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;Data Science and Organizational Communication:
;Course Title: Data Science and Organizational Communication:
;Principal instructor: [[User:Groceryheist|Nate TeBlunthuis]]
;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 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.
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 appreciation 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 organizational 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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* Combine quantitative and qualitative data to generate critical insights into human behavior.
* Combine quantitative and qualitative data to generate critical insights into human behavior.
* Discuss and evaluate ethical, social, organizational and legal trade-offs of different data analysis, testing, curation, and sharing methods.
* Discuss and evaluate ethical, social, organizational and legal trade-offs of different data analysis, testing, curation, and sharing methods.
== Assignments ==
Your grade in this class will be assigned through:
* 6 Project assignments (50%)
* 9 Reading reflections (30%)
* Participation (20%)
=== Project assignments ===
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Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete.
Unless otherwise noted, all assignments are due before 5pm on the following week's class.
Unless otherwise noted, all assignments are individual assignments.
=== Assignment timeline ===
;Assignments due every week
* '''In-class activities - 2 points''' (weekly): In-class activity output posted to Canvas (group or individual) within 24 hours of class session.
* '''Reading reflections - 2 points''' (weekly): Reading reflections posted to Canvas (individual) before following class session.
;Scheduled assignments
* '''A1 - 5 points''' (due 10/18): Data curation (programming/analysis)
* '''A2 - 10 points''' (due 11/1): Sources of bias in data (programming/analysis)
* '''A3  - 10 points''' (due 11/8): Crowdwork Ethnography (written)
* '''A4 - 10 points''' (due 11/22): Final project plan (written)
* '''A5 - 10 points''' (due 12/6): Final project presentation (oral, slides)
* '''A6 - 15 points''' (due 12/9): Final project report (programming/analysis, written)
[[Human Centered Data Science (Fall 2018)/Assignments|more information...]]
</onlyinclude>
=== Weekly in-class activities ===
In each class session, one in-class activity will have a graded deliverable that is due the next day. The sum of these deliverables constitutes your participation grade for the course. The deliverable is intended to be something that you complete (and ideally, turn in, in class), but in rare cases may involve some work after class. It could be as simple as a picture of a design sketch you made, or notes from a group brainstorm. When you and/or your group complete the assigned activity, follow the instructions below to submit the activity and get full credit.
Love it or hate it, teamwork is an integral part of data science practice (and work in general). During some class sessions, you will be asked to participate in one or more group activities. These activities may involve reading discussions, group brainstorming activities, collaborative coding or data analysis, working together on designs, or offering peer support.
;Instructions (individual activity)
# Do the in-class activity
# Submit the deliverable via Canvas, in the format specified by the instructor within 24 hours of class
;Instructions (group activity)
# Do the in-class activity
# Before the end of class, choose one group member to submit the deliverable for the whole group
# The designated group member will submit the deliverable via Canvas, in the format specified by the instructor within 24 hours of class
::*'''''Note: Make sure to list the full names of all group members in the Canvas post!'''''
Late deliverables will never be accepted, and in the case of group activities, everyone in the group will lose points. So make sure you choose someone reliable to turn the assignment in!
=== Weekly reading reflections ===
This course will introduce you to cutting edge research and opinion from major thinkers in the domain of human centered data science. By reading and writing about this material, you will have an opportunity to explore the complex intersections of technology, methodology, ethics, and social thought that characterize this budding field of research and practice.
As a participant in the course, you are responsible for intellectually engaging with ''all assigned readings'' and developing an understanding of the ideas discussed in them.
The weekly reading reflections assignment is designed to encourage you to reflect on these works and make connections during our class discussions. To this end, you will be responsible for posting reflections on the previous week's assigned reading before the next class session.
There will generally be multiple readings assigned each week. You are responsible for reading ''all of them.'' However, you only need to write a reflection on '''one reading per week.''' Unless your instructor specifies otherwise, you can choose which reading you would like to reflect on.
These reflections are meant to be succinct but meaningful. Follow the instructions below, demonstrate that you engaged with the material, and turn the reflection in on time, and you will receive full credit. Late reading reflections will never be accepted.
;Instructions
# Read all assigned readings.
# Select a reading to reflect on.
# In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?"
# Using full sentences, list ''at least 1 question'' that this reading raised in your mind, and say ''why'' the reading caused you to ask this question.
# Post your reflection to Canvas before the next class session.
You are encouraged, but not required, to make connections between different readings (from the current week, from previous weeks, or other relevant material you've read/listened to/watched) in your reflections.
== Scheduled assignments ==
This section provides basic descriptions of all scheduled course assignments (everything you are graded on except for weekly in-class activities and reading reflections). The instructor will make specific rubrics and requirements for each of these assignments available on Canvas the day the homework is assigned.
