Human Centered Data Science (Fall 2019)

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Human Centered Data Science
DATA 512 - UW Interdisciplinary Data Science Masters Program - Thursdays 5:00-9:50pm in Thompson Hall room 134.
Principal instructor
Jonathan T. Morgan
Co-instructor
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 Canvas 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
Human Centered Data Science focuses on 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.[1]

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 2019).

Office hours

  • TA: TBD
  • Jonathan Morgan: Thursday 2:30 - 4:30 (Comm FIXME) and via Google Meet (by request)


Lecture slides

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


Schedule

Direct link: Human Centered Data Science (Fall 2019)/Schedule

Course schedule (click to expand)


Week 1: September 26

Day 1 plan

Introduction to Human Centered Data Science
What is data science? What is human centered? What is human centered data science?
Assignments due
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?
  • In-class activity
  • Intro to assignment 1: Data Curation


Homework assigned
  • Read and reflect on both:
Resources




Week 2: October 3

Day 2 plan


Reproducibility and Accountability
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 1 reading reflection
  • A1: Data curation
Agenda
  • Reading reflection discussion
  • Assignment 1 review & reflection
  • A primer on copyright, licensing, and hosting for code and data
  • Introduction to replicability, reproducibility, and open research
  • Reproducibility case study: fivethirtyeight.com
  • In-class activity
  • Intro to assignment 2: Bias in data


Homework assigned
Resources





Week 3: October 10

Day 3 plan

Interrogating datasets
causes and consequences of bias in data; best practices for selecting, describing, and implementing training data
Assignments due
  • Week 2 reading reflection
Agenda
  • Reading reflection review
  • Sources of bias in datasets
  • Sources of bias in data collection and processing
  • In-class activity


Readings assigned (Read both, reflect on one)
Homework assigned
  • Reading reflection
Resources




Week 4: October 17

Day 4 plan


Introduction to mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing
Assignments due
  • Week 3 reading reflection
  • A2: Bias in data
Agenda
  • Reading reflection review
  • Review of assignment 2
  • Survey of qualitative research methods
  • Mixed-methods case study
  • Introduction to ethnography
  • Ethnographic research case study
  • In-class activity
  • Introduction to crowdwork
  • Overview of Assignment 3: Crowdwork ethnography


Homework assigned
Qualitative research methods resources
Wikipedia gender gap research resources
Crowdwork research resources




Week 5: October 24

Day 5 plan

Research ethics for big data
privacy, informed consent and user treatment
Assignments due
  • Week 4 reading reflection
Agenda
  • Reading reflection review
  • A brief history of research ethics in the United States
  • Research ethics in data science
  • Technological approaches to data privacy
  • Guest lecture
  • Procedural approaches to data privacy


Homework assigned
  • Read and reflect: Mary Gray, Ghost Work FIXME
  • Final project proposal FIXME
Resources




Week 6: October 31

Day 6 plan

Data science and society
power, data, and society; ethics of crowdwork
Assignments due
  • Reading reflection
  • A3: Crowdwork ethnography
Agenda
  • Guest lecture: Rochelle LaPlante


Homework assigned
  • Read both, reflect on one:
Resources




Week 7: November 7

Day 7 plan

Human centered machine learning
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection
  • A4: Project proposal
Agenda
  • Reading reflections
  • Ethical implications of crowdwork
  • Algorithmic transparency, interpretability, and accountability
  • Auditing algorithms
  • In-class activity: auditing the Perspective API


Homework assigned
  • Read and reflect: TBD
  • A5: Final project plan
Resources




Week 8: November 14

Day 8 plan

User experience and data science
algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems
Assignments due
  • Reading reflection
  • A5: Final project plan
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


Homework assigned
  • Reading and reflect: TBD (data science ethics survey paper)
  • A6: Final project presentation
Resources




Week 9: November 21

Day 9 plan

Data science in organizations
TBD
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


Homework assigned
  • Read and reflect: TBD
  • A6: Final project presentation
  • A7: Final project report
Resources




Week 10: November 28 (No Class Session)

Assignments due
  • Reading reflection
Readings assigned
  • NONE
Homework assigned
  • NONE
Resources




Week 11: December 5

Final presentations
presentation of student projects, course wrap up
Assignments due
  • Reading reflection
  • A5: Final presentation
Readings assigned
  • NONE
Homework assigned
  • NONE
Resources
  • NONE




Week 12: Finals Week (No Class Session)

  • NO CLASS
  • A7: FINAL PROJECT REPORT DUE BY 5:00PM on Tuesday, December 10 via Canvas
  • LATE PROJECT SUBMISSIONS NOT ACCEPTED.

Assignments

For details on individual assignments, see Human Centered Data Science (Fall 2019)/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.


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.

By default, 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 without prior written permission. Requests for special dispensation on final project due dates must be submitted to the instructor via email no less than 2 week before the final project deadline.

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:

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 individual 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.

References