Human Centered Data Science (Fall 2019)/Schedule: Difference between revisions

From CommunityData
Line 20: Line 20:


=== Week 1: September 26 ===
=== Week 1: September 26 ===
[[HCDS_(Fall_2019)/Day_1_plan|Day 1 plan]]
<!--
<!--
[[:File:HCDS_2018_week_1_slides.pdf|Day 1 slides]]
[[:File:HCDS_2018_week_1_slides.pdf|Day 1 slides]]
Line 32: Line 31:


;Agenda
;Agenda
{{:HCDS (Fall 2019)/Day 1 plan}}
* 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
;Homework assigned
Line 44: Line 49:
* Kling, Rob and Star, Susan Leigh. [https://scholarworks.iu.edu/dspace/bitstream/handle/2022/1798/wp97-04B.html ''Human Centered Systems in the Perspective of Organizational and Social Informatics.''] 1997.
* Kling, Rob and Star, Susan Leigh. [https://scholarworks.iu.edu/dspace/bitstream/handle/2022/1798/wp97-04B.html ''Human Centered Systems in the Perspective of Organizational and Social Informatics.''] 1997.
* Harford, T. (2014). ''[http://doi.org/10.1111/j.1740-9713.2014.00778.x Big data: A big mistake?]'' Significance, 11(5), 14–19.
* Harford, T. (2014). ''[http://doi.org/10.1111/j.1740-9713.2014.00778.x Big data: A big mistake?]'' Significance, 11(5), 14–19.
<!-- * Ideo.org [http://www.designkit.org/ ''The Field Guide to Human-Centered Design.''] 2015. -->
* Ideo.org [http://www.designkit.org/ ''The Field Guide to Human-Centered Design.''] 2015.
 
<br/>
<br/>
<hr/>
<hr/>

Revision as of 21:40, 15 September 2019

This page is a work in progress.


Week 1: September 26

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

HCDS (Fall 2019)/Day 2 plan

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

HCDS (Fall 2019)/Day 3 plan

Homework assigned
  • Read both, reflect on one:
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

HCDS (Fall 2019)/Day 4 plan

Homework assigned
Qualitative research methods resources
Crowdwork research resources




Week 5: October 24

Day 5 plan

Research ethics for big data
privacy, informed consent and user treatment
Assignments due
  • Reading reflection
Agenda

HCDS (Fall 2019)/Day 5 plan

Homework assigned
  • Read and reflect: Gray, M. L., & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Eamon Dolan Books. (PDF available on Canvas)
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

HCDS (Fall 2019)/Day 6 plan

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

HCDS (Fall 2019)/Day 7 plan

Homework assigned
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

HCDS (Fall 2019)/Day 8 plan

Homework assigned
Resources




Week 9: November 21

Day 9 plan

Data science in context
Doing human centered datascience in product organizations; communicating across roles and disciplines; data science for social good
Assignments due
  • Reading reflection
Agenda

HCDS (Fall 2019)/Day 9 plan

Homework assigned
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.