Human Centered Data Science (Fall 2018)

Human Centered Data Science

DATA 512 - UW Interdisciplinary Data Science Masters Program - Thursdays 5:00-9:50pm in ART 003.

Principal instructor

Jonathan T. Morgan

Co-instructor

Os Keyes

Course Website

This wiki page is the canonical information resource for DATA512. All other course-related information will be linked on this page. We will use the Canvas site for announcements, file hosting, and submitting reading reflections, graded in-class assignments, and other programming and writing assignments. We will use Slack for Q&A and general discussion.

Course Description

Fundamental principles of data science and its human implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness and accountability; data provenance, curation, preservation, and reproducibility; user experience design and research for big data; human computation; data communication and visualization; and societal impacts of data science.

Overview and learning objectives

The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments. Students will work in small groups for in-class activities, and work independently on all class project deliverables and homework assignments. Instructors will provide guidance in completing the exercises each week.

By the end of this course, students will be able to:

• Analyze large and complex data effectively and ethically with an understanding of human, societal, and socio-technical contexts.
• Take into account the ethical, social, and legal considerations when designing algorithms and performing large-scale data analysis.
• Combine quantitative and qualitative research methods to generate critical insights into human behavior.
• Discuss and evaluate ethical, social and legal trade-offs of different data analysis, testing, curation, and sharing methods.

Course resources

All pages and files on this wiki that are related to the Fall 2018 edition of DATA 512: Human-Centered Data Science are listed in Category:HCDS (Fall 2018).

Office hours

• Os Keyes: Monday (5pm-7pm) and Wednesday (5-7pm), Sieg 431, and by request.
• Jonathan Morgan: Google Meet, by request

Datasets

For some examples of datasets you could use for your final project, see Human Centered Data Science/Datasets.

Lecture slides

Slides for weekly lectures will be available in PDF form on this wiki, generally within 24 hours of each course session

• Week 1 slides
• Week 2 slides
• Week 3 slides
• Week 4 slides
• Week 5 slides
• Week 6 slides
• Week 7 slides
• Week 8 slides
• Week 10 slides

Schedule

Direct link: Human Centered Data Science (Fall 2018)/Schedule

Course schedule (click to expand)

Week 1: September 27

Day 1 plan

Day 1 slides

Introduction to Human Centered Data Science

What is data science? What is human centered? What is human centered data science?

Assignments due

• fill out the pre-course survey
• 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. (no reading reflection required)

Agenda

• Syllabus review
• Pre-course survey results
• What do we mean by data science?
• What do we mean by human centered?
• How does human centered design relate to data science?
• Looking ahead: Week 2 assignments and topics

Readings assigned

• Read: Barocas, Solan and Nissenbaum, Helen. Big Data's End Run around Anonymity and Consent. In Privacy, Big Data, and the Public Good. 2014.

Homework assigned

• Reading reflection

Resources

• Aragon, C. et al. (2016). Developing a Research Agenda for Human-Centered Data Science. Human Centered Data Science workshop, CSCW 2016.
• Kling, Rob and Star, Susan Leigh. Human Centered Systems in the Perspective of Organizational and Social Informatics. 1997.
• Harford, T. (2014). Big data: A big mistake? Significance, 11(5), 14–19.

---

Week 2: October 4

Day 2 plan

Ethical considerations

privacy, informed consent and user treatment

Assignments due

• Week 1 reading reflection

Agenda

• Intro to assignment 1: Data Curation
• A brief history of research ethics
• Guest lecture: Javier Salido and Mark van Hollebeke, "A Practitioners View of Privacy & Data Protection"
• Guest lecture: Javier Salido, "Differential Privacy"
• Contextual Integrity in data science
• Week 2 reading reflection

Readings assigned

• Read: boyd, danah and Crawford, Kate, Six Provocations for Big Data (September 21, 2011). A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011. Available at SSRN: https://ssrn.com/abstract=1926431 or http://dx.doi.org/10.2139/ssrn.1926431

