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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.
- Various authors. Position papers from 2016 CSCW Human Centered Data Science Workshop. 2016.
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
- Hill, Kashmir. Facebook Manipulated 689,003 Users' Emotions For Science. Forbes, 2014.
- Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock Experimental evidence of massive-scale emotional contagion through social networks. PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
- Barbaro, Michael and Zeller, Tom. A Face Is Exposed for AOL Searcher No. 4417749. New York Times, 2008.
- Gray, Mary. When Science, Customer Service, and Human Subjects Research Collide. Now What? Culture Digitally, 2014.
- Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
- Tene, Omer and Polonetsky, Jules. Privacy in the Age of Big Data. Stanford Law Review, 2012.
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report. U.S. Department of Health and Human Services, 1979.
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