=== A1: Data curation ===
[[File:En-wikipedia_traffic_200801-201709_thompson.png|300px|thumb|Your assignment is to create a graph that looks a lot like this one, starting from scratch, and following best practices for reproducible research.]]
The goal of this assignment is to construct, analyze, and publish a dataset of monthly traffic on English Wikipedia from January 1 2008 through September 30 2018. All analysis should be performed in a single Jupyter notebook and all data, documentation, and code should be published in a single GitHub repository.
The purpose of the assignment is to demonstrate that you can follow best practices for open scientific research in designing and implementing your project, and make your project fully reproducible by others: from data collection to data analysis.
For this assignment, you combine data about Wikipedia page traffic from two different [https://www.mediawiki.org/wiki/REST_API Wikimedia REST API] endpoints into a single dataset, perform some simple data processing steps on the data, and then analyze that data.
==== Step 0: Read about reproducibility ====
Read 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.
==== Step 1: Data acquisition ====
In order to measure Wikipedia traffic from 2008-2018, you will need to collect data from two different API endpoints, the Legacy Pagecounts API and the Pageviews API.
# The '''Legacy Pagecounts API''' ([https://wikitech.wikimedia.org/wiki/Analytics/AQS/Legacy_Pagecounts documentation], [https://wikimedia.org/api/rest_v1/#!/Pagecounts_data_(legacy)/get_metrics_legacy_pagecounts_aggregate_project_access_site_granularity_start_end endpoint]) provides access to desktop and mobile traffic data from December 2007 through July 2016.
#The '''Pageviews API''' ([https://wikitech.wikimedia.org/wiki/Analytics/AQS/Pageviews documentation], [https://wikimedia.org/api/rest_v1/#!/Pageviews_data/get_metrics_pageviews_aggregate_project_access_agent_granularity_start_end endpoint]) provides access to desktop, mobile web, and mobile app traffic data from July 2015 through last month.
For each API, you will need to collect data ''for all months where data is avaiable'' and then save the raw results into 5 separate JSON source data files (one file per API query type) before continuing to step 2.
To get you started, you can refer to this example Notebook that contains 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]). This sample code is [https://creativecommons.org/share-your-work/public-domain/cc0/ licensed CC0] so feel free to re-use any of the code in that notebook without attribution.
Your JSON-formatted source data file must contain the complete and un-edited output of your API queries. The naming convention for the source data files is:
apiname_accesstype_firstmonth-lastmonth.json
For example, your filename for monthly page views on desktop should be:
pagecounts_desktop-site_200712-201809.json
'''Important notes:'''
# As much as possible, we're interested in ''organic'' (user) traffic, as opposed to traffic by web crawlers or spiders. The Pageview API (but not the Pagecount API) allows you to filter by <tt>agent=user</tt>. You should do that.
# There was about 1 year of overlapping traffic data between the two APIs. You need to gather, and later graph, data from both APIs for this period of time.
==== Step 2: Data processing ====
You will need to perform a series of processing steps on these data files in order to prepare them for analysis. These steps must be followed exactly in order to prepare the data for analysis. At the end of this step, you will have a single CSV-formatted data file that can be used in your analysis (step 3) with no significant additional processing.
* For data collected from the Pageviews API, combine the monthly values for <tt>mobile-app</tt> and <tt>mobile-web</tt> to create a total mobile traffic count for each month.
* For all data, separate the value of <tt>timestamp</tt> into four-digit year (YYYY) and two-digit month (MM) and discard values for day and hour (DDHH).
Combine all data into a single CSV file with the following headers:
{|class="wikitable"
|-
! Column
!Value
|-
|year
|YYYY
|-
| month
|MM
|-
| pagecount_all_views
|num_views
|-
| pagecount_desktop_views
|num_views
|-
|pagecount_mobile_views
|num_views
|-
|pageview_all_views
|num_views
|-
|pageview_desktop_views
|num_views
|-
|pageview_mobile_views
|num_views
|}
For all months with 0 pageviews for a given access method (e.g. <tt>desktop-site, mobile-app</tt>), that value for that (column, month) should be listed as 0. So for example all values of <tt>pagecount_mobile_views</tt> for months before October 2014 should be 0, because mobile traffic data is not available before that month.
The final data file should be named:
en-wikipedia_traffic_200712-201809.csv
==== Step 3: Analysis ====
<!-- [[File:PlotPageviewsEN_overlap.png|200px|thumb|A sample visualization of pageview traffic data.]] -->
For this assignment, the "analysis" will be fairly straightforward: you will visualize the dataset you have created as a time series graph.
Your visualization will track three traffic metrics: mobile traffic, desktop traffic, and all traffic (mobile + desktop).
<!-- Your visualization should look similar to the example graph above, which is based on the same data you'll be using! The only big difference should be that your mobile traffic data will only go back to October 2014, since the API does not provide monthly traffic data going back to 2010. -->
In order to complete the analysis correctly and receive full credit, your graph will need to be the right scale to view the data; all units, axes, and values should be clearly labeled; and the graph should possess a key and a title. You must also generate a .png or .jpeg formatted image of your final graph.
You should graph the data in Python or R, in your notebook.
<!-- If you decide to use Google Sheet or some other open, public data visualization platform to build your graph, link to it in the README, and make sure sharing settings allow anyone who clicks on the link to view the graph and download the data! -->
==== Step 4: Documentation ====