Homework assigned

• Reading reflection
• A1: Data curation

Resources

• Nissenbaum, Helen, Privacy as Contextual Integrity
• 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.
• Bethan Cantrell, Javier Salido, and Mark Van Hollebeke (2016). Industry needs to embrace data ethics: Here's how it could be done. Workshop on Data and Algorithmic Transparency (DAT'16). http://datworkshop.org/
• Javier Salido (2012). Differential Privacy for Everyone. Microsoft Corporation Whitepaper.
• Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
• 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.
• Zetter, Kim. Arvind Narayanan Isn’t Anonymous, and Neither Are You. WIRED, 2012.
• Gray, Mary. When Science, Customer Service, and Human Subjects Research Collide. Now What? Culture Digitally, 2014.
• Tene, Omer and Polonetsky, Jules. Privacy in the Age of Big Data. Stanford Law Review, 2012.
• Dwork, Cynthia. Differential Privacy: A survey of results. Theory and Applications of Models of Computation , 2008.
• Hsu, Danny. Techniques to Anonymize Human Data. Data Sift, 2015.
• Metcalf, Jacob. Twelve principles of data ethics. Ethical Resolve, 2016.

---

Week 3: October 11

Day 3 plan

Day 3 slides

Reproducibility and Accountability

data curation, preservation, documentation, and archiving; best practices for open scientific research

Assignments due

• Week 2 reading reflection

Agenda

• Six Provocations for Big Data: Review & Reflections
• A primer on copyright, licensing, and hosting for code and data
• Introduction to replicability, reproducibility, and open research
• Reproducibility case study: fivethirtyeight.com
• Group activity: assessing reproducibility in data journalism
• Overview of Assignment 1: Data curation

Readings assigned

• Read: Duarte, N., Llanso, E., & Loup, A. (2018). Mixed Messages? The Limits of Automated Social Media Content Analysis. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 106.

Homework assigned

• Reading reflection

Resources

• Hickey, Walt. The Dollars and Cents Case Against Hollywood's Exclusion of Women. FiveThirtyEight, 2014.
• Keegan, Brian. The Need for Openness in Data Journalism. 2014.
• Hickey, Walt. The Bechdel Test: Checking Our Work. FiveThirtyEight, 2014.
• J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), Altmetrics: A manifesto, 26 October 2010.

Assignment 1 Data curation resources

• Chapter 2 "Assessing Reproducibility" and Chapter 3 "The Basic Reproducible Workflow Template" from The Practice of Reproducible Research University of California Press, 2018.
• sample code for API calls (view the notebook, download the notebook).
• See the datasets page for examples of well-documented and not-so-well documented open datasets.

---

Week 4: October 18

Day 4 plan

Day 4 slides

Interrogating datasets

causes and consequences of bias in data; best practices for selecting, describing, and implementing training data

Assignments due

• Reading reflection
• A1: Data curation

Agenda

• Final project: Goal, timeline, and deliverables.
• Overview of assignment 2: Bias in data
• Reading reflections review
• Sources of bias in datasets
• Introduction to assignment 2: Bias in data
• Sources of bias in data collection and processing
• In-class exercise: assessing bias in training data

Readings assigned (Read both, reflect on one)

• Wang, Tricia. Why Big Data Needs Thick Data. Ethnography Matters, 2016.
• Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao (Ken) Wang, and Brent Hecht. 2015. Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '15)

Homework assigned

• Reading reflection
• A2: Bias in data

Resources

• Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016). Social data: Biases, methodological pitfalls, and ethical boundaries.
• Brian N Larson. 2017. Gender as a Variable in Natural-Language Processing: Ethical Considerations. EthNLP, 3: 30–40.
• Bender, E. M., & Friedman, B. (2018). Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science. To appear in Transactions of the ACL.
• Isaac L. Johnson, Yilun Lin, Toby Jia-Jun Li, Andrew Hall, Aaron Halfaker, Johannes Schöning, and Brent Hecht. 2016. Not at Home on the Range: Peer Production and the Urban/Rural Divide. CHI '16. DOI: https://doi.org/10.1145/2858036.2858123
• Leo Graiden Stewart, Ahmer Arif, A. Conrad Nied, Emma S. Spiro, and Kate Starbird. 2017. Drawing the Lines of Contention: Networked Frame Contests Within #BlackLivesMatter Discourse. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 96 (December 2017), 23 pages. DOI: https://doi.org/10.1145/3134920
• Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. No country for old members: user lifecycle and linguistic change in online communities. In Proceedings of the 22nd international conference on World Wide Web (WWW '13). ACM, New York, NY, USA, 307-318. DOI: https://doi.org/10.1145/2488388.2488416

