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

From CommunityData
Line 74: Line 74:
  
 
;Homework assigned
 
;Homework assigned
* Read and reflect: Olteanu, A., Castillo, C., Diaz, F., Kıcıman, E., & Kiciman, E. (2019). ''[https://www.frontiersin.org/articles/10.3389/fdata.2019.00013/pdf Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries].'' Frontiers in Big Data, 2, 13. https://doi.org/10.3389/fdata.2019.00013
+
* Read and reflect: Duarte, N., Llanso, E., & Loup, A. (2018). ''[https://cdt.org/files/2017/12/FAT-conference-draft-2018.pdf Mixed Messages? The Limits of Automated Social Media Content Analysis].'' Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 106.
 
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A2:_Bias_in_data|A2: Bias in data]]
 
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A2:_Bias_in_data|A2: Bias in data]]
  
Line 81: Line 81:
 
* J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), ''[http://altmetrics.org/manifesto Altmetrics: A manifesto]'', 26 October 2010.
 
* J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), ''[http://altmetrics.org/manifesto Altmetrics: A manifesto]'', 26 October 2010.
 
* Chapter 2 [https://www.practicereproducibleresearch.org/core-chapters/2-assessment.html "Assessing Reproducibility"] and Chapter 3 [https://www.practicereproducibleresearch.org/core-chapters/3-basic.html "The Basic Reproducible Workflow Template"] from ''The Practice of Reproducible Research'' University of California Press, 2018.  
 
* Chapter 2 [https://www.practicereproducibleresearch.org/core-chapters/2-assessment.html "Assessing Reproducibility"] and Chapter 3 [https://www.practicereproducibleresearch.org/core-chapters/3-basic.html "The Basic Reproducible Workflow Template"] from ''The Practice of Reproducible Research'' University of California Press, 2018.  
 
<!--
 
 
* TeBlunthuis, N., Shaw, A., and Hill, B.M. (2018). Revisiting "The rise and decline" in a population of peer production projects. In ''Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems (CHI '18)''. https://doi.org/10.1145/3173574.3173929
 
* TeBlunthuis, N., Shaw, A., and Hill, B.M. (2018). Revisiting "The rise and decline" in a population of peer production projects. In ''Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems (CHI '18)''. https://doi.org/10.1145/3173574.3173929
 
* Press, Gil. [https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2608257f6f63 ''Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says.''] Forbes, 2016.
 
* Press, Gil. [https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2608257f6f63 ''Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says.''] Forbes, 2016.
 
* Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
 
* Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
 +
<!--
 
* Aschwanden, Christie. [https://fivethirtyeight.com/features/science-isnt-broken/ ''Science Isn't Broken''] FiveThirtyEight, 2015.
 
* Aschwanden, Christie. [https://fivethirtyeight.com/features/science-isnt-broken/ ''Science Isn't Broken''] FiveThirtyEight, 2015.
 
* sample code for API calls ([http://paws-public.wmflabs.org/paws-public/User:Jtmorgan/data512_a1_example.ipynb view the notebook], [http://paws-public.wmflabs.org/paws-public/User:Jtmorgan/data512_a1_example.ipynb?format=raw download the notebook]).
 
* sample code for API calls ([http://paws-public.wmflabs.org/paws-public/User:Jtmorgan/data512_a1_example.ipynb view the notebook], [http://paws-public.wmflabs.org/paws-public/User:Jtmorgan/data512_a1_example.ipynb?format=raw download the notebook]).
 
*''See [[Human_Centered_Data_Science/Datasets#Dataset_documentation_examples|the datasets page]] for examples of well-documented and not-so-well documented open datasets.''
 
*''See [[Human_Centered_Data_Science/Datasets#Dataset_documentation_examples|the datasets page]] for examples of well-documented and not-so-well documented open datasets.''
 
-->
 
-->
 
 
<br/>
 
<br/>
 
<hr/>
 
<hr/>

Revision as of 22:31, 25 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

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
  • In-class activity
  • Intro to assignment 2: Bias in data
Homework assigned
Resources




Week 3: October 10

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
Homework assigned
  • Read both, reflect on one:
Resources




Week 4: October 17

Introduction to mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing
Assignments due
  • 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 and mixed-methods research resources
Crowdwork research resources




Week 5: October 24

Research ethics for big data
privacy, informed consent and user treatment
Assignments due
  • 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: 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

Data science and society
power, data, and society; ethics of crowdwork
Assignments due
  • Reading reflection
  • A3: Crowdwork ethnography
Agenda
  • Reading reflections
  • Assignment 3 review
  • In-class activity
  • Introduction to assignment 4: Final project proposal
Homework assigned
  • Read both, reflect on one:
Resources




Week 7: November 7

Human centered machine learning
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection
  • A4: Project proposal
Agenda
  • Reading reflection review
  • Algorithmic transparency, interpretability, and accountability
  • Auditing algorithms
  • In-class activity
  • Introduction to assignment 5: Final project proposal
Homework assigned
Resources




Week 8: November 14

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
  • coming soon
Homework assigned
Resources




Week 9: November 21

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
  • coming soon
Homework assigned
Resources




Week 10: November 28 (No Class Session)

Assignments due
  • Reading reflection
Homework assigned
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.