Learn to navigate ethical challenges in data science, from privacy concerns to algorithmic bias, in this comprehensive introduction.
Learn to navigate ethical challenges in data science, from privacy concerns to algorithmic bias, in this comprehensive introduction.
This course explores the crucial ethical considerations in data science, focusing on privacy, data ownership, informed consent, and algorithmic fairness. Students examine real-world scenarios involving medical records, security concerns, and decision-making algorithms to understand the ethical implications of data analysis. The curriculum provides a framework for evaluating ethical dilemmas in data science, helping professionals make responsible decisions that balance innovation with ethical considerations. Designed for both practicing data scientists and those interested in the field's ethical dimensions.
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Instructors:
English
English
What you'll learn
Understand fundamental concepts of data ownership and privacy
Evaluate different aspects of privacy in data collection and usage
Master the principles and implementation of informed consent
Analyze fairness in algorithmic decision-making
Develop frameworks for ethical data science practices
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive introduction to ethical considerations in data science. Through practical examples and case studies, students explore fundamental concepts including data ownership, privacy protection, informed consent, and algorithmic fairness. The curriculum emphasizes the importance of ethical decision-making in data analysis and algorithm development, helping professionals understand the potential consequences of their work. Special attention is given to balancing technological advancement with ethical responsibilities.
Fee Structure
Instructor
1 Course
Distinguished Scholar Advancing Database Systems and Data Science
H.V. Jagadish (Hosagrahar Visvesvaraya Jagadish) is a preeminent computer scientist and the Edgar F. Codd Distinguished University Professor at the University of Michigan. He currently serves as Director of the Michigan Institute for Data Science (MIDAS) and has made groundbreaking contributions to database systems and data science. After earning his PhD from Stanford University in 1985, he began his career at AT&T Bell Laboratories before joining the University of Michigan in 1999. His research focuses on human-data interaction, database usability, and the ethical implications of data science. He has authored over 200 major papers and holds 38 patents in information management. His achievements have earned him numerous honors, including fellowship in the ACM, IEEE, and AAAS, the 2013 ACM SIGMOD Contributions Award, and the 2019 Distinguished Faculty Achievement Award from the University of Michigan. As a founding Editor-in-Chief of the Proceedings of the VLDB Endowment and member of the National Academies Panel on Data Governance, he continues to shape the fields of database management and data science through his work on data modeling, schema design, and analytics processes. Under his leadership, MIDAS has grown to include over 420 affiliate faculty members across multiple campuses, fostering interdisciplinary collaboration in data science and artificial intelligence.
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