---

Week 5: October 25

Day 5 plan

Day 5 slides

Introduction to mixed-methods research

Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing

Assignments due

• Reading reflection

Agenda

• Assignment 1 review & reflection
• Week 4 reading reflection discussion
• Survey of qualitative research methods
• Mixed-methods case study #1: The Wikipedia Gender Gap: causes & consequences
• In-class activity: Automated Gender Recognition scenarios
• Introduction to ethnography
• Ethnographic research case study: Structured data on Wikimedia Commons
• Introduction to crowdwork
• Overview of Assignment 3: Crowdwork ethnography

Readings assigned (Read both, reflect on one)

• Donovan, J., Caplan, R., Matthews, J., & Hanson, L. (2018). Algorithmic accountability: A primer. Data & Society, 501(c).

Homework assigned

• Reading reflection
• A3: Crowdwork ethnography

Qualitative research methods resources

• Ladner, S. (2016). Practical ethnography: A guide to doing ethnography in the private sector. Routledge.
• Spradley, J. P. (2016). The ethnographic interview. Waveland Press.
• Eriksson, P., & Kovalainen, A. (2015). Ch 12: Ethnographic Research. In Qualitative methods in business research: A practical guide to social research. Sage.
• Usability.gov, System usability scale.
• Nielsen, Jakob (2000). Why you only need to test with five users. nngroup.com.

Wikipedia gender gap research resources

• Hill, B. M., & Shaw, A. (2013). [journals.plos.org/plosone/article?id=10.1371/journal.pone.0065782 The Wikipedia gender gap revisited: Characterizing survey response bias with propensity score estimation]. PloS one, 8(6), e65782
• Shyong (Tony) K. Lam, Anuradha Uduwage, Zhenhua Dong, Shilad Sen, David R. Musicant, Loren Terveen, and John Riedl. 2011. WP:clubhouse?: an exploration of Wikipedia's gender imbalance. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym '11). ACM, New York, NY, USA, 1-10. DOI=http://dx.doi.org/10.1145/2038558.2038560
• Maximillian Klein. Gender by Wikipedia Language. Wikidata Human Gender Indicators (WHGI), 2017.
• Source: Wagner, C., Garcia, D., Jadidi, M., & Strohmaier, M. (2015, April). It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia. In ICWSM (pp. 454-463).

• Benjamin Collier and Julia Bear. Conflict, criticism, or confidence: an empirical examination of the gender gap in wikipedia contributions. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12). DOI: https://doi.org/10.1145/2145204.2145265
• Christina Shane-Simpson, Kristen Gillespie-Lynch, Examining potential mechanisms underlying the Wikipedia gender gap through a collaborative editing task, In Computers in Human Behavior, Volume 66, 2017, https://doi.org/10.1016/j.chb.2016.09.043. (PDF on Canvas)
• Amanda Menking and Ingrid Erickson. 2015. The Heart Work of Wikipedia: Gendered, Emotional Labor in the World's Largest Online Encyclopedia. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). https://doi.org/10.1145/2702123.2702514

Crowdwork research resources

• WeArDynamo contributors. How to be a good requester and Guidelines for Academic Requesters. Wearedynamo.org

---

Week 6: November 1

Day 6 plan

Day 6 slides

Interrogating algorithms

algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits

Assignments due

• Reading reflection
• A2: Bias in data

Agenda

• Reading reflections
• Ethical implications of crowdwork
• Algorithmic transparency, interpretability, and accountability
• Auditing algorithms
• In-class activity: auditing the Perspective API

