ethical, fair, and responsible use of data and AI
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