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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. However, students without any background in either programming or in qualitative research (e.g. interviewing) may find this course a challenge. | ||
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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;Assignments due | ;Assignments due | ||
* Week 4 reading reflection | * Week 4 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 | ||
* A1: Project proposal and data aquisition | * A1: Project proposal and data aquisition | ||
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;Homework assigned | ;Homework assigned | ||
* Week 5 reading reflection | * Week 5 reading reflection | ||
* Attend week 4 of CDSW | |||
* A2: Data analysis (due week 6) | * A2: Data analysis (due week 6) | ||
<!-- ;Resources --> | <!-- ;Resources --> | ||
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=== Week 6 === | === Week 6 === | ||
; Data science in Organizational Contexts | ; Data science in Organizational Contexts | ||
''And a crash course on qualitative research'' | ''And a crash course on qualitative research'' | ||
; Assignments due | ; Assignments due | ||
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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'' | ||
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;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 | ||
<!-- ;Agenda --> | <!-- ;Agenda --> | ||
<!-- {{:HCDS (Fall 2018)/Day 9 plan}} --> | <!-- {{:HCDS (Fall 2018)/Day 9 plan}} --> | ||
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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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;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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:* Includes a table of summary statistics (minimum, maximum, median, and mean values) for variables in your dataset related to these questions | :* 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. | ||
==== Evaluation and Rubric ==== | ==== Evaluation and Rubric ==== | ||
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For this exercise, you will design and execute the analysis that you proposed in A1. You must attempt to answer to the questions you posed using your new data science skills, but you must also practice a kind of "meta-analysis" of your analysis to understand limitations and potential consequences of your analysis. Turn in a report of about 1500 words with about equal space dedicated to: | For this exercise, you will design and execute the analysis that you proposed in A1. You must attempt to answer to the questions you posed using your new data science skills, but you must also practice a kind of "meta-analysis" of your analysis to understand limitations and potential consequences of your analysis. Turn in a report of about 1500 words with about equal space dedicated to: | ||
* Presenting your analysis: what did you do and what did you find out? | * Presenting your analysis: what did you do and what did you find out? Commnicate your findings though using at least one chart or table. | ||
* Explaining the significance of your analysis to the (real or hypothetical) organization or community that will make use of it. Why should we care about this analysis? | * Explaining the significance of your analysis to the (real or hypothetical) organization or community that will make use of it. Why should we care about this analysis? | ||
* Critique of the analysis in terms of both what you did and how it might be used. How might your analysis improve through better data or analysis? What assumptions | * Critique of the analysis in terms of both what you did and how it might be used. How might your analysis improve through better data or analysis? What assumptions underly your interpretation of it? How might (or might not) this analysis influence or mislead its audiences? | ||
==== Evaluation and Rubric ==== | ==== Evaluation and Rubric ==== | ||
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:''Critique in terms of improving the analysis :'' 10% | :''Critique in terms of improving the analysis :'' 10% | ||
:''Critique in terms of application :'' 10% | :''Critique in terms of application :'' 10% | ||
:''Writing quality (see [[ | :''Writing quality (see [[Assessment#Writing Rubric | the writing rubric]]): 15%'' | ||
=== | === A4: Final project plan === | ||
For this assignment, you will write up a study plan for your final class project | 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. | ||
Specifically your plan should: | Specifically your plan should: | ||
* Identify the organization that you will work with, and your contact there. | * Identify the organization that you will work with, and your contact there. | ||
* Summarize what you already know about this organization and how they use data science. | * Summarize what you already know about this organization and how they use data science. | ||
* Identify a research question that you don't already know the answer to, but where project can realistically help you answer it | * Identify a research question that you don't already know the answer to, but where project can realistically help you answer it. | ||
* Outline your plan to collect qualitative data. Will you conduct interviews? Who with? What questions will you ask? Will you conduct workplace observations? | * Outline your plan to collect qualitative data. Will you conduct interviews? Who with? What questions will you ask? Will you conduct workplace observations? | ||
* Explain what results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself). | * Explain what results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself). | ||
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:''Organization and identification:'' 20% | :''Organization and identification:'' 20% | ||
:''Summary of what you already know:'' 20% | :''Summary of what you already know:'' 20% | ||
:''Research | :''Research Question:'' 20% | ||
:''Data collection plan:'' 20% | :''Data collection plan:'' 20% | ||
:''Anticipated results and their significance:'' 20% | :''Anticipated results and their significance:'' 20% | ||
=== | === 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. This is your chance to get quality feedback on your project from me and from your classmates. Key elements that you should cover in your presentation include: | 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. This is your chance to get quality feedback on your project from me and from your classmates. Key elements that you should cover in your presentation include: | ||
:* What | :* What orgazniation or team you studied | ||
:* Your research question | :* Your research question | ||
:* Your findings: what you learned about your research question | :* Your findings: what you learned about your research question | ||
:* Who you observed or interviewed | :* Who you observed or interviewed | ||
:* | :* Quotations or anectodes from your qualitative data that support your findings | ||
:* | :* Tenative recommendations to the organization based on your findings | ||
:''Presentation organization and slide design:'' 15% | |||
:''Presentation of '' 25% | |||
:''Data collection plan:'' 25% | |||
:''Anticipated results and their significance:'' 25% | |||
=== 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]] |