Readings assigned

• Astrid Mager. 2012. Algorithmic ideology: How capitalist society shapes search engines. Information, Communication & Society 15, 5: 769–787. http://doi.org/10.1080/1369118X.2012.676056

Homework assigned

• Reading reflection

Resources

• Christian Sandvig, Kevin Hamilton, Karrie Karahalios, Cedric Langbort (2014/05/22) Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms. Paper presented to "Data and Discrimination: Converting Critical Concerns into Productive Inquiry," a preconference at the 64th Annual Meeting of the International Communication Association. May 22, 2014; Seattle, WA, USA.
• Shahriari, K., & Shahriari, M. (2017). IEEE standard review - Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems. Institute of Electrical and Electronics Engineers
• ACM US Policy Council Statement on Algorithmic Transparency and Accountability. January 2017.
• Asilomar AI Principles. Future of Life Institute, 2017.
• Diakopoulos, N., Friedler, S., Arenas, M., Barocas, S., Hay, M., Howe, B., … Zevenbergen, B. (2018). Principles for Accountable Algorithms and a Social Impact Statement for Algorithms. Fatml.Org 2018.
• Friedman, B., & Nissenbaum, H. (1996). Bias in Computer Systems. ACM Trans. Inf. Syst., 14(3), 330–347.
• Diakopoulos, N. (2014). Algorithmic accountability reporting: On the investigation of black boxes. Tow Center for Digital Journalism, 1–33. https://doi.org/10.1002/ejoc.201200111
• Nate Matias, 2017. How Anyone Can Audit Facebook's Newsfeed. Medium.com
• Hill, Kashmir. Facebook figured out my family secrets, and it won't tell me how. Engadget, 2017.
• Blue, Violet. Google’s comment-ranking system will be a hit with the alt-right. Engadget, 2017.
• Ingold, David and Soper, Spencer. Amazon Doesn’t Consider the Race of Its Customers. Should It?. Bloomberg, 2016.
• Paul Lamere. How good is Google's Instant Mix?. Music Machinery, 2011.
• Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. Machine Bias: Risk Assessment in Criminal Sentencing. Propublica, May 2018.
• Google's Perspective API

---

Week 7: November 8

Day 7 plan

Day 7 slides

Critical approaches to data science

power, data, and society; ethics of crowdwork

Assignments due

• Reading reflection
• A3: Crowdwork ethnography

Agenda

• Guest lecture: Rochelle LaPlante

Readings assigned (read both, reflect on one)

• Read: Baumer, E. P. S. (2017). Toward human-centered algorithm design. Big Data & Society.
• Read: Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine, 35(4), 105.

Homework assigned

• Reading reflection
• A4: Final project plan

Resources

• Neff, G., Tanweer, A., Fiore-Gartland, B., & Osburn, L. (2017). Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big Data, 5(2), 85–97. https://doi.org/10.1089/big.2016.0050
• Lilly C. Irani and M. Six Silberman. 2013. Turkopticon: interrupting worker invisibility in amazon mechanical turk. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). DOI: https://doi.org/10.1145/2470654.2470742
• Bivens, R. and Haimson, O.L. 2016. 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.
• Schlesinger, A. et al. 2017. 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.

---

Week 8: November 15

Day 8 plan

Day 8 slides

Human-centered algorithm design

algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems

Assignments due

• Reading reflection

Agenda

• Final project overview & examples
• Guest Lecture: Kelly Franznick, Blink UX
• Reading reflections
• Human-centered algorithm design

• design process
• user-driven evaluation
• design patterns & anti-patterns

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.

Homework assigned

• Reading reflection

Resources

• Ethical OS Toolkit and Risk Mitigation Checklist. EthicalOS.org.
• Morgan, J. 2016. Evaluating Related Articles recommendations. Wikimedia Research.
• Morgan, J. 2017. Comparing most read and trending edits for the top articles feature. Wikimedia Research.
• Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14).
• Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Making recommendations better: an analytic model for human-recommender interaction. In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06).
• Sean M. McNee, Nishikant Kapoor, and Joseph A. Konstan. 2006. 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).
• Michael D. Ekstrand and Martijn C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16).
• Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
• Anderson, Carl. The role of model interpretability in data science. Medium, 2016.

