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| === Week 1: September 28 === | | === Week 1: September 28 === |
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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]] |
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| [[:File:HCDS Week 6 slides.pdf|Day 6 slides]]
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| ;Mixed-methods research: ''Big data vs thick data; qualitative research in data science '' | | ;Mixed-methods research: ''Big data vs thick data; qualitative research in data science '' |
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| [[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]] | | [[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]] |
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| [[:File:HCDS Week 8 slides.pdf|Day 8 slides]]
| | ;User experience and big data: ''prototyping and user testing; benchmarking and iterative evaluation; UI design for data science'' |
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| ;User experience and big data: ''user-centered design and evaluation of recommender systems; UI design for data science, collaborative visual analytics'' | |
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| ;Readings assigned | | ;Readings assigned |
| *Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. ''[https://md.ekstrandom.net/research/pubs/listcmp/listcmp.pdf User perception of differences in recommender algorithms].'' In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14). ACM, New York, NY, USA, 161-168. DOI: https://doi.org/10.1145/2645710.2645737
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| * Chen, N., Brooks, M., Kocielnik, R., Hong, R., Smith, J., Lin, S., Qu, Z., Aragon, C. ''[https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1254&context=hicss-50 Lariat: A visual analytics tool for social media researchers to explore Twitter datasets].'' Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS), Data Analytics and Data Mining for Social Media Minitrack (2017)
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| ;Homework assigned | | ;Homework assigned |
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| ;Resources | | ;Resources |
| * Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. ''[http://files.grouplens.org/papers/mcnee-chi06-hri.pdf Making recommendations better: an analytic model for human-recommender interaction].'' In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06). ACM, New York, NY, USA, 1103-1108. DOI=http://dx.doi.org/10.1145/1125451.1125660
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| * Kevin Crowston and the Gravity Spy Team. 2017. ''[https://crowston.syr.edu/sites/crowston.syr.edu/files/cpa137-crowstonA.pdf Gravity Spy: Humans, Machines and The Future of Citizen Science].'' In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17 Companion). ACM, New York, NY, USA, 163-166. DOI: https://doi.org/10.1145/3022198.3026329
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| * Michael D. Ekstrand and Martijn C. Willemsen. 2016. ''[https://md.ekstrandom.net/research/pubs/behaviorism/BehaviorismIsNotEnough.pdf Behaviorism is Not Enough: Better Recommendations through Listening to Users].'' In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 221-224. DOI: https://doi.org/10.1145/2959100.2959179
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| * Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017. | | * Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017. |
| * Xavier Amatriain and Justin Basilico. ''[https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 Netflix Recommendations: Beyond the 5 stars].'' Netflix Tech Blog, 2012. | | * Xavier Amatriain and Justin Basilico. ''[https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 Netflix Recommendations: Beyond the 5 stars].'' Netflix Tech Blog, 2012. |
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| ;Readings assigned | | ;Readings assigned |
| * Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). Democratizing Data Science: The Community Data Science Workshops and Classes. In N. Jullien, S. A. Matei, & S. P. Goggins (Eds.), ''Big Data Factories: Scientific Collaborative approaches for virtual community data collection, repurposing, recombining, and dissemination''. New York, New York: Springer Nature. [[https://mako.cc/academic/hill_etal-cdsw_chapter-DRAFT.pdf Preprint/Draft PDF]]
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| * Bivens, R. and Haimson, O.L. 2016. ''[http://journals.sagepub.com/doi/pdf/10.1177/2056305116672486 Baking Gender Into Social Media Design: How Platforms Shape Categories for Users and Advertisers]''. Social Media + Society. 2, 4 (2016), 205630511667248. DOI:https://doi.org/10.1177/2056305116672486.
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| * Schlesinger, A. et al. 2017. ''[http://arischlesinger.com/wp-content/uploads/2017/03/chi2017-schlesinger-intersectionality.pdf Intersectional HCI: Engaging Identity through Gender, Race, and Class].'' Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17. (2017), 5412–5427. DOI:https://doi.org/10.1145/3025453.3025766.
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| ;Homework assigned | | ;Homework assigned |
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| ;Resources | | ;Resources |
| * Berney, Rachel, Bernease Herman, Gundula Proksch, Hillary Dawkins, Jacob Kovacs, Yahui Ma, Jacob Rich, and Amanda Tan. ''[https://dssg.uchicago.edu/wp-content/uploads/2017/09/berney.pdf Visualizing Equity: A Data Science for Social Good Tool and Model for Seattle].'' Data Science for Social Good Conference, September 2017, Chicago, Illinois USA (2017).
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| * Sayamindu Dasgupta and Benjamin Mako Hill. ''[https://cscw2016hcds.files.wordpress.com/2015/10/dasgupta_hcds2016.pdf Learning With Data: Designing for Community Introspection and Exploration].'' Position paper for Developing a Research Agenda for Human-Centered Data Science (a CSCW 2016 workshop).
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| <br/> | | <br/> |
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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]] |
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| [[:File:HCDS Week 10 slides.pdf|Day 10 slides]]
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| ;Communicating methods, results, and implications: translating for non-data scientists '' | | ;Communicating methods, results, and implications: translating for non-data scientists '' |
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| ;Readings assigned | | ;Readings assigned |
| * Megan Risdal, ''[http://blog.kaggle.com/2016/06/29/communicating-data-science-a-guide-to-presenting-your-work/ Communicating data science: a guide to presenting your work].'' Kaggle blog, 2016.
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| * Marilynn Larkin, ''[https://www.elsevier.com/connect/how-to-give-a-dynamic-scientific-presentation How to give a dynamic scientific presentation].'' Elsevier Connect, 2015.
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| ;Homework assigned | | ;Homework assigned |
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| ;Resources | | ;Resources |
| * Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. ''[https://pure.tue.nl/ws/files/3484177/724656348730405.pdf Explaining the user experience of recommender systems].'' User Modeling and User-Adapted Interaction 22, 4-5 (October 2012), 441-504. DOI=http://dx.doi.org/10.1007/s11257-011-9118-4 | | * ''one'' |
| * Sean M. McNee, Nishikant Kapoor, and Joseph A. Konstan. 2006. ''[http://files.grouplens.org/papers/p171-mcnee.pdf Don't look stupid: avoiding pitfalls when recommending research papers].'' In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work (CSCW '06). ACM, New York, NY, USA, 171-180. DOI=http://dx.doi.org/10.1145/1180875.1180903
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| * Megan Risdal, ''[http://blog.kaggle.com/2016/08/10/communicating-data-science-why-and-some-of-the-how-to-visualize-information/ Communicating data science: Why and how to visualize information].'' Kaggle blog, 2016.
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| * Megan Risdal, ''[http://blog.kaggle.com/2016/06/13/communicating-data-science-an-interview-with-a-storytelling-expert-tyler-byers/ Communicating data science: an interview with a storytelling expert].'' Kaggle blog, 2016.
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| * Richard Garber, ''[https://joyfulpublicspeaking.blogspot.com/2010/08/power-of-brief-speeches-world-war-i-and.html Power of brief speeches: World War I and the Four Minute Men].'' Joyful Public Speaking, 2010.
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| * Brent Dykes, ''[https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/ Data Storytelling: The Essential Data Science Skill Everyone Needs].'' Forbes, 2016.
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| [[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]] | | [[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]] |
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| ;Future of human centered data science: course wrap up, final presentations'' | | ;Future of human centered data science: ''case studies from research, industry, and policy; final presentations'' |
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