Difference between revisions of "Human Centered Data Science (Fall 2019)/Schedule"

From CommunityData
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;Agenda
 
;Agenda
 
* Reading reflection review
 
* Reading reflection review
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* Guest lecture
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* A2 retrospective
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* Final project deliverables and timeline
 
* A brief history of research ethics in the United States
 
* A brief history of research ethics in the United States
* Research ethics in data science
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* Technological approaches to data privacy
 
* Guest lecture
 
* Procedural approaches to data privacy
 
  
 
;Homework assigned
 
;Homework assigned
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* Javier Salido (2012). ''[http://download.microsoft.com/download/D/1/F/D1F0DFF5-8BA9-4BDF-8924-7816932F6825/Differential_Privacy_for_Everyone.pdf Differential Privacy for Everyone].'' Microsoft Corporation Whitepaper.
 
* Javier Salido (2012). ''[http://download.microsoft.com/download/D/1/F/D1F0DFF5-8BA9-4BDF-8924-7816932F6825/Differential_Privacy_for_Everyone.pdf Differential Privacy for Everyone].'' Microsoft Corporation Whitepaper.
 
* Markham, Annette and Buchanan, Elizabeth. [https://aoir.org/reports/ethics2.pdf ''Ethical Decision-Making and Internet Researchers.''] Association for Internet Research, 2012.
 
* Markham, Annette and Buchanan, Elizabeth. [https://aoir.org/reports/ethics2.pdf ''Ethical Decision-Making and Internet Researchers.''] Association for Internet Research, 2012.
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* Kelley, P. G., Bresee, J., Cranor, L. F., & Reeder, R. W. (2009). ''[http://cups.cs.cmu.edu/soups/2009/proceedings/a4-kelley.pdf A “nutrition label” for privacy.]'' Proceedings of the 5th Symposium on Usable Privacy and Security - SOUPS ’09, 1990, 1. https://doi.org/10.1145/1572532.1572538
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* Warncke-Wang, M., Cosley, D., & Riedl, J. (2013). ''[https://opensym.org/wsos2013/proceedings/p0202-warncke.pdf Tell me more: An actionable quality model for wikipedia].'' Proceedings of the 9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013. https://doi.org/10.1145/2491055.2491063
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* Hill, Kashmir. [https://www.forbes.com/sites/kashmirhill/2014/06/28/facebook-manipulated-689003-users-emotions-for-science/#6a01653e197c ''Facebook Manipulated 689,003 Users' Emotions For Science.''] Forbes, 2014.
 
* Hill, Kashmir. [https://www.forbes.com/sites/kashmirhill/2014/06/28/facebook-manipulated-689003-users-emotions-for-science/#6a01653e197c ''Facebook Manipulated 689,003 Users' Emotions For Science.''] Forbes, 2014.
 
* Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock [http://www.pnas.org/content/111/24/8788.full ''Experimental evidence of massive-scale emotional contagion through social networks.''] PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
 
* Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock [http://www.pnas.org/content/111/24/8788.full ''Experimental evidence of massive-scale emotional contagion through social networks.''] PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
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* Zetter, Kim. [https://www.wired.com/2012/06/wmw-arvind-narayanan/ ''Arvind Narayanan Isn’t Anonymous, and Neither Are You.''] WIRED, 2012.
 
* Zetter, Kim. [https://www.wired.com/2012/06/wmw-arvind-narayanan/ ''Arvind Narayanan Isn’t Anonymous, and Neither Are You.''] WIRED, 2012.
 
* Gray, Mary. [http://culturedigitally.org/2014/07/when-science-customer-service-and-human-subjects-research-collide-now-what/ ''When Science, Customer Service, and Human Subjects Research Collide. Now What?''] Culture Digitally, 2014.
 
* Gray, Mary. [http://culturedigitally.org/2014/07/when-science-customer-service-and-human-subjects-research-collide-now-what/ ''When Science, Customer Service, and Human Subjects Research Collide. Now What?''] Culture Digitally, 2014.
* Tene, Omer and Polonetsky, Jules. [https://www.stanfordlawreview.org/online/privacy-paradox-privacy-and-big-data/ ''Privacy in the Age of Big Data.''] Stanford Law Review, 2012.
 
 
* Dwork, Cynthia. [https://www.microsoft.com/en-us/research/wp-content/uploads/2008/04/dwork_tamc.pdf ''Differential Privacy: A survey of results'']. Theory and Applications of Models of Computation , 2008.
 
