Data Science Ethics with R

Overview

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Develop the ethical mindset every data scientist needs. In this course, you’ll examine the real-world implications of how data are collected, analyzed, and presented and the role of ethics in ensuring fairness, transparency, and trust. Through examples and case studies, you’ll learn to recognize misrepresentation in visualizations, algorithmic bias in models, and privacy risks in data collection. You’ll also explore strategies for mitigating these challenges and communicating results responsibly. By the end of this course, you’ll be able to identify ethical risks, apply frameworks for responsible data use, and make informed choices that uphold integrity in your analyses.

Syllabus

Data Ethics

Data ethics is an essential component for those who work with data. In this module, we will become aware and hold discussions around how data visualizations can mislead and strategies to mitigate these types of situations. Further, we will discuss and critically think about data privacy. Lastly, we will define algorithmic bias and be aware of situations where this type of bias can occur.