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; | ;Data Science and Organizational Communication: | ||
; | ;Principal instructor: [[User:Groceryheist|Nate TeBlunthuis]] | ||
;Course Catalog Description: Fundamental principles of data science and its implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness and accountability; data provenance, curation, preservation, and reproducibility; human computation; data communication and visualization; the role of data science in organizational context and the societal impacts of data science. | ;Course Catalog Description: Fundamental principles of data science and its implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness and accountability; data provenance, curation, preservation, and reproducibility; human computation; data communication and visualization; the role of data science in organizational context and the societal impacts of data science. | ||
== Course Description == | == Course Description == | ||
The rise of "data science" reflects a broad and ongoing shift in how many teams, organizational leaders, communities of practice, and entire industries create and use knowledge. This class teaches "data science" as practiced by data-intensive knowledge workers but also as it is positioned in historical, organizational, institutional, and societal contexts. Students will gain an | The rise of "data science" reflects a broad and ongoing shift in how many teams, organizational leaders, communities of practice, and entire industries create and use knowledge. This class teaches "data science" as practiced by data-intensive knowledge workers but also as it is positioned in historical, organizational, institutional, and societal contexts. Students will gain an appriciation for the technical and intellectual aspects of data science, consider critical questions about how data science is often practiced, and envision ethical and effective science practice in their current and future organiational roles. The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments. We assume no prior expertise in programming or statistics, only strong academic skills and a willingness to learn. | ||
The course is designed around two high-stakes projects. In the first stage of the students will attend the Community Data Science Workshop (CDSC). I am one of the organizers and instructors of this three week intensive workshop on basic programming and data analysis skills. The first course project is to apply these skills together with the conceptual material from this course we have covered so far to conduct an original data analysis on a topic of the student's interest. The second high-stakes project is a critical analysis of an organization or work team. For this project students will serve as consultants to an organizational unit involved in data science. Through interviews and workplace observations they will gain an understanding of the socio-technical and organizational context of their team. They will then synthesize this understanding with the knowledge they gained from the course material to compose a report offering actionable insights to their team. | The course is designed around two high-stakes projects. In the first stage of the students will attend the Community Data Science Workshop (CDSC). I am one of the organizers and instructors of this three week intensive workshop on basic programming and data analysis skills. The first course project is to apply these skills together with the conceptual material from this course we have covered so far to conduct an original data analysis on a topic of the student's interest. The second high-stakes project is a critical analysis of an organization or work team. For this project students will serve as consultants to an organizational unit involved in data science. Through interviews and workplace observations they will gain an understanding of the socio-technical and organizational context of their team. They will then synthesize this understanding with the knowledge they gained from the course material to compose a report offering actionable insights to their team. | ||
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;Homework assigned | ;Homework assigned | ||
* | * Reading reflection | ||
* Attend week 1 of CDSW | * Attend week 1 of CDSW | ||
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;Assignments due | ;Assignments due | ||
* Week | * Week 1 reading reflection | ||
* Attend week 1 of CDSW | * Attend week 1 of CDSW | ||
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;Homework assigned | ;Homework assigned | ||
* | * Reading reflection | ||
* Attend week 2 of CDSW | * Attend week 2 of CDSW | ||
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;Assignments due | ;Assignments due | ||
* Week | * Week 2 reading reflection | ||
* Attend week 2 of CDSW | * Attend week 2 of CDSW | ||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
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;Homework assigned | ;Homework assigned | ||
* | * Reading reflection | ||
* Attend week 3 of CDSW | * Attend week 3 of CDSW | ||
