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=== Week 4: October 19 === | === Week 4: October 19 === | ||
[[HCDS_(Fall_2017)/Day_4_plan|Day 4 plan]] | [[HCDS_(Fall_2017)/Day_4_plan|Day 4 plan]] | ||
;Study design: ''understanding your data; framing research questions; planning your study'' | |||
;Assignments due | ;Assignments due | ||
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=== Week 5: October 26 === | === Week 5: October 26 === | ||
[[HCDS_(Fall_2017)/Day_5_plan|Day 5 plan]] | [[HCDS_(Fall_2017)/Day_5_plan|Day 5 plan]] | ||
;Machine learning: ''ethical AI, algorithmic transparency, societal implications of machine learning'' | |||
;Assignments due | ;Assignments due | ||
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=== Week 6: November 2 === | === Week 6: November 2 === | ||
[[HCDS_(Fall_2017)/Day_6_plan|Day 6 plan]] | [[HCDS_(Fall_2017)/Day_6_plan|Day 6 plan]] | ||
;Mixed-methods research: ''Big data vs thick data; qualitative research in data science '' | |||
;Assignments due | ;Assignments due | ||
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=== Week 7: November 9 === | === Week 7: November 9 === | ||
[[HCDS_(Fall_2017)/Day_7_plan|Day 7 plan]] | [[HCDS_(Fall_2017)/Day_7_plan|Day 7 plan]] | ||
;Human computation: ''ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation'' | |||
;Assignments due | ;Assignments due | ||
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=== Week 8: November 16 === | === Week 8: November 16 === | ||
[[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]] | [[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]] | ||
;User experience and big data: ''prototyping and user testing; benchmarking and iterative evaluation; UI design for data science'' | |||
;Assignments due | ;Assignments due | ||
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=== Week 9: November 23 === | === Week 9: November 23 === | ||
NO CLASS | NO CLASS | ||
;Human-centered data science in the wild: ''community data science; data science for social good'' | |||
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=== Week 10: November 30 === | === Week 10: November 30 === | ||
[[HCDS_(Fall_2017)/Day_10_plan|Day 10 plan]] | [[HCDS_(Fall_2017)/Day_10_plan|Day 10 plan]] | ||
;Communicating methods, results, and implications: translating for non-data scientists '' | |||
;Assignments due | ;Assignments due | ||
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=== Week 11: December 7 === | === Week 11: December 7 === | ||
[[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]] | [[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]] | ||
;Future of human centered data science: ''case studies from research, industry, and policy; final presentations'' | |||
;Assignments due | ;Assignments due | ||
Revision as of 01:01, 22 September 2017
This page is a work in progress.
Last updated on 08/08/2018 by Jtmorgan
Week 1: September 28
- Assignments due
- fill out the pre-course survey
- Agenda
- Course overview & orientation
- What do we mean by "data science?"
- What do we mean by "human centered?"
- How does human centered design relate to data science?
- Readings assigned
- Watch: Why Humans Should Care About Data Science (Cecilia Aragon, 2016 HCDE Seminar Series)
- Read: Aragon, C. et al. (2016). Developing a Research Agenda for Human-Centered Data Science Human Centered Data Science workshop] (CSCW 2016)
- Homework assigned
- Chose 1 additional position paper from the 2016 CSCW HCDS workshop and write a reflection.
- Resources
Week 2: October 5
- Legal and ethical considerations in data collection
- licensing and terms of use; informed consent and user expectations; limits of anonymization
- Assignments due
- reading reflection
- Agenda
- Informed consent in the age of Data Science
- Privacy
- User expectations
- Inferred information
- Correlation
- Anonymisation strategies
- Homework
- Resources
Week 3: October 12
- Data provenance, preparation, and reproducibility
- data curation, preservation, documentation, and archiving; best practices for open scientific research
- Assignments due
- Agenda
- Final project overview
- Introduction to open research
- Understanding data licensing and attribution
- Supporting replicability and reproducibility
- Making your research and data accessible
- Working with Wikipedia datasets
- Assignment 1 description
- Homework
- Resources
- go here
Week 4: October 19
- Study design
- understanding your data; framing research questions; planning your study
- Assignments due
- Agenda
- Homework
- Resources
Week 5: October 26
- Machine learning
- ethical AI, algorithmic transparency, societal implications of machine learning
- Assignments due
- Agenda
- Homework
- Resources
Week 6: November 2
- Mixed-methods research
- Big data vs thick data; qualitative research in data science
- Assignments due
- Agenda
- Homework
- Resources
Week 7: November 9
- Human computation
- ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation
- Assignments due
- Agenda
- Resources
- go here
Week 8: November 16
- User experience and big data
- prototyping and user testing; benchmarking and iterative evaluation; UI design for data science
- Assignments due
- Agenda
- Resources
Week 9: November 23
NO CLASS
- Human-centered data science in the wild
- community data science; data science for social good
- Agenda
- Resources
Week 10: November 30
- Communicating methods, results, and implications
- translating for non-data scientists
- Assignments due
- Agenda
- Resources
- one
Week 11: December 7
- Future of human centered data science
- case studies from research, industry, and policy; final presentations
- Assignments due
- Agenda
- Resources
- one
Week 12: December 14
FINALS WEEK - NO CLASS - ALL ASSIGNMENTS DUE BY TBA
