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. We assume no prior expertise in programming or statistics, only strong academic skills and a willingness to learn. However, students without any background in either programming or in qualitative research may find this course a challenge.

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.

This version of the syllabus is designed around a weekly schedule.

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


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


 * Homework assigned
 * Week 2 reading reflection
 * Attend week 1 of CDSW

Week 2

 * Ethical considerations: privacy, informed consent and user treatment


 * Assignments due
 * Week 2 reading reflection
 * Attend week 1 of CDSW


 * 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
 * Week 3 reading reflection
 * Attend week 2 of CDSW

Week 3

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


 * Assignments due
 * Week 3 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
 * Week 4 reading reflection
 * Attend week 3 of CDSW
 * A1: Project proposal and data aquisition

Week 4

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


 * Assignments due
 * Week 4 reading reflection
 * A1: Data curation
 * Attend week 3 of CDSW
 * A1: Project proposal and data aquisition


 * 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
 * Week 5 reading reflection
 * Attend week 4 of CDSW
 * A2: Data analysis (due week 6)

Week 5

 * Technology and Organizing


 * Assignments due
 * Week 5 reading reflection
 * Assignment 1: Project proposal and data aquisition


 * 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
 * Week 6 reading reflection
 * A2: Data analysis (due week 6)

Week 6

 * Data science in Organizational Contexts


 * Assignments due
 * Week 6 reading reflection
 * A2: Data analysis
 * 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
 * Week 7 reading reflection
 * A3: Final project proposal

Week 7

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


 * Assignments due
 * Week 7 reading reflection
 * A3: Final project proposal


 * 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
 * Week 8 reading reflection
 * A4: Final presentation (due week 11)

Week 8

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


 * Assignments due
 * Week 8 Reading reflection


 * 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
 * Week 9 Reading reflection
 * A4: Final presentation (due week 11)

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


 * 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
 * A4: Final presentation (due week 11)
 * 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
 * Week 10 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

Assignments and coursework
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.

Weekly reading reflections
This course will introduce you to cutting edge research and opinion from major thinkers in the domain of human centered data science. By reading and writing about this material, you will have an opportunity to explore the complex intersections of technology, methodology, ethics, and social thought that characterize this budding field of research and practice.

As a participant in the course, you are responsible for intellectually engaging with all assigned readings and developing an understanding of the ideas discussed in them.

The weekly reading reflections assignment is designed to encourage you to reflect on these works and make connections during our class discussions. To this end, you will be responsible for posting reflections on the previous week's assigned reading before the next class session.

There will generally be multiple readings assigned each week. You are responsible for reading all of them. However, you only need to write a reflection on one reading per week. Unless your instructor specifies otherwise, you can choose which reading you would like to reflect on.

These reflections are meant to be succinct but meaningful. Follow the instructions below, demonstrate that you engaged with the material, and turn the reflection in on time, and you will receive full credit. Late reading reflections will never be accepted.


 * Instructions
 * 1) Read all assigned readings.
 * 2) Select a reading to reflect on.
 * 3) In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?"
 * 4) Using full sentences, list at least 1 question that this reading raised in your mind, and say why the reading caused you to ask this question.
 * 5) Post your reflection to Canvas before the next class session.

You are encouraged, but not required, to make connections between different readings (from the current week, from previous weeks, or other relevant material you've read/listened to/watched) in your reflections.

Project Assignments
This section provides basic descriptions of all scheduled course assignments. In assignments 1 and 2 you will build on the skills you'll learn in the community data science workshop to analyze data of your own substantive interests. The goals are to reinforce learning from the workshop and to give you hands on experience that will help you think about how data science might apply to your own community or organization.

Assignments 3, 4, and 5 scaffold your final project for the course in which you will conduct a qualitative study of data science in an organizational context. I strongly recommend for you to make arrangements to conduct observations and interviews with a data science team as soon as possible.

A1: Project proposal and data aquisition
For this assignment you will propose a midterm project and use the skills you have learned in the CDSW to collect or present a dataset. You will turn in a one-page project description that


 * Identifies a dataset for analysis, and what makes it interesting to you.
 * Explains how the source of the data, how did you get it?
 * Describes 2-3 questions that the data can help answer, and explain how you will answer them.
 * What results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself).
 * Includes a table of summary statistics (minimum, maximum, median, and mean values) for variables in your dataset related to these questions

I hope that you find a dataset related to your own interests, such as data from your workplace, community, or any other organization you may be involved in. For some ideas about where to look for datasets related to your interests, HCDS (Fall 2017)/Datasets with examples of freely available datasets that you can use for this project.

Evaluation and Rubric

 * Dataset identification: 25%


 * Explaination of data source: 25%


 * Example questions: 25%


 * Summary statistics: 25%

A2: Data analysis
For this exercise, you will design and execute the analysis that you proposed in A1. You must attempt to answer to the questions you posed using your new data science skills, but you must also practice a kind of "meta-analysis" of your analysis to understand limitations and potential consequences of your analysis. Turn in a report of about 1500 words with about equal space dedicated to:


 * Presenting your analysis: what did you do and what did you find out? Commnicate your findings though using at least one chart or table.
 * Explaining the significance of your analysis to the (real or hypothetical) organization or community that will make use of it. Why should we care about this analysis?
 * Critique of the analysis in terms of both what you did and how it might be used. How might your analysis improve through better data or analysis? What assumptions underly your interpretation of it? How might (or might not) this analysis influence or mislead its audiences?

