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;Readings assigned | ;Readings assigned | ||
* Watch: [https://www.hcde.washington.edu/seminar-series/aragon ''Why Humans Should Care About Data Science''] (Cecilia Aragon, 2016 HCDE Seminar Series) | * Watch: [https://www.hcde.washington.edu/seminar-series/aragon ''Why Humans Should Care About Data Science''] (Cecilia Aragon, 2016 HCDE Seminar Series) | ||
* Read: Aragon, C. et al. (2016). [https://cscw2016hcds.files.wordpress.com/2015/10/cscw_2016_human-centered-data-science_workshop.pdf ''Developing a Research Agenda for Human-Centered Data Science''] Human Centered Data Science workshop | * Read: Aragon, C. et al. (2016). [https://cscw2016hcds.files.wordpress.com/2015/10/cscw_2016_human-centered-data-science_workshop.pdf ''Developing a Research Agenda for Human-Centered Data Science.''] Human Centered Data Science workshop, CSCW 2016. | ||
* Read: Provost, Foster, and Tom Fawcett. [http://online.liebertpub.com/doi/pdf/10.1089/big.2013.1508 ''Data science and its relationship to big data and data-driven decision making.''] Big Data 1.1 (2013): 51-59. | * Read: Provost, Foster, and Tom Fawcett. [http://online.liebertpub.com/doi/pdf/10.1089/big.2013.1508 ''Data science and its relationship to big data and data-driven decision making.''] Big Data 1.1 (2013): 51-59. | ||
Revision as of 18:20, 24 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.
- Read: Provost, Foster, and Tom Fawcett. Data science and its relationship to big data and data-driven decision making. Big Data 1.1 (2013): 51-59.
- Homework assigned
- Reading reflection
- Resources
- Kling, Rob and Star, Susan Leigh. Human Centered Systems in the Perspective of Organizational and Social Informatics. 1997.
- Ideo.org The Field Guide to Human-Centered Design. 2015.
- Faraway, Julian. The Decline and Fall of Statistics. Faraway Statistics, 2015.
- Press, Gil. Data Science: What's The Half-Life Of A Buzzword? Forbes, 2013.
- Bloor, Robin. A Data Science Rant. Inside Analysis, 2013.
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
- Week 1 reading reflection
- Agenda
- Informed consent in the age of Data Science
- Privacy
- User expectations
- Inferred information
- Correlation
- Anonymisation strategies
- Readings assigned
- Homework assigned
- 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
- How Wikipedia works (and how it doesn't)
- guest speaker: Morten Warnke-Wang, Wikimedia Foundation
- Sources of bias in data science research
- Sources of bias in Wikipedia data
- Homework
- Resources
Week 5: October 26
- Machine learning
- ethical AI, algorithmic transparency, societal implications of machine learning
- Assignments due
- Agenda
- Social implications of machine learning
- Consequences of algorithmic bias
- Sources of algorithmic bias
- Addressing algorithmic bias
- Auditing algorithms
- Homework
- Resources
Week 6: November 2
- Mixed-methods research
- Big data vs thick data; qualitative research in data science
- Assignments due
- Agenda
- Guest speakers: Aaron Halfaker, Caroline Sinders (Wikimedia Foundation)
- Mixed methods research
- Ethnographic methods in data science
- Project plan brainstorm/Q&A session
- Homework
- Resources
Week 7: November 9
- Human computation
- ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation
- Assignments due
- Agenda
- the role of qualitative research in human centered data science
- scaling qualitative research through crowdsourcing
- types of crowdwork
- ethical and practical considerations for crowdwork
- Introduction to assignment 4: Mechanical Turk ethnography
- 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
- HCD process in the design of data-driven applications
- understanding user needs, user intent, and context of use in recommender system design
- trust, empowerment, and seamful design
- HCD in data analysis and visualization
- final project lightning feedback sessions
- Resources
Week 9: November 23
- Human-centered data science in the wild
- community data science; data science for social good
- Agenda
- NO CLASS - work on your own
- Resources
Week 10: November 30
- Communicating methods, results, and implications
- translating for non-data scientists
- Assignments due
- Agenda
- communicating about your research effectively and honestly to different audiences
- publishing your research openly
- disseminating your research
- final project workshop
- Resources
- one
Week 11: December 7
- Future of human centered data science
- case studies from research, industry, and policy; final presentations
- Assignments due
- Agenda
- future directions of of human centered data science
- final presentations
- Resources
- one
Week 12: December 14
FINALS WEEK - NO CLASS - ALL ASSIGNMENTS DUE BY TBA