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

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;Resources
 
;Resources
 
* Singer, P., Lemmerich, F., West, R., Zia, L., Wulczyn, E., Strohmaier, M., & Leskovec, J. (2017, April). ''[https://arxiv.org/pdf/1702.05379.pdf Why we read wikipedia]''. In Proceedings of the 26th International Conference on World Wide Web.
 
* Singer, P., Lemmerich, F., West, R., Zia, L., Wulczyn, E., Strohmaier, M., & Leskovec, J. (2017, April). ''[https://arxiv.org/pdf/1702.05379.pdf Why we read wikipedia]''. In Proceedings of the 26th International Conference on World Wide Web.
 +
* [https://meta.wikimedia.org/wiki/Research:The_role_of_citations_in_how_readers_evaluate_Wikipedia_articles/Trust_taxonomy Taxonomy of reasons why people trust/distrust Wikipedia], Jonathan Morgan, Wikimedia Research report, May 2019.
 
* Ladner, S. (2016). ''[http://www.practicalethnography.com/ Practical ethnography: A guide to doing ethnography in the private sector]''. Routledge.
 
* Ladner, S. (2016). ''[http://www.practicalethnography.com/ Practical ethnography: A guide to doing ethnography in the private sector]''. Routledge.
 
* Spradley, J. P. (2016). ''[https://www.waveland.com/browse.php?t=688 The ethnographic interview]''. Waveland Press.
 
* Spradley, J. P. (2016). ''[https://www.waveland.com/browse.php?t=688 The ethnographic interview]''. Waveland Press.

Revision as of 22:15, 17 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
  • A brief history of research ethics in the United States
  • Research ethics in data science
  • Technological approaches to data privacy
  • Guest lecture
  • Procedural approaches to data privacy
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.