* | * Project assignment 1: Project proposal and data aquisition | ||
<!-- ;Resources --> | <!-- ;Resources --> | ||
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;Assignments due | ;Assignments due | ||
* | * Reading reflection | ||
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|A1: Data curation]] | |||
* Attend week 3 of CDSW | * Attend week 3 of CDSW | ||
* | * Project assignment 1: Project proposal and data aquisition | ||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
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;Homework assigned | ;Homework assigned | ||
* | * Reading reflection | ||
* | * Attend week 4 of CDSW | ||
<!-- ;Resources --> | <!-- ;Resources --> | ||
<!-- * Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016). ''[http://kiciman.org/wp-content/uploads/2017/08/SSRN-id2886526.pdf Social data: Biases, methodological pitfalls, and ethical boundaries]. --> | <!-- * Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016). ''[http://kiciman.org/wp-content/uploads/2017/08/SSRN-id2886526.pdf Social data: Biases, methodological pitfalls, and ethical boundaries]. --> | ||
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; Assignments due | ; Assignments due | ||
* Week | * Week 4 reading reflection | ||
* Assignment 1: Project proposal and data aquisition | * Assignment 1: Project proposal and data aquisition | ||
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; Homework Assigned | ; Homework Assigned | ||
* | * Reading reflection | ||
* | * Assignment 2: Data analysis | ||
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=== Week 6 === | === Week 6 === | ||
; Data science in Organizational Contexts | ; Data science in Organizational Contexts | ||
; Assignments due | ; Assignments due | ||
* Week | * Week 5 reading reflection | ||
;Readings assigned (Read both, reflect on one) | ;Readings assigned (Read both, reflect on one) | ||
* Wang, Tricia. ''[https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7 Why Big Data Needs Thick Data]''. Ethnography Matters, 2016. | * Wang, Tricia. ''[https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7 Why Big Data Needs Thick Data]''. Ethnography Matters, 2016. | ||
* Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao (Ken) Wang, and Brent Hecht. 2015. ''[http://www-users.cs.umn.edu/~bhecht/publications/goldstandards_CSCW2015.pdf Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards]''. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '15) | * Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao (Ken) Wang, and Brent Hecht. 2015. ''[http://www-users.cs.umn.edu/~bhecht/publications/goldstandards_CSCW2015.pdf Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards]''. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '15) | ||
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<!-- [[:File:HCDS 2018 week 5 slides.pdf|Day 5 slides]] --> | <!-- [[:File:HCDS 2018 week 5 slides.pdf|Day 5 slides]] --> | ||
;Introduction to mixed-methods research: ''Big data vs thick data; integrating qualitative research methods into data science practice; | ;Introduction to mixed-methods research: ''Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing'' | ||
;Assignments due | ;Assignments due | ||
* | * Reading reflection | ||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
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;Homework assigned | ;Homework assigned | ||
* | * Reading reflection | ||
<!-- ;Qualitative research methods resources --> | <!-- ;Qualitative research methods resources --> | ||
<!-- * Ladner, S. (2016). ''[http://www.practicalethnography.com/ Practical ethnography: A guide to doing ethnography in the private sector]''. Routledge. --> | <!-- * Ladner, S. (2016). ''[http://www.practicalethnography.com/ Practical ethnography: A guide to doing ethnography in the private sector]''. Routledge. --> | ||
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<!-- * Usability.gov, ''[https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html System usability scale]''. --> | <!-- * Usability.gov, ''[https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html System usability scale]''. --> | ||
<!-- * Nielsen, Jakob (2000). ''[https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/ Why you only need to test with five users]''. nngroup.com. --> | <!-- * Nielsen, Jakob (2000). ''[https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/ Why you only need to test with five users]''. nngroup.com. --> | ||
<!-- ;Wikipedia gender gap research resources --> | <!-- ;Wikipedia gender gap research resources --> | ||
<!-- * Hill, B. M., & Shaw, A. (2013). ''[journals.plos.org/plosone/article?id=10.1371/journal.pone.0065782 The Wikipedia gender gap revisited: Characterizing survey response bias with propensity score estimation]''. PloS one, 8(6), e65782 --> | <!-- * Hill, B. M., & Shaw, A. (2013). ''[journals.plos.org/plosone/article?id=10.1371/journal.pone.0065782 The Wikipedia gender gap revisited: Characterizing survey response bias with propensity score estimation]''. PloS one, 8(6), e65782 --> | ||