Evaluation and Rubric

 * Presentation of data analysis: 40%
 * Appropriate chart or table: 5%
 * Explanation of applicability to community or organization: 20%
 * Critique in terms of improving the analysis : 10%
 * Critique in terms of application : 10%
 * Writing quality (see the writing rubric): 15%

A4: Final project plan
For this assignment, you will write up a study plan for your final class project. The plan will cover a variety of details about your final project.

Specifically your plan should:
 * Identify the organization that you will work with, and your contact there.
 * Summarize what you already know about this organization and how they use data science.
 * Outline your plan to collect qualitative data. Will you conduct interviews? Who with? Will you conduct workplace observations?
 * Explain what results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself).

Evaluation and Rubric

 * Organization and identification: 25%


 * Summary of what you already know 25%


 * Data collection plan: 25%


 * : 25%

A5: Final project presentation
For this assignment, you will give an in-class presentation of your final project. The goal of this assignment is to demonstrate that you are able to effectively communicate your research questions, methods, conclusions, and implications to your target audience.

A6: Final project report
For this assignment, you will publish the complete code, data, and analysis of your final research project. The goal is to demonstrate that you can incorporate all of the human-centered design considerations you learned in this course and create research artifacts that are understandable, impactful, and reproducible.

Policies
The following general policies apply to this course.

Attendance
As detailed in my page on assessment, attendance in class is expected of all participants. If you need to miss class for any reason, please contact a member of the teaching team ahead of time (email is best). Multiple unexplained absences will likely result in a lower grade or (in extreme circumstances) a failing grade. In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc.

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

Devices in Class
Electronic devices (e.g., phones, tablets, laptops) are not going to permitted in class. If you have a documented need to use a device, please contact me ahead of time to let me know. If you do get permission to use a device, I will ask you to sit in the very back of the classroom.

The goal of this policy is to help you stay focused and avoid distractions for yourself and your peers in the classroom. This is really important and turns out to be much more difficult in the presence of powerful computing devices with brightly glowing screens and fast connections to the Internet. For more on the rationale behind this policy, please read Clay Shirky’s thoughtful discussion of his approach to this issue.

Electronic Mail Standards of Conduct
Email communications (and all communications generally) among UW community members should seek to respect the rights and privileges of all members of the academic community. This includes not interfering with university functions or endangering the health, welfare, or safety of other persons. With this in mind, in addition to the University of Washington's Student Conduct Code, I establishes the following standards of conduct in respect to electronic communications among students and faculty:


 * If, as a student, you have a question about course content or procedures, please use the online discussion board designed for this purpose. If you have specific questions about your performance, contact me directly.
 * I strive to respond to Email communications within 48 hours. If you do not hear from me, please come to my office, call me, or send me a reminder Email.
 * Email communications should be limited to occasional messages necessary to the specific educational experience at hand.
 * Email communications should not include any CC-ing of anyone not directly involved in the specific educational experience at hand.
 * Email communications should not include any blind-CC-ing to third parties, regardless of the third party’s relevance to the matter at hand.

Academic integrity and plagiarism
As a University of Washington student, you are expected to practice high standards of academic honesty and integrity. You are responsible to understand and abide by UW’s Student Governance Code on Academic Misconduct, and the UW’s Administrative Code on Academic Misconduct, and to comply with verbal or written instructions from the professor or TA of this course. This includes plagiarism, which is a serious offense. All assignments will be reviewed for integrity. All rules regarding academic integrity extend to electronic communication and the use of online sources. If you are not sure what constitutes plagiarism, read this overview in addition to UW’s policy statements.

I am committed to upholding the academic standards of the University of Washington’s Student Conduct Code. If I suspect a student violation of that code, I will first engage in a conversation with that student about my concerns. If we cannot successfully resolve a suspected case of academic misconduct through our conversations, I will refer the situation to the department of communication advising office who can then work with the COM Chair to seek further input and if necessary, move the case up through the College.

While evidence of academic misconduct may result in a lower grade, I will not unilaterally lower a grade without addressing the issue with you first through the process outlined above.

Other academic integrity resources:
 * Center for Teaching and Learning: Cheating or Plagiarism
 * University of Washington Student Academic Responsibility (PDF)

Notice: The University has a license agreement with VeriCite, an educational tool that helps prevent or identify plagiarism from Internet resources. Your instructor may use the service in this class by requiring that assignments are submitted electronically to be checked by VeriCite. The VeriCite Report will indicate the amount of original text in your work and whether all material that you quoted, paraphrased, summarized, or used from another source is appropriately referenced.

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.

Grades
Grades will be determined as follows:


 * 20% Participation
 * 20% Reading reflections
 * 20% Midterm project
 * 40% Final project

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.