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

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* 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. ([https://canvas.uw.edu/courses/1319253/files/folder/Readings PDF available on Canvas])
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* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A4:_Final project proposal|A4: Final project proposal]]
  
 
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Revision as of 23:19, 15 September 2019

This page is a work in progress.


Week 1: September 26

Day 1 plan

Introduction to Human Centered Data Science
What is data science? What is human centered? What is human centered data science?
Assignments due
Agenda

HCDS (Fall 2019)/Day 1 plan

Homework assigned
  • Read and reflect on both:
Resources




Week 2: October 3

Day 2 plan


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

HCDS (Fall 2019)/Day 2 plan

Homework assigned
Resources





Week 3: October 10

Day 3 plan

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

HCDS (Fall 2019)/Day 3 plan

Homework assigned
  • Read both, reflect on one:
Resources




Week 4: October 17

Day 4 plan


Introduction to mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing
Assignments due
  • Week 3 reading reflection
  • A2: Bias in data
Agenda

HCDS (Fall 2019)/Day 4 plan

Homework assigned
Qualitative research methods resources
Crowdwork research resources




Week 5: October 24

Day 5 plan

Research ethics for big data
privacy, informed consent and user treatment
Assignments due
  • Reading reflection
Agenda

HCDS (Fall 2019)/Day 5 plan

Homework assigned
Resources




Week 6: October 31

Day 6 plan

Data science and society
power, data, and society; ethics of crowdwork
Assignments due
  • Reading reflection
  • A3: Crowdwork ethnography
Agenda

HCDS (Fall 2019)/Day 7 plan

Homework assigned
  • Read both, reflect on one:
Resources




Week 7: November 7

Day 7 plan

Human centered machine learning
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection
  • A4: Project proposal
Agenda

HCDS (Fall 2019)/Day 6 plan

Homework assigned
  • Read and reflect: TBD
  • A5: Final project plan
Resources




Week 8: November 14

Day 8 plan

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

HCDS (Fall 2019)/Day 8 plan

Homework assigned
  • Reading and reflect: TBD (data science ethics survey paper)
  • A6: Final project presentation
Resources




Week 9: November 21

Day 9 plan

Data science in organizations
TBD
Assignments due
  • Reading reflection
Agenda

HCDS (Fall 2019)/Day 9 plan

Homework assigned
  • Read and reflect: TBD
  • A6: Final project presentation
  • A7: Final project report
Resources




Week 10: November 28 (No Class Session)

Assignments due
  • Reading reflection
Readings assigned
  • NONE
Homework assigned
  • NONE
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.