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<!-- * Maximillian Klein. ''[http://whgi.wmflabs.org/gender-by-language.html Gender by Wikipedia Language]''. Wikidata Human Gender Indicators (WHGI), 2017. --> | <!-- * Maximillian Klein. ''[http://whgi.wmflabs.org/gender-by-language.html Gender by Wikipedia Language]''. Wikidata Human Gender Indicators (WHGI), 2017. --> | ||
<!-- * Source: Wagner, C., Garcia, D., Jadidi, M., & Strohmaier, M. (2015, April). ''[https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10585/10528 It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia]''. In ICWSM (pp. 454-463). --> | <!-- * Source: Wagner, C., Garcia, D., Jadidi, M., & Strohmaier, M. (2015, April). ''[https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10585/10528 It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia]''. In ICWSM (pp. 454-463). --> | ||
<!-- * Benjamin Collier and Julia Bear. ''[https://static1.squarespace.com/static/521c8817e4b0dca2590b4591/t/523745abe4b05150ff027a6e/1379354027662/2012+-+Collier%2C+Bear+-+Conflict%2C+confidence%2C+or+criticism+an+empirical+examination+of+the+gender+gap+in+Wikipedia.pdf Conflict, criticism, or confidence: an empirical examination of the gender gap in wikipedia contributions]''. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12). DOI: https://doi.org/10.1145/2145204.2145265 --> | <!-- * Benjamin Collier and Julia Bear. ''[https://static1.squarespace.com/static/521c8817e4b0dca2590b4591/t/523745abe4b05150ff027a6e/1379354027662/2012+-+Collier%2C+Bear+-+Conflict%2C+confidence%2C+or+criticism+an+empirical+examination+of+the+gender+gap+in+Wikipedia.pdf Conflict, criticism, or confidence: an empirical examination of the gender gap in wikipedia contributions]''. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12). DOI: https://doi.org/10.1145/2145204.2145265 --> | ||
<!-- * Christina Shane-Simpson, Kristen Gillespie-Lynch, Examining potential mechanisms underlying the Wikipedia gender gap through a collaborative editing task, In Computers in Human Behavior, Volume 66, 2017, https://doi.org/10.1016/j.chb.2016.09.043. (PDF on Canvas) --> | <!-- * Christina Shane-Simpson, Kristen Gillespie-Lynch, Examining potential mechanisms underlying the Wikipedia gender gap through a collaborative editing task, In Computers in Human Behavior, Volume 66, 2017, https://doi.org/10.1016/j.chb.2016.09.043. (PDF on Canvas) --> | ||
<!-- * Amanda Menking and Ingrid Erickson. 2015. ''[https://upload.wikimedia.org/wikipedia/commons/7/77/The_Heart_Work_of_Wikipedia_Gendered,_Emotional_Labor_in_the_World%27s_Largest_Online_Encyclopedia.pdf The Heart Work of Wikipedia: Gendered, Emotional Labor in the World's Largest Online Encyclopedia]''. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). https://doi.org/10.1145/2702123.2702514 --> | <!-- * Amanda Menking and Ingrid Erickson. 2015. ''[https://upload.wikimedia.org/wikipedia/commons/7/77/The_Heart_Work_of_Wikipedia_Gendered,_Emotional_Labor_in_the_World%27s_Largest_Online_Encyclopedia.pdf The Heart Work of Wikipedia: Gendered, Emotional Labor in the World's Largest Online Encyclopedia]''. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). https://doi.org/10.1145/2702123.2702514 --> | ||
<!-- ;Crowdwork research resources --> | <!-- ;Crowdwork research resources --> | ||
<!-- * WeArDynamo contributors. ''[http://wiki.wearedynamo.org/index.php?title=Basics_of_how_to_be_a_good_requester How to be a good requester]'' and ''[http://wiki.wearedynamo.org/index.php?title=Guidelines_for_Academic_Requesters Guidelines for Academic Requesters]''. Wearedynamo.org --> | <!-- * WeArDynamo contributors. ''[http://wiki.wearedynamo.org/index.php?title=Basics_of_how_to_be_a_good_requester How to be a good requester]'' and ''[http://wiki.wearedynamo.org/index.php?title=Guidelines_for_Academic_Requesters Guidelines for Academic Requesters]''. Wearedynamo.org --> | ||
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;Assignments due | ;Assignments due | ||
* | * Reading reflection | ||
* A4: Final Project Plan | |||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
<!-- {{:HCDS (Fall 2018)/Day 6 plan}} --> | <!-- {{:HCDS (Fall 2018)/Day 6 plan}} --> | ||
;Readings assigned | ;Readings assigned | ||
* Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). ''[https://mako.cc/academic/hill_etal-cdsw_chapter-DRAFT.pdf Democratizing Data Science: The Community Data Science Workshops and Classes].'' In N. Jullien, S. A. Matei, & S. P. Goggins (Eds.), Big Data Factories: Scientific Collaborative approaches for virtual community data collection, repurposing, recombining, and dissemination. | * Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). ''[https://mako.cc/academic/hill_etal-cdsw_chapter-DRAFT.pdf Democratizing Data Science: The Community Data Science Workshops and Classes].'' In N. Jullien, S. A. Matei, & S. P. Goggins (Eds.), Big Data Factories: Scientific Collaborative approaches for virtual community data collection, repurposing, recombining, and dissemination. | ||
;Homework assigned | ;Homework assigned | ||
* | * Reading reflection | ||
<!-- ;Resources --> | <!-- ;Resources --> | ||
