HCDS (Fall 2017)/Schedule



Week 1: September 28
Day 1 plan


 * Assignments due
 * fill out the pre-course survey


 * Agenda


 * 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.
 * Read: Kling, Rob and Star, Susan Leigh. Human Centered Systems in the Perspective of Organizational and Social Informatics. 1997.


 * Homework assigned
 * Reading reflection


 * Resources
 * 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
Day 2 plan


 * 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


 * Readings assigned


 * Homework assigned
 * Reading reflection
 * A1: privacy and anonymity


 * 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.
 * Zetter, Kim. Arvind Narayanan Isn’t Anonymous, and Neither Are You. WIRED, 2012.
 * Fiesler, Casey. Law & Ethics of Scraping: What HiQ v LinkedIn Could Mean for Researchers Violating TOS. Medium, 2017.
 * EU General Data Protection Regulation.
 * Green, Matthew. What is Differential Privacy? A Few Thoughts on Cryptographic Engineering, 2016.
 * Hsu, Danny. Techniques to Anonymize Human Data. Data Sift, 2015.

Week 3: October 12
Day 3 plan


 * Data provenance, preparation, and reproducibility: data curation, preservation, documentation, and archiving; best practices for open scientific research


 * Assignments due
 * Week 2 reading reflection
 * A1: Privacy and anonymity


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection
 * A2: Data curation


 * Resources
 * Press, Gil. Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. Forbes, 2016.
 * Christensen, Garret. Manual of Best Practices in Transparent Social Science Research. 2016.
 * Chapman et al. Cross Industry Standard Process for Data Mining. IBM, 2000.

Week 4: October 19
Day 4 plan


 * Study design: understanding your data; framing research questions; planning your study


 * Assignments due
 * Reading reflection
 * A2: Data curation


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection
 * A3: Measuring bias in data


 * Resources

Week 5: October 26
Day 5 plan


 * Machine learning: ethical AI, algorithmic transparency, societal implications of machine learning


 * Assignments due
 * Reading reflection
 * A3: Measuring bias in data


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection
 * A4: Auditing algorithms


 * Resources

Week 6: November 2
Day 6 plan


 * Mixed-methods research: Big data vs thick data; qualitative research in data science 


 * Assignments due
 * Reading reflection
 * A4: Auditing algorithms


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection
 * A5: Final project plan


 * Resources

Week 7: November 9
Day 7 plan


 * Human computation: ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation


 * Assignments due
 * Reading reflection
 * A5: Final project plan


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection
 * A6: Self-ethnography


 * Resources
 * go here

Week 8: November 16
Day 8 plan


 * User experience and big data: prototyping and user testing; benchmarking and iterative evaluation; UI design for data science


 * Assignments due
 * Reading reflection


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection


 * Resources

Week 9: November 23
Day 9 plan


 * Human-centered data science in the wild: community data science; data science for social good


 * Assignments due
 * Reading reflection
 * A6: Crowdwork self-ethnography


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection


 * Resources

Week 10: November 30
Day 10 plan


 * Communicating methods, results, and implications: translating for non-data scientists ''


 * Assignments due
 * Reading reflection


 * Agenda


 * Readings assigned


 * Homework assigned
 * Reading reflection
 * A7: Final presentation


 * Resources
 * one

Week 11: December 7
Day 11 plan


 * Future of human centered data science: case studies from research, industry, and policy; final presentations


 * Assignments due
 * Reading reflection
 * Final presentation


 * Agenda


 * Readings assigned
 * none!


 * Homework assigned
 * none!


 * Resources
 * one

Week 12: Finals Week

 * NO CLASS
 * FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 10
 * LATE PROJECT SUBMISSIONS NOT ACCEPTED.