Follow best practices for documenting your project, as outlined in the Week 3 slides and in 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''.
Your documentation will be done in your Jupyter Notebook, a README file, and a LICENSE file.
At minimum, your Jupyter Notebook should:
* Provide a short, clear description of every step in the acquisition, processing, and analysis of your data ''in full Markdown sentences'' (not just inline comments or docstrings)
At minimum, you README file should
* Describe the goal of the project.
* List the license of the source data and a link to the Wikimedia Foundation REST API terms of use: https://www.mediawiki.org/wiki/REST_API#Terms_and_conditions
* Link to all relevant API documentation
* Describe the values of all fields in your final data file.
* List any known issues or special considerations with the data that would be useful for another researcher to know. For example, you should describe that data from the Pageview API excludes spiders/crawlers, while data from the Pagecounts API does not.
==== Submission instructions ====
#Create the data-512-a1 repository on GitHub w/ your code and data.
#Complete and add your README and LICENSE file.
#Submit the link to your GitHub repo to: https://canvas.uw.edu/courses/1244514/assignments/4376106
==== Required deliverables ====
A directory in your GitHub repository called <tt>data-512-a1</tt> that contains the following files:
:# 5 source data files in JSON format that follow the specified naming convention.
:# 1 final data file in CSV format that follows the specified naming convention.
:# 1 Jupyter notebook named <tt>hcds-a1-data-curation</tt> that contains all code as well as information necessary to understand each programming step.
:# 1 README file in .txt or .md format that contains information to reproduce the analysis, including data descriptions, attributions and provenance information, and descriptions of all relevant resources and documentation (inside and outside the repo) and hyperlinks to those resources.
:# 1 LICENSE file that contains an [https://opensource.org/licenses/MIT MIT LICENSE] for your code.
:# 1 .png or .jpeg image of your visualization.
==== Helpful tips ====
* Read all instructions carefully before you begin
* Read all API documentation carefully before you begin
* Experiment with queries in the sandbox of the technical documentation  for each API to familiarize yourself with the schema and the data
* Ask questions on Slack if you're unsure about anything
* When documenting/describing your project, think: "If I found this GitHub repo, and wanted to fully reproduce the analysis, what information would I want? What information would I need?"
=== A2: Bias in data ===
The goal of this assignment is to explore the concept of bias through data on Wikipedia articles - specifically, articles on political figures from a variety of countries. For this assignment, you will combine a dataset of Wikipedia articles with a dataset of country populations, and use a machine learning service called ORES to estimate the quality of each article.
You are expected to perform an analysis of how the ''coverage'' of politicians on Wikipedia and the ''quality'' of articles about politicians varies between countries. Your analysis will consist of a series of tables that show:
# the countries with the greatest and least coverage of politicians on Wikipedia compared to their population.
# the countries with the highest and lowest proportion of high quality articles about politicians.
You are also expected to write a short reflection on the project, that describes how this assignment helps you understand the causes and consequences of bias on Wikipedia.
'''A repository with a README framework and examples of querying the ORES datastore in R and Python can be found [https://github.com/Ironholds/data-512-a2 here]'''
==== Getting the article and population data ====
The first step is getting the data, which lives in several different places. The wikipedia dataset can be found [https://figshare.com/articles/Untitled_Item/5513449 on Figshare]. Read through the documentation for this repository, then download and unzip it.
The population data is on [https://www.dropbox.com/s/5u7sy1xt7g0oi2c/WPDS_2018_data.csv?dl=0 Dropbox]. Download this data as a CSV file (hint: look for the 'Microsoft Excel' icon in the upper right).
==== Getting article quality predictions ====
Now you need to get the predicted quality scores for each article in the Wikipedia dataset. For this step, we're using a Wikimedia API endpoint for a machine learning system called [https://www.mediawiki.org/wiki/ORES ORES] ("Objective Revision Evaluation Service"). ORES estimates the quality of an article (at a particular point in time), and assigns a series of probabilities that the article is in one of 6 quality categories. The options are, from best to worst:
# FA - Featured article
# GA - Good article
# B - B-class article
# C - C-class article
# Start - Start-class article
# Stub - Stub-class article
For context, these quality classes are a sub-set of quality assessment categories developed by Wikipedia editors. If you're curious, you can read more about what these assessment classes mean on [https://en.wikipedia.org/wiki/Wikipedia:WikiProject_assessment#Grades English Wikipedia]. We will talk about what these categories mean, and how the ORES model predicts which category an article goes into, next week in class. For this assignment, you only need to know that these categories exist, and that ORES will assign one of these 6 categories to any article you send it.
The ORES API is configured fairly similarly to the pageviews API we used last assignment; documentation can be found [https://ores.wikimedia.org/v3/#!/scoring/get_v3_scores_context_revid_model here]. It expects a revision ID, which is the third column in the Wikipedia dataset, and a model, which is "wp10". The [https://github.com/Ironholds/data-512-a2 sample iPython notebooks for this assignment] provide examples of a correctly-structured API query that you can use to understand how to gather your data, and also to examine the query output.
In order to get article predictions for each article in the Wikipedia dataset, you will need to read <tt>page_data.csv</tt> into Python (or R), and then read through the dataset line by line, using the value of the <tt>last_edit</tt> column in the API query. If you're working in Python, the [https://docs.python.org/3/library/csv.html CSV module] will help with this.
