User:Groceryheist/drafts/Data Science Syllabus


 * Data Science and Organizational Communication:
 * Principal instructor: Nate TeBlunthuis
 * Course Catalog Description: Fundamental principles of data science and its implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness and accountability; data provenance, curation, preservation, and reproducibility; human computation; data communication and visualization; the role of data science in organizational context and the societal impacts of data science.

Course Description
The rise of "data science" reflects a broad and ongoing shift in how many teams, organizational leaders, communities of practice, and entire industries create and use knowledge. This class teaches "data science" as practiced by data-intensive knowledge workers but also as it is positioned in historical, organizational, institutional, and societal contexts. Students will gain an appriciation for the technical and intellectual aspects of data science, consider critical questions about how data science is often practiced, and envision ethical and effective science practice in their current and future organiational roles. The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments.

The course is designed around two high-stakes projects. In the first stage of the students will attend the Community Data Science Workshop (CDSC). I am one of the organizers and instructors of this three week intensive workshop on basic programming and data analysis skills. The first course project is to apply these skills together with the conceptual material from this course we have covered so far to conduct an original data analysis on a topic of the student's interest. The second high-stakes project is a critical analysis of an organization or work team. For this project students will serve as consultants to an organizational unit involved in data science. Through interviews and workplace observations they will gain an understanding of the socio-technical and organizational context of their team. They will then synthesize this understanding with the knowledge they gained from the course material to compose a report offering actionable insights to their team.

Learning Objectives
By the end of this course, students will be able to:
 * Understand what it means to analyze large and complex data effectively and ethically with an understanding of human, societal, organizational, and socio-technical contexts.
 * Consider the account ethical, social, organizational, and legal considerations of data science in organizational and institutional contexts.
 * Combine quantitative and qualitative data to generate critical insights into human behavior.
 * Discuss and evaluate ethical, social, organizational and legal trade-offs of different data analysis, testing, curation, and sharing methods.

Schedule
Course schedule (click to expand) 

Week 1

 * 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
 * Attend week 1 of CDSW
 * 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.


 * 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
 * Attend week 2 of CDSW


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

Week 2

 * Ethical considerations: privacy, informed consent and user treatment


 * Assignments due
 * Week 1 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
 * Attend week 2 of CDSW
 * Assignment 1: Data curation

Week 3

 * Reproducibility and Accountability: data curation, preservation, documentation, and archiving; best practices for open scientific research


 * Assignments due
 * Week 2 reading reflection
 * Attend week 2 of CDSW


 * 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
 * Attend week 3 of CDSW

Week 4

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


 * Assignments due
 * Reading reflection


 * Readings assigned (Read both, reflect on one)
 * Barley, S. R. (1986). Technology as an occasion for structuring: evidence from observations of ct scanners and the social order of radiology departments. Administrative Science Quarterly, 31(1), 78–108.
 * Orlikowski, W. J., & Barley, S. R. (2001). Technology and institutions: what can research on information technology and research on organizations learn from each other? MIS Q., 25(2), 145–165. https://doi.org/10.2307/3250927


 * Homework assigned
 * Reading reflection
 * A1: Data curation

Week 5

 * Technology and Organizing


 * Assignments due
 * Week 4 reading reflection
 * A1: Data curation


 * Readings assigned
 * Passi, S., & Jackson, S. J. (2018). Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects. Proc. ACM Hum.-Comput. Interact., 2(CSCW), 136:1–136:28. https://doi.org/10.1145/3274405


 * Homework Assigned
 * Reading reflection
 * A2: Bias in data

Week 6

 * Data science in Organizational Contexts


 * Assignments due
 * Week 5 reading reflection
 * A2: Bias in 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)

Week 7

 * Introduction to mixed-methods research: Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing


 * Assignments due
 * Reading reflection


 * Readings assigned (Read both, reflect on one)
 * Donovan, J., Caplan, R., Matthews, J., & Hanson, L. (2018). Algorithmic accountability: A primer. Data & Society, 501(c).
 * 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

Week 8

 * Algorithms: algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits


 * Assignments due
 * Reading reflection
 * A4: Final Project Plan


 * 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

Week 9

 * 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


 * 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

 * User experience and big data: Design considerations for machine learning applications; human centered data visualization; data storytelling


 * Assignments due
 * Reading reflection


 * Readings assigned
 * NONE


 * Homework assigned
 * A5: Final presentation

Week 11

 * Final presentations: course wrap up, presentation of student projects''


 * Assignments due
 * A5: Final presentation


 * Readings assigned
 * none!


 * Homework assigned
 * A6: Final project report (by 11:59pm)

Week 12: Finals Week (No Class Session)

 * NO CLASS
 * A6: FINAL PROJECT REPORT DUE BY 11:59PM