Human Centered Data Science (Fall 2019)/Schedule

From CommunityData
Jump to navigation Jump to search
This page is a work in progress.


Week 1: September 26[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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)[edit]

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




Week 11: December 5[edit]

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)[edit]

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