When you query the API, you will notice that ORES returns a <tt>prediction</tt> value that contains the name of one category, as well as <tt>probability</tt> values for each of the 6 quality categories. For this assignment, you only need to capture and use the value for <tt>prediction</tt>. We'll talk more about what the other values mean in class next week.
==== Combining the datasets ====
Some processing of the data will be necessary! In particular, you'll need to - after retrieving and including the ORES data for each article - merge the wikipedia data and population data together. Both have fields containing country names for just that purpose. After merging the data, you'll invariably run into entries which ''cannot'' be merged. Either the population dataset does not have an entry for the equivalent Wikipedia country, or vice versa. You will need to remove the rows that do not have matching data.
Consolidate the remaining data into a single CSV file which looks something like this:
{|class="wikitable"
|-
! Column
|-
|country
|-
|article_name
|-
|revision_id
|-
|article_quality
|-
|population
|}
Note: <tt>revision_id</tt> here is the same thing as <tt>last_edit</tt>, which you used to get scores from the ORES API.
==== Analysis ====
Your analysis will consist of calculating the proportion (as a percentage) of articles-per-population and high-quality articles for each country. By "high quality" articles, in this case we mean the number of articles about politicians in a given country that ORES predicted would be in either the "FA" (featured article) or "GA" (good article) classes.
Examples:
* if a country has a population of 10,000 people, and you found 10 articles about politicians from that country, then the percentage of articles-per-population would be .1%.
* if a country has 10 articles about politicians, and 2 of them are FA or GA class articles, then the percentage of high-quality articles would be 20%.
==== Tables ====
The tables should be pretty straightforward. Produce four tables that show:
#10 highest-ranked countries in terms of number of politician articles as a proportion of country population
#10 lowest-ranked countries in terms of number of politician articles as a proportion of country population
#10 highest-ranked countries in terms of number of GA and FA-quality articles as a proportion of all articles about politicians from that country
#10 lowest-ranked countries in terms of number of GA and FA-quality articles as a proportion of all articles about politicians from that country
Embed them in the iPython notebook.
==== Writeup ====
Write a few paragraphs, either in the README or in the notebook, reflecting on what you have learned, what you found, what (if anything) surprised you about your findings, and/or what theories you have about why any biases might exist (if you find they exist). You can also include any questions this assignment raised for you about bias, Wikipedia, or machine learning. Particular questions you might want to answer:
# What biases did you expect to find in the data, and why?
# What are the results?
# What theories do you have about why the results are what they are?
==== Submission instructions ====
#Complete your Notebook and datasets in Jupyter Hub.
#Create the data-512-a2 repository on GitHub w/ your code and data.
#Complete and add your README and LICENSE file.
#Submit the link to your GitHub repo to: https://canvas.uw.edu/courses/1244514/assignments/4376107
==== Required deliverables ====
A directory in your GitHub repository called <tt>data-512-a2</tt> that contains the following files:
:# 1 final data file in CSV format that follows the formatting conventions.
:# 1 Jupyter notebook named <tt>hcds-a2-bias</tt> that contains all code as well as information necessary to understand each programming step, as well as your writeup (if you have not included it in the README) and the tables.
:# 1 README file in .txt or .md format that contains information to reproduce the analysis, including data descriptions, attributions and provenance information, and descriptions of all relevant resources and documentation (inside and outside the repo) and hyperlinks to those resources, and your writeup (if you have not included it in the notebook). A prototype framework is included in the [https://github.com/Ironholds/data-512-a2 sample repository]
:# 1 LICENSE file that contains an [https://opensource.org/licenses/MIT MIT LICENSE] for your code.
==== Helpful tips ====
* Read all instructions carefully before you begin
* Read all API documentation carefully before you begin
* Experiment with queries in the sandbox of the technical documentation for the API to familiarize yourself with the schema and the data
* Explore the data a bit before starting to be sure you understand how it is structured and what it contains
* Ask questions on Slack if you're unsure about anything. Please email Os to set up a meeting, or come to office hours, if you want to! This time is set aside specifically for you - it is not an imposition.
* When documenting/describing your project, think: "If I found this GitHub repo, and wanted to fully reproduce the analysis, what information would I want? What information would I need?"
=== A3: Crowdwork ethnography ===
For this assignment, you will go undercover as a member of the Amazon Mechanical Turk community. You will preview or perform Mechanical Turk tasks (called "HITs"), lurk in Turk worker discussion forums, and write an ethnographic account of your experience as a crowdworker, and how this experience changes your understanding of the phenomenon of crowdwork.
The full assignment description is available [https://docs.google.com/document/d/16lZdTxkw1meUPMzA-BYl8TVtk0Jxv4Wh8mbZq_BursM/edit?usp=sharing as a Google doc] and [[:File:HCDS_Crowdwork_ethnography_instructions.pdf|as a PDF]].
=== A4: Final project plan ===
''For examples of datasets you may want to use for your final project, see [[HCDS_(Fall_2017)/Datasets]].''
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, including what 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.
[[Category:HCDS (Fall 2018)]]