* Dwork, Cynthia. [https://www.microsoft.com/en-us/research/wp-content/uploads/2008/04/dwork_tamc.pdf ''Differential Privacy: A survey of results'']. Theory and Applications of Models of Computation , 2008.
 
* Hsu, Danny. [http://blog.datasift.com/2015/04/09/techniques-to-anonymize-human-data/ ''Techniques to Anonymize Human Data.''] Data Sift, 2015.
 
* Hsu, Danny. [http://blog.datasift.com/2015/04/09/techniques-to-anonymize-human-data/ ''Techniques to Anonymize Human Data.''] Data Sift, 2015.
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Revision as of 21:32, 24 October 2019

This page is a work in progress.


Week 1: September 26

Introduction to Human Centered Data Science
What is data science? What is human centered? What is human centered data science?
Assignments due
Agenda
  • Syllabus review
  • Pre-course survey results
  • What do we mean by data science?
  • What do we mean by human centered?
  • How does human centered design relate to data science?
  • In-class activity
  • Intro to assignment 1: Data Curation
Homework assigned
  • Read and reflect on both:
Resources




Week 2: October 3

Reproducibility and Accountability
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 1 reading reflection
  • A1: Data curation
Agenda
  • Reading reflection discussion
  • Assignment 1 review & reflection
  • A primer on copyright, licensing, and hosting for code and data
  • Introduction to replicability, reproducibility, and open research
  • In-class activity
  • Intro to assignment 2: Bias in data
Homework assigned
Resources




Week 3: October 10

Interrogating datasets
causes and consequences of bias in data; best practices for selecting, describing, and implementing training data
Assignments due
  • Week 2 reading reflection
Agenda
  • Reading reflection review
  • Sources and consequences of bias in data collection, processing, and re-use
  • In-class activity
Homework assigned
  • Read both, reflect on one:
Resources




Week 4: October 17

Introduction to qualitative and mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing
Assignments due
  • Reading reflection
  • A2: Bias in data
Agenda
  • Reading reflection reflection
  • Overview of qualitative research
  • Introduction to ethnography
  • In-class activity: explaining art to aliens
  • Mixed methods research and data science
  • An introduction to crowdwork
  • Overview of assignment 3: Crowdwork ethnography
Homework assigned
Resources





Week 5: October 24

Research ethics for big data
privacy, informed consent and user treatment
Assignments due
  • Reading reflection
Agenda
  • Reading reflection review
  • Guest lecture
  • A2 retrospective
  • Final project deliverables and timeline
  • A brief history of research ethics in the United States


Homework assigned
  • Read and reflect: Gray, M. L., & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Eamon Dolan Books. (PDF available on Canvas)
Resources




Week 6: October 31

Data science and society
power, data, and society; ethics of crowdwork
Assignments due
  • Reading reflection
  • A3: Crowdwork ethnography
Agenda
  • Reading reflections
  • Assignment 3 review
  • In-class activity
  • Introduction to assignment 4: Final project proposal
Homework assigned
  • Read both, reflect on one:
Resources




Week 7: November 7

Human centered machine learning
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection
  • A4: Project proposal
Agenda
  • Reading reflection review
  • Algorithmic transparency, interpretability, and accountability
  • Auditing algorithms
  • In-class activity
  • Introduction to assignment 5: Final project proposal
Homework assigned
Resources




Week 8: November 14

User experience and data science
algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems
Assignments due
  • Reading reflection
  • A5: Final project plan
Agenda
  • coming soon
Homework assigned
Resources




Week 9: November 21

Data science in context
Doing human centered datascience in product organizations; communicating and collaborating across roles and disciplines; HCDS industry trends and trajectories
Assignments due
  • Reading reflection
Agenda
  • coming soon
Homework assigned
Resources




Week 10: November 28 (No Class Session)

Assignments due
  • Reading reflection
Homework assigned
Resources




Week 11: December 5

Final presentations
presentation of student projects, course wrap up
Assignments due
  • Reading reflection
  • A5: Final presentation
Readings assigned
  • NONE
Homework assigned
  • NONE
Resources
  • NONE




Week 12: Finals Week (No Class Session)

  • NO CLASS
  • A7: FINAL PROJECT REPORT DUE BY 5:00PM on Tuesday, December 10 via Canvas
  • LATE PROJECT SUBMISSIONS NOT ACCEPTED.