Ethical Decision-Making in Data Science

As data-driven technologies shape our daily lives, the responsibility of data professionals has never been greater. This text-based course equips you with the practical frameworks to navigate complex ethical dilemmas, ensuring your data models and analyses are fair, transparent, and accountable. You will transition from understanding foundational ethical principles to applying practical decision-making frameworks in your daily data workflows. By reading through real-world scenarios and analyzing case studies, you will learn how to identify potential harms before they reach production. What you'll learn: 1. Understand foundational ethical theories and how they apply to modern data science and AI. 2. Identify and mitigate algorithmic bias and systemic unfairness in datasets. 3. Apply modern privacy-preserving techniques and understand global data protection standards. 4. Establish transparent data governance and model reproducibility protocols. 5. Design accountability frameworks for responsible AI deployment. 6. Navigate ethical dilemmas through structured, step-by-step written case studies. The course begins with essential terminology and the core philosophy of data ethics, before guiding you through structured frameworks for bias detection, privacy protection, and responsible model deployment. You will read detailed explanations and practice applying these concepts through written exercises and scenario-based analyses. Designed specifically for beginners, this course requires no prior background in programming or advanced statistics. Start building a more responsible, trustworthy career in data today.