== Schedule ==
== Schedule ==
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;Assignments due
;Assignments due
* Fill out the pre-course survey
<!-- * Fill out the pre-course survey -->
* Attend week 1 of CDSW
* Read: Provost, Foster, and Tom Fawcett. [http://online.liebertpub.com/doi/pdf/10.1089/big.2013.1508 ''Data science and its relationship to big data and data-driven decision making.''] Big Data 1.1 (2013): 51-59.
* Read: Provost, Foster, and Tom Fawcett. [http://online.liebertpub.com/doi/pdf/10.1089/big.2013.1508 ''Data science and its relationship to big data and data-driven decision making.''] Big Data 1.1 (2013): 51-59.


Line 409: Line 64:
;Readings assigned
;Readings assigned
* Read: Barocas, Solan and Nissenbaum, Helen. [https://www.nyu.edu/projects/nissenbaum/papers/BigDatasEndRun.pdf ''Big Data's End Run around Anonymity and Consent'']. In ''Privacy, Big Data, and the Public Good''. 2014.
* Read: Barocas, Solan and Nissenbaum, Helen. [https://www.nyu.edu/projects/nissenbaum/papers/BigDatasEndRun.pdf ''Big Data's End Run around Anonymity and Consent'']. In ''Privacy, Big Data, and the Public Good''. 2014.
* 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


