Human Centered Data Science (Fall 2018)/Schedule

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Week 1: September 27[edit]

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
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
Homework assigned
  • Reading reflection
Resources




Week 2: October 4[edit]

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


Homework assigned
Resources




Week 3: October 11[edit]

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
Homework assigned
  • Reading reflection
Resources


Assignment 1 Data curation resources





Week 4: October 18[edit]

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
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)
Homework assigned


Resources




Week 5: October 25[edit]

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)


Homework assigned


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





Week 6: November 1[edit]

Day 6 plan

Day 6 slides

Interrogating algorithms
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
Agenda
  • Reading reflections
  • Ethical implications of crowdwork
  • Algorithmic transparency, interpretability, and accountability
  • Auditing algorithms
  • In-class activity: auditing the Perspective API


Readings assigned


Homework assigned
  • Reading reflection


Resources





Week 7: November 8[edit]

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)
Homework assigned


Resources





Week 8: November 15[edit]

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
Homework assigned
  • Reading reflection
Resources





Week 9: November 22 (No Class Session)[edit]

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
  • No class! Enjoy your night off.


Readings assigned
Homework assigned
  • Reading reflection
Resources





Week 10: November 29[edit]

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





Week 11: December 6[edit]

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)[edit]

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