<!-- * Ethical OS ''[https://ethicalos.org/wp-content/uploads/2018/08/Ethical-OS-Toolkit-2.pdf Toolkit]'' and ''[https://ethicalos.org/wp-content/uploads/2018/08/EthicalOS_Check-List_080618.pdf Risk Mitigation Checklist]''. EthicalOS.org. --> | <!-- * Ethical OS ''[https://ethicalos.org/wp-content/uploads/2018/08/Ethical-OS-Toolkit-2.pdf Toolkit]'' and ''[https://ethicalos.org/wp-content/uploads/2018/08/EthicalOS_Check-List_080618.pdf Risk Mitigation Checklist]''. EthicalOS.org. --> | ||
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<!-- * Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. ''[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias: Risk Assessment in Criminal Sentencing]. Propublica, May 2018. --> | <!-- * Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. ''[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias: Risk Assessment in Criminal Sentencing]. Propublica, May 2018. --> | ||
<!-- * [https://www.perspectiveapi.com/#/ Google's Perspective API] --> | <!-- * [https://www.perspectiveapi.com/#/ Google's Perspective API] --> | ||
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<hr/> | <hr/> | ||
<br/> | <br/> | ||
<!-- === Week 7 === --> | <!-- === Week 7 === --> | ||
<!-- <\!-- [[HCDS_(Fall_2018)/Day_7_plan|Day 7 plan]] -\-> --> | <!-- <\!-- [[HCDS_(Fall_2018)/Day_7_plan|Day 7 plan]] -\-> --> | ||
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<!-- <hr/> --> | <!-- <hr/> --> | ||
<!-- <br/> --> | <!-- <br/> --> | ||
=== Week 9 === | === Week 9 === | ||
<!-- [[HCDS_(Fall_2018)/Day_8_plan|Day 9 plan]] --> | <!-- [[HCDS_(Fall_2018)/Day_8_plan|Day 9 plan]] --> | ||
;Data science for social good: ''Community-based and participatory approaches to data science; Using data science for society's benefit'' | ;Data science for social good: ''Community-based and participatory approaches to data science; Using data science for society's benefit'' | ||
;Assignments due | ;Assignments due | ||
* | * Reading reflection | ||
* A4: Final project plan | |||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
<!-- {{:HCDS (Fall 2018)/Day 9 plan}} --> | <!-- {{:HCDS (Fall 2018)/Day 9 plan}} --> | ||
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;Homework assigned | ;Homework assigned | ||
* Reading reflection | * Reading reflection | ||
;Resources | ;Resources | ||
* Daniela Aiello, Lisa Bates, et al. [https://shelterforce.org/2018/08/22/eviction-lab-misses-the-mark/ Eviction Lab Misses the Mark], ShelterForce, August 2018. | * Daniela Aiello, Lisa Bates, et al. [https://shelterforce.org/2018/08/22/eviction-lab-misses-the-mark/ Eviction Lab Misses the Mark], ShelterForce, August 2018. | ||
<br/> | <br/> | ||
<hr/> | <hr/> | ||
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=== Week 10 === | === Week 10 === | ||
<!-- [[HCDS_(Fall_2018)/Day_10_plan|Day 10 plan]] --> | <!-- [[HCDS_(Fall_2018)/Day_10_plan|Day 10 plan]] --> | ||
<!-- [[:File:HCDS 2018 week 10 slides.pdf|Day 10 slides]] --> | <!-- [[:File:HCDS 2018 week 10 slides.pdf|Day 10 slides]] --> | ||
;User experience and big data: ''Design considerations for machine learning applications; human centered data visualization; data storytelling'' | ;User experience and big data: ''Design considerations for machine learning applications; human centered data visualization; data storytelling'' | ||
;Assignments due | ;Assignments due | ||
* | * Reading reflection | ||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
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;Homework assigned | ;Homework assigned | ||
* | * A5: Final presentation | ||
<!-- ;Resources --> | <!-- ;Resources --> | ||
<!-- *Fabien Girardin. ''[https://medium.com/@girardin/experience-design-in-the-machine-learning-era-e16c87f4f2e2 Experience design in the machine learning era].'' Medium, 2016. --> | <!-- *Fabien Girardin. ''[https://medium.com/@girardin/experience-design-in-the-machine-learning-era-e16c87f4f2e2 Experience design in the machine learning era].'' Medium, 2016. --> | ||
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<!-- * Megan Risdal, ''[http://blog.kaggle.com/2016/06/13/communicating-data-science-an-interview-with-a-storytelling-expert-tyler-byers/ Communicating data science: an interview with a storytelling expert].'' Kaggle blog, 2016. --> | <!-- * Megan Risdal, ''[http://blog.kaggle.com/2016/06/13/communicating-data-science-an-interview-with-a-storytelling-expert-tyler-byers/ Communicating data science: an interview with a storytelling expert].'' Kaggle blog, 2016. --> | ||
<!-- * Brent Dykes, ''[https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/ Data Storytelling: The Essential Data Science Skill Everyone Needs].'' Forbes, 2016. --> | <!-- * Brent Dykes, ''[https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/ Data Storytelling: The Essential Data Science Skill Everyone Needs].'' Forbes, 2016. --> | ||
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;Assignments due | ;Assignments due | ||
* | * A5: Final presentation | ||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
<!-- {{:HCDS (Fall 2018)/Day 11 plan}} --> | <!-- {{:HCDS (Fall 2018)/Day 11 plan}} --> | ||