---

Week 9: November 22 (No Class Session)

Day 9 plan

Data science for social good

Community-based and participatory approaches to data science; Using data science for society's benefit

Assignments due

• Reading reflection
• A4: Final project plan

Agenda

• Reading reflections discussion
• Feedback on Final Project Plans
• Guest lecture: Steven Drucker (Microsoft Research)
• UI patterns & UX considerations for ML/data-driven applications
• Final project presentation: what to expect
• In-class activity: final project peer review

Readings assigned

• Berney, Rachel, Bernease Herman, Gundula Proksch, Hillary Dawkins, Jacob Kovacs, Yahui Ma, Jacob Rich, and Amanda Tan. 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).

Homework assigned

• Reading reflection

Resources

• Daniela Aiello, Lisa Bates, et al. Eviction Lab Misses the Mark, ShelterForce, August 2018.

---

Week 10: November 29

Day 10 plan

Day 10 slides

User experience and big data

Design considerations for machine learning applications; human centered data visualization; data storytelling

Assignments due

• Reading reflection

Agenda

• Reading reflections discussion
• Feedback on Final Project Plans
• Guest lecture: Steven Drucker (Microsoft Research)
• UI patterns & UX considerations for ML/data-driven applications
• Final project presentation: what to expect
• In-class activity: final project peer review

Readings assigned

• NONE

Homework assigned

• A5: Final presentation

Resources

• Fabien Girardin. Experience design in the machine learning era. Medium, 2016.
• Xavier Amatriain and Justin Basilico. Netflix Recommendations: Beyond the 5 stars. Netflix Tech Blog, 2012.
• Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
• Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. 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
• Patrick Austin, Facebook, Google, and Microsoft Use Design to Trick You Into Handing Over Your Data, New Report Warns. Gizmodo, 6/18/2018
• Brown, A., Tuor, A., Hutchinson, B., & Nichols, N. (2018). [Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. arXiv preprint arXiv:1803.04967.
• Cremonesi, P., Elahi, M., & Garzotto, F. (2017). User interface patterns in recommendation-empowered content intensive multimedia applications. Multimedia Tools and Applications, 76(4), 5275-5309.
• Marilynn Larkin, How to give a dynamic scientific presentation. Elsevier Connect, 2015.
• Megan Risdal, Communicating data science: a guide to presenting your work. Kaggle blog, 2016.
• Megan Risdal, Communicating data science: Why and how to visualize information. Kaggle blog, 2016.
• Megan Risdal, Communicating data science: an interview with a storytelling expert. Kaggle blog, 2016.
• Brent Dykes, Data Storytelling: The Essential Data Science Skill Everyone Needs. Forbes, 2016.

---

Week 11: December 6

Day 11 plan

Final presentations

course wrap up, presentation of student projects

Assignments due

• A5: Final presentation

Agenda

• Student final presentations
• Course wrap-up

Readings assigned

• none!

Homework assigned

• A6: Final project report (due 12/9 by 11:59pm)

Resources

• one

---

Week 12: Finals Week (No Class Session)

• NO CLASS
• A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 9
• LATE PROJECT SUBMISSIONS NOT ACCEPTED.

Assignments

For details on individual assignments, see Human Centered Data Science (Fall 2018)/Assignments

Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete.

Unless otherwise noted, all assignments are due before 5pm on the following week's class.

Unless otherwise noted, all assignments are individual assignments.

Assignment timeline

Assignments due every week

• In-class activities - 2 points (weekly): In-class activity output posted to Canvas (group or individual) within 24 hours of class session.
• Reading reflections - 2 points (weekly): Reading reflections posted to Canvas (individual) before following class session.

