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

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;Homework assigned
;Homework assigned
* Read and reflect: Passi, S., & Jackson, S. J. (2018). ''[https://dl.acm.org/citation.cfm?doid=3290265.3274405 Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects].'' Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–28. https://doi.org/10.1145/3274405
* Read and reflect: Passi, S., & Jackson, S. J. (2018). ''[https://dl.acm.org/citation.cfm?doid=3290265.3274405 Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects].'' Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–28. https://doi.org/10.1145/3274405 ([https://sjackson.infosci.cornell.edu/Passi&Jackson_TrustinDataScience(CSCW2018).pdf ACCESS PDF HERE])
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A7:_Final_project_report|A7: Final project report]]
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A7:_Final_project_report|A7: Final project report]]



Latest revision as of 19:22, 27 November 2019

This page is a work in progress.


Week 1: September 26[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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




Week 7: November 7[edit]

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[edit]

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[edit]

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
  • Filling out course evaluation
  • Week 8 in-class activity report out
  • End of quarter logistics
  • Final project presentations and reports
  • Guest lecture: Rich Caruana, Microsoft Research
  • In-class activity (InterpretML): Harsha Nori, Microsoft


Homework assigned
Resources




Week 10: November 28 (No Class Session)[edit]

Assignments due
  • Reading reflection
Homework assigned
Resources




Week 11: December 5[edit]

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)[edit]

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