Ethical Issues in Data Science
Explore essential ethical considerations in data science, from privacy and algorithmic bias to professional ethics and healthcare applications.
Explore essential ethical considerations in data science, from privacy and algorithmic bias to professional ethics and healthcare applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Vital Skills for Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Identify and analyze ethical challenges in data science
Apply ethical frameworks to real-world scenarios
Understand privacy and security implications
Evaluate algorithmic bias and fairness
Assess ethical considerations in healthcare applications
Skills you'll gain
This course includes:
6.75 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course examines ethical challenges in modern data science. Students explore fundamental ethical frameworks and their application to real-world scenarios in data science and computing. Topics include privacy concerns, security breaches, professional ethics in tech companies, algorithmic bias, facial recognition technologies, and ethical implications of AI in healthcare. The curriculum combines theoretical foundations with practical case studies, encouraging critical thinking about ethical decision-making in data science careers.
Ethical Foundations
Module 1 · 3 Hours to complete
Internet, Privacy, and Security
Module 2 · 4 Hours to complete
Professional Ethics
Module 3 · 4 Hours to complete
Algorithmic Bias
Module 4 · 6 Hours to complete
Medical Applications and Implications
Module 5 · 5 Hours to complete
Fee Structure
Instructor
Distinguished Leader in Computer Science and Education
Bobby Schnabel is a Professor and External Chair of Computer Science at the University of Colorado Boulder, where he also serves as the Faculty Director for Entrepreneurship in the College of Engineering and Applied Science. He previously held the role of CEO of the Association for Computing Machinery (ACM) from 2015 to 2017 and was Dean of the School of Informatics and Computing at Indiana University from 2007 to 2015. Schnabel was part of the Computer Science faculty at CU Boulder from 1977 to 2007, during which time he served as CS department chair from 1990 to 1995, associate dean for academic affairs from 1995 to 1997, founding director of the ATLAS Institute from 1997 to 2007, and vice provost for academic and campus computing and Chief Information Officer from 1998 to 2007. He is a co-founder of the National Center for Women & Information Technology (NCWIT) and remains active on its executive team. Additionally, he co-founded the AAAI/ACM Conference on AI, Ethics and Society and chairs the ACM task force on ethics in computing education.
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