;Homework assigned
;Homework assigned
* Reading reflection
* Week 2 reading reflection
* Attend week 2 of CDSW
* Attend week 1 of CDSW


<!-- ;Resources -->
<!-- ;Resources -->
* 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


<!-- * Aragon, C. et al. (2016). [https://cscw2016hcds.files.wordpress.com/2015/10/cscw_2016_human-centered-data-science_workshop.pdf ''Developing a Research Agenda for Human-Centered Data Science.''] Human Centered Data Science workshop, CSCW 2016. -->
<!-- * Aragon, C. et al. (2016). [https://cscw2016hcds.files.wordpress.com/2015/10/cscw_2016_human-centered-data-science_workshop.pdf ''Developing a Research Agenda for Human-Centered Data Science.''] Human Centered Data Science workshop, CSCW 2016. -->
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;Assignments due
;Assignments due
* Week 1 reading reflection
* Week 2 reading reflection
*  
* Attend week 1 of CDSW


<!-- ;Agenda -->
<!-- ;Agenda -->
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;Homework assigned
;Homework assigned
* Reading reflection
* Week 3 reading reflection
* Attend week 2 of CDSW
* Attend week 2 of CDSW
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|Assignment 1: Data curation]]


<!-- ;Resources -->
<!-- ;Resources -->
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;Assignments due
;Assignments due
* Week 2 reading reflection
* Week 3 reading reflection
* Attend week 2 of CDSW
* Attend week 2 of CDSW
<!-- ;Agenda -->
<!-- ;Agenda -->
<!-- {{:HCDS (Fall 2018)/Day 3 plan}} -->
<!-- {{:HCDS (Fall 2018)/Day 3 plan}} -->
Line 485: Line 138:


;Homework assigned
;Homework assigned
* Reading reflection
* Week 4 reading reflection
* Attend week 3 of CDSW
* Attend week 3 of CDSW
* A1: Project proposal and data aquisition


<!-- ;Resources -->
<!-- ;Resources -->
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<!-- * Hickey, Walt. [https://fivethirtyeight.com/features/the-bechdel-test-checking-our-work/ ''The Bechdel Test: Checking Our Work'']. FiveThirtyEight, 2014. -->
<!-- * Hickey, Walt. [https://fivethirtyeight.com/features/the-bechdel-test-checking-our-work/ ''The Bechdel Test: Checking Our Work'']. FiveThirtyEight, 2014. -->
<!-- * 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. -->
<!-- <\!--  -->
<!-- <\!--  -->
<!-- * 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 -->
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<!-- * 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. -->
<!-- -\->  -->
<!-- -\->  -->
<!-- *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.  -->
<!-- * 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/>
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;Assignments due
;Assignments due
* Reading reflection
* Week 4 reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|A1: Data curation]]
* Attend week 3 of CDSW
*  A1: Project proposal and data aquisition


<!-- ;Agenda -->
<!-- ;Agenda -->
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;Homework assigned
;Homework assigned
* Reading reflection
* Week 5 reading reflection
 
* A2: Data analysis (due week 6)
 
 
<!-- ;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 4 reading reflection
* Week 5 reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|A1: Data curation]]
* Assignment 1: Project proposal and data aquisition
 


; Readings assigned
; Readings assigned
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; Homework Assigned
; Homework Assigned
* Reading reflection
* Week 6 reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A2:_Bias_in_data|A2: Bias in data]]
* A2: Data analysis (due week 6)
 
<br/>
<br/>
<hr/>
<hr/>
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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''


; Assignments due
; Assignments due
* Week 5 reading reflection
* Week 6 reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A2:_Bias_in_data|A2: Bias in data]]
* A2: Data analysis
 
;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)


; Homework assigned
* Week 7 reading reflection
* A3: Final project proposal
<br/>
<br/>
<hr/>
<hr/>
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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; crowdsourcing''
;Introduction to mixed-methods research: ''Big data vs thick data; integrating qualitative research methods into data science practice; crowd-sourcing''




;Assignments due
;Assignments due
* Reading reflection
* Week 7 reading reflection
 
* A3: Final project proposal


<!-- ;Agenda -->
<!-- ;Agenda -->
Line 603: Line 252:


;Homework assigned
;Homework assigned
* Reading reflection
* Week 8 reading reflection
<!-- * [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A3:_Crowdwork_ethnography|A3: Crowdwork ethnography]] -->
* A4: Final presentation (due week 11)
 
 
<!-- ;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 -->
<br/>
<br/>
<hr/>
<hr/>
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;Assignments due
;Assignments due
* Reading reflection
* Week 8 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
* Week 9 Reading reflection
 
* A4: Final presentation (due week 11)
<!-- ;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] -->


<br/>
<br/>
<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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<!-- ;Assignments due -->
<!-- ;Assignments due -->
<!-- * Reading reflection -->
<!-- * Reading reflection -->
<!-- <\!-- * [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A3:_Crowdwork_ethnography|A3: Crowdwork ethnography]] -\-> -->
<!-- <\!-- ;Agenda -\-> -->
<!-- <\!-- ;Agenda -\-> -->
<!-- <\!-- {{:HCDS (Fall 2018)/Day 7 plan}} -\-> -->
<!-- <\!-- {{:HCDS (Fall 2018)/Day 7 plan}} -\-> -->
Line 706: Line 341:
<!-- <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
* Week 9 reading reflection
* A4: Final project plan
 
<!-- ;Agenda -->
<!-- ;Agenda -->
<!-- {{:HCDS (Fall 2018)/Day 9 plan}} -->
<!-- {{:HCDS (Fall 2018)/Day 9 plan}} -->
Line 723: Line 353:


;Homework assigned
;Homework assigned
* A4: Final presentation (due week 11)
* 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
* Week 10 reading reflection


<!-- ;Agenda -->
<!-- ;Agenda -->
Line 751: Line 378:


;Homework assigned
;Homework assigned
* A5: Final presentation
* A4: 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. -->
<br/>
<br/>
<hr/>
<hr/>
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;Assignments due
;Assignments due
* A5: Final presentation
* A4: 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)
* A5: 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
* A5.: FINAL PROJECT REPORT DUE BY 11:59PM
<!-- * LATE PROJECT SUBMISSIONS NOT ACCEPTED. -->
<!-- * LATE PROJECT SUBMISSIONS NOT ACCEPTED. -->


