University of Colorado Boulder - Ethical Issues in Data Science
- Offered byCoursera
Ethical Issues in Data Science at Coursera Overview
Duration | 23 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Ethical Issues in Data Science at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the Vital Skills for Data Science Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level No specific background necessary.
- Approx. 23 hours to complete
- English Subtitles: English
Ethical Issues in Data Science at Coursera Course details
- Computing applications involving large amounts of data ? the domain of data science ? impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning.
- This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals.
- Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder?s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder?s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
- This course can be taken for academic credit as part of CU Boulder?s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder?s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Ethical Issues in Data Science at Coursera Curriculum
Ethical Foundations
Introduction to Ethical Issues in Data Science: Part 1
Introduction to Ethical Issues in Data Science: Part 2
Ethical Foundations I
Ethical Foundations II
Week 1: Review and Reflect
A Note About Reading Assignments
Ethics Overview
Ethical Foundations Readings
Ethical Foundations
Internet, Privacy, and Security
Internet Background and Implications for Privacy and Security
Privacy: Part 1
Privacy: Part 2
Security, Causes and Types of Breaches: Part 1
Security, Causes and Types of Breaches: Part 2
Privacy and the Right to Be Forgotten Readings
Security and Security Breaches Readings
Security and Security Breaches
Professional Ethics
Professional Society Codes of Ethics
Contemporary Ethical Issues from Tech Companies: Part 1
Contemporary Ethical Issues from Tech Companies: Part 2
Sharing Experiences of Data Science / Computing Professionals
Professional Society Codes of Ethics Readings
Contemporary Ethical Issues from Tech Companies Readings
Professional Ethics
Algorithmic Bias
Perspectives on Algorithmic Bias: Part 1
Perspectives on Algorithmic Bias: Part 2
Algorithmic Bias Related to Gender and Race: Part 1
Algorithmic Bias Related to Gender and Race: Part 2
Facial Recognition: Part 1
Facial Recognition: Part 2
Algorithmic Bias Readings
Algorithmic Bias Related to Gender and Race Readings
Facial Recognition Readings
Algorithmic Bias and Facial Recognition
Medical Applications and Implications
Data Science in Health Care: Part 1
Data Science in Health Care: Part 2
Gene Editing and Neurological Interventions: Part 1
Gene Editing and Neurological Interventions: Part 2
The Future of Work: Part 1
The Future of Work: Part 2
Data Science in Health Care Readings
Gene Editing and Neurological Interventions Readings
The Future of Work Readings
Ethical Issues in Data Science at Coursera Admission Process
Important Dates
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