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;Homework assigned | ;Homework assigned | ||
* | * A6: Final project report (by 11:59pm) | ||
<!-- ;Resources --> | <!-- ;Resources --> | ||
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=== Week 12: Finals Week (No Class Session) === | === Week 12: Finals Week (No Class Session) === | ||
* NO CLASS | * NO CLASS | ||
* | * A6: FINAL PROJECT REPORT DUE BY 11:59PM | ||
<!-- * LATE PROJECT SUBMISSIONS NOT ACCEPTED. --> | <!-- * LATE PROJECT SUBMISSIONS NOT ACCEPTED. --> | ||
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For this assignment you will propose a midterm project and use the skills you have learned in the CDSW to collect or present a dataset. You will turn in a one-page project description that | For this assignment you will propose a midterm project and use the skills you have learned in the CDSW to collect or present a dataset. You will turn in a one-page project description that | ||
* Identifies a dataset for analysis, and what makes it interesting to you. | |||
* Explains how the source of the data, how did you get it? | |||
* Describes 2-3 questions that you hope the data can help answer | |||
* Includes a table of summary statistics (minimum, maximum, median, and mean values) for variables in your dataset related to these questions | |||
I hope that you find a dataset related to your own interests, such as data from your workplace, community, or any other organization you may be involved in. For some ideas about where to look for datasets related to your interests, [[HCDS (Fall 2017)/Datasets | I hope that you find a dataset related to your own interests, such as data from your workplace, community, or any other organization you may be involved in. For some ideas about where to look for datasets related to your interests, [[see this page | HCDS (Fall 2017)/Datasets]] with examples of freely available datasets that you can use for this project. | ||
==== | ==== Rubric ==== | ||
'''Dataset identification:''' 25% | |||
'''Explaination of data source:''' 25% | |||
'''Example questions:''' 25% | |||
'''Summary statistics:''' 25% | |||
=== A2: Data analysis === | === A2: Data analysis === | ||
For this | === A4: Final project plan === | ||
For this assignment, you will write up a study plan for your final class project. The plan will cover a variety of details about your final project. Identify the organization that you will work with, the data you will use, what you will do with the data (e.g. statistical analysis, train a model), what results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself). | |||
=== A5: Final project presentation === | |||
For this assignment, you will give an in-class presentation of your final project. The goal of this assignment is to demonstrate that you are able to effectively communicate your research questions, methods, conclusions, and implications to your target audience. | |||
=== | === A6: Final project report === | ||
For this assignment, you will publish the complete code, data, and analysis of your final research project. The goal is to demonstrate that you can incorporate all of the human-centered design considerations you learned in this course and create research artifacts that are understandable, impactful, and reproducible. | |||
== Policies == | == Policies == | ||
The following general policies apply to this course. | The following general policies apply to this course. | ||
=== Attendance === | === Attendance === | ||
As detailed in [[ | As detailed in [[Teaching Assessment | my page on assessment]], attendance in class is expected of all participants. If you need to miss class for any reason, please contact a member of the teaching team ahead of time (email is best). Multiple unexplained absences will likely result in a lower grade or (in extreme circumstances) a failing grade. In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc. | ||
=== Respect === | === Respect === | ||
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=== Disability and accommodations === | === 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 | 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 individial 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. | 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. | ||
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For more information on disability accommodations, and how to apply for one, please review [http://depts.washington.edu/uwdrs/current-students/accommodations/ UW's Disability Resources for Students]. | For more information on disability accommodations, and how to apply for one, please review [http://depts.washington.edu/uwdrs/current-students/accommodations/ UW's Disability Resources for Students]. | ||
=== Grades === | |||
Grades will be determined as follows: | |||
* 20% Participation | |||
* 20% Reading reflections | |||
* 20% Midterm project | |||
* 40% Final project | |||
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. | |||
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. | |||
[[Category:Groceryheist drafts]] | [[Category:Groceryheist drafts]] |