Scheduled assignments

• A1 - 5 points (due 10/18): Data curation (programming/analysis)
• A2 - 10 points (due 11/1): Sources of bias in data (programming/analysis)
• A3 - 10 points (due 11/8): Crowdwork Ethnography (written)
• A4 - 10 points (due 11/22): Final project plan (written)
• A5 - 10 points (due 12/6): Final project presentation (oral, slides)
• A6 - 15 points (due 12/9): Final project report (programming/analysis, written)

more information...

Policies

The following general policies apply to this course.

Respect

Students are expected to treat each other, and the instructors, with respect. Students are prohibited from engaging in any kind of harassment or derogatory behavior, which includes offensive verbal comments or imagery related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, or religion. In addition, students should not engage in any form of inappropriate physical contact or unwelcome sexual attention, and should respect each others’ right to privacy in regards to their personal life. In the event that you feel you (or another student) have been subject to a violation of this policy, please reach out to the instructors in whichever form you prefer.

The instructors are committed to providing a safe and healthy learning environment for students. As part of this, students are asked not to wear any clothing, jewelry, or any related medium for symbolic expression which depicts an indigenous person or cultural expression re­appropriated as a mascot, logo, or caricature. These include, but are not limited to, iconography associated with the following sports teams:

1. Chicago Blackhawks
2. Washington Redskins
3. Cleveland Indians
4. Atlanta Braves

Attendance and participation

Students are expected to attend class regularly. If you run into a conflict that requires you to be absent (for example, medical issues) feel free to reach out to the instructors. We will do our best to ensure that you don’t miss out, and treat your information as confidential.

If you miss class session, please do not ask the professor or TA what you missed during class; check the website or ask a classmate (best bet: use Slack). Graded in-class activities cannot be made up if you miss a class session.

Grading

Active participation in class activities is one of the requirements of the course. You are expected to engage in group activities, class discussions, interactions with your peers, and constructive critiques as part of the course work. This will help you hone your communication and other professional skills. Correspondingly, working in groups or on teams is an essential part of all data science disciplines. As part of this course, you will be asked to provide feedback of your peers' work.

Individual assignments will have specific requirements listed on the assignment sheet, which the instructor will make available on the day the homework is assigned. If you have questions about how your assignment was graded, please see the TA or instructor.

Assignments and coursework

Grades will be determined as follows:

• 20% in-class work
• 20% reading reflections
• 60% assignments

You are expected to produce work in all of the assignments that reflects the highest standards of professionalism. For written documents, this means proper spelling, grammar, and formatting.

Late assignments will not be accepted; if your assignment is late, you will receive a zero score. Again, if you run into an issue that necessitates an extension, please reach out. Final projects cannot be turned in late and are not eligible for any extension whatsoever.

Academic integrity and plagiarism

Students are expected to adhere to rules around academic integrity. Simply stated, academic integrity means that you are to do your own work in all of your classes, unless collaboration is part of an assignment as defined in the course. In any case, you must be responsible for citing and acknowledging outside sources of ideas in work you submit.

Please be aware of the HCDE Department's and the UW's policies on plagiarism and academic misconduct: HCDE Academic Conduct policy. This policy will be strictly enforced.

Other academic integrity resources:

• Center for Teaching and Learning: Cheating or Plagiarism
• University of Washington Student Academic Responsibility (PDF)

Disability and accommodations

As part of ensuring that the class is as accessible as possible, the instructors are entirely comfortable with you using whatever form of note-taking method or recording is most comfortable to you, including laptops and audio recording devices. The instructors will do their best to ensure that all slides and scripts/notes are immediately available online after a lecture has concluded. In addition, if asked ahead of time we can try to record the audio of individial lectures for students who have learning differences that make audiovisual notes preferable to written ones.

If you require additional accommodations, please contact Disabled Student Services: 448 Schmitz, 206-543-8924 (V/TTY). If you have a letter from DSS indicating that you have a disability which requires academic accommodations, please present the letter to the instructors so we can discuss the accommodations you might need in the class. If you have any questions about this policy, reach out to the instructors directly.

For more information on disability accommodations, and how to apply for one, please review UW's Disability Resources for Students.

Disclaimer

This syllabus and all associated assignments, requirements, deadlines and procedures are subject to change.