HCDS (Fall 2017): Difference between revisions
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:''' | :'''Human Centered Data Science''' | ||
:''' | :'''DATA512''' - Interdisciplinary Data Science Masters Program | ||
:'''Instructor:''' [http://jtmorgan.net Jonathan T. Morgan] | :'''Instructor:''' [http://jtmorgan.net Jonathan T. Morgan] | ||
:'''TA:''' Oliver Keyes | |||
:'''Course Website''': We will use Canvas for [https://canvas.uw.edu/courses/1040891/announcements announcements], [https://canvas.uw.edu/courses/1040891/assignments turning in assignments], and [https://canvas.uw.edu/courses/1040891/discussion_topics discussion]. Everything else will be linked on this page. | :'''Course Website''': We will use Canvas for [https://canvas.uw.edu/courses/1040891/announcements announcements], [https://canvas.uw.edu/courses/1040891/assignments turning in assignments], and [https://canvas.uw.edu/courses/1040891/discussion_topics discussion]. Everything else will be linked on this page. | ||
:'''Course Description:''' | :'''Course Description:''' | ||
Fundamental principles of data science and its human implications. Data ethics, data privacy, differential privacy, algorithmic bias, legal frameworks and intellectual property, provenance and reproducibility, data curation and preservation, user experience design and usability testing for big data, ethics of crowdwork, data communication and societal impacts of data science. | |||
[[Category:HCDS (Fall 2017)]] | [[Category:HCDS (Fall 2017)]] |
Revision as of 23:14, 15 July 2017
This page is a work in progress.
Last updated: 19:08, 15 July 2017 (EDT)
- Human Centered Data Science
- DATA512 - Interdisciplinary Data Science Masters Program
- Instructor: Jonathan T. Morgan
- TA: Oliver Keyes
- Course Website: We will use Canvas for announcements, turning in assignments, and discussion. Everything else will be linked on this page.
- Course Description:
Fundamental principles of data science and its human implications. Data ethics, data privacy, differential privacy, algorithmic bias, legal frameworks and intellectual property, provenance and reproducibility, data curation and preservation, user experience design and usability testing for big data, ethics of crowdwork, data communication and societal impacts of data science.