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</div>
</div>


== Assignments and coursework  ==
Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete.
Unless otherwise noted, all assignments are due before 5pm on the following week's class.
Unless otherwise noted, all assignments are individual assignments.
=== Weekly reading reflections ===
This course will introduce you to cutting edge research and opinion from major thinkers in the domain of human centered data science. By reading and writing about this material, you will have an opportunity to explore the complex intersections of technology, methodology, ethics, and social thought that characterize this budding field of research and practice.
As a participant in the course, you are responsible for intellectually engaging with ''all assigned readings'' and developing an understanding of the ideas discussed in them.
The weekly reading reflections assignment is designed to encourage you to reflect on these works and make connections during our class discussions. To this end, you will be responsible for posting reflections on the previous week's assigned reading before the next class session.
There will generally be multiple readings assigned each week. You are responsible for reading ''all of them.'' However, you only need to write a reflection on '''one reading per week.''' Unless your instructor specifies otherwise, you can choose which reading you would like to reflect on.
These reflections are meant to be succinct but meaningful. Follow the instructions below, demonstrate that you engaged with the material, and turn the reflection in on time, and you will receive full credit. Late reading reflections will never be accepted.
;Instructions
# Read all assigned readings.
# Select a reading to reflect on.
# In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?"
# Using full sentences, list ''at least 1 question'' that this reading raised in your mind, and say ''why'' the reading caused you to ask this question.
# Post your reflection to Canvas before the next class session.
You are encouraged, but not required, to make connections between different readings (from the current week, from previous weeks, or other relevant material you've read/listened to/watched) in your reflections.
=== Project Assignments ===
This section provides basic descriptions of all scheduled course assignments.
In assignments 1 and 2 you will build on the skills you'll learn in the community data science workshop to analyze data of your own substantive interests. The goals are to reinforce learning from the workshop and to give you hands on experience that will help you think about how data science might apply to your own community or organization. 
Assignments 3, 4, and 5 scaffold your final project for the course in which you will conduct a qualitative study of data science in an organizational context. I strongly recommend for you to make arrangements to conduct observations and interviews with a data science team as soon as possible.
=== A1: Project proposal and data aquisition ===
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 the data can help answer, and explain how you will answer them.
:* What results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself).
:* 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 | see this page]] with examples of freely available datasets that you can use for this project.
==== Evaluation and Rubric ====
:''Dataset identification:'' 20%
:''Explaination of data source:'' 20%
:''Example questions:'' 20%
:''Anticipated results and their significance:'' 20%
:''Summary  statistics:'' 20%
=== A2: Data analysis ===
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? Communicate 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?
* 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 underlie your interpretation of it?  How might (or might not) this analysis influence or mislead its audiences? 
==== Evaluation and Rubric ====
:''Presentation of data analysis:'' 40%
:''Appropriate chart or table:'' 5%
:''Explanation of applicability to community or organization:'' 20%
:''Critique in terms of improving the analysis :'' 10%
:''Critique in terms of application :'' 10%
:''Writing quality (see [[User:Benjamin Mako Hill/Assessment#Writing Rubric | the writing rubric]]): 15%''
=== A3: Final project plan ===
For this assignment, you will write up a study plan for your final class project. The goal of this project is for you to apply what you have learned about data science studies to understanding and improving data science practice in an organizational context. The plan will cover a variety of details about your final project.
Specifically your plan should:
* 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.
* Identify a research question that you don't already know the answer to, but where project can realistically help you answer it.  This should be specific and tied to a particular aspect of how data science is practiced or is used in this organization. Effective research questions will often raise issues or problems with the organization. 
* 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).
A reasonable amount of data collection for this project is about 4 30 minutes interviews with different team members, about 4 hours of workplace observation, or an equivalent combination of interviews and observation.
Maximum length: 1500 words.
==== Evaluation and Rubric ====
:''Organization and identification:'' 20%
:''Summary of what you already know:'' 20%
:''Research question:'' 20%
:''Data collection plan:'' 20%
:''Anticipated results and their significance:'' 20%
=== A4: 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:
:* What organization or team you studied
:* Your research question about how data science functions in this organization
:* Your findings: what you learned about your research question from your data in relation to course material.
:* Who you observed or interviewed
:* At least one quotation or anecdote from your qualitative data that support your findings
:* Tentative recommendations to the organization based on your findings
==== Evaluation and Rubric ====
:''Presentation organization and design:'' 15%
:''Explaination and justification of a research question:'' 15%
:''Presentation of evidence:'' 25%
:''Findings:'' 25%
:''Recommendations to the organization:'' 20%
=== A5: Final project report ===
In the final report, I expect you to take feedback from your presentation and and report on your project in up to 3000 words. You can organize your paper however you want, but it should do the following:
:* Introduce the organization or team you studied
:* Document how you collected data with the team (who you interviewed or observed, for how long, describe their jobs and roles).
:* Motivate your project in terms of your substantive interest, curiosity, and course concepts.
:* Introduce and articulate a specific research question about how data science functions in this organization. This will often be driven by a particular challenge or issue the team you are studying faces.
:*  The bulk of your report (about 2000 words) should argue for an understanding of the research question based on
::* Your empirical findings: what you learned about the organization and the practice of data science from your own observations or interviews. Use anecdotes and quotes as appropriate to support your argument.
::* Any course material relevant to the challenge or issue at hand.
:* Make recommendations to the organization based on your findings.
Optionally, you may make recommendations to me about the course material in relation to this project.  Was there anything that you expected to see based on the course material that you didn't observe? Was there anything interesting that you observed that the course didn't address? How might you improve the course given what you learned?
==== Evaluation and Rubric ====
:''Writing quality (see [[User:Benjamin Mako Hill/Assessment#Writing Rubric | the writing rubric]]): 15%''
:''Explaination and justification of a research question:'' 15%
:''Presentation of evidence:'' 25%
:''Findings:'' 25%
:''Recommendations to the organization:'' 20%
:''Course feedback (Extra credit):'' 3%


== Policies ==
== Policies ==
The following general policies apply to this course.
The following general policies apply to this course.
=== Grades ===
Grades will be determined as follows:
* 20% Participation
* 20% Reading reflections
* 5% Midterm proposal
* 15% Midterm report
* 5% Final project proposal
* 10% Final project presentation
* 25% Final project report
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.


=== Attendance ===
=== Attendance ===


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.  
As detailed in [[User:Benjamin Mako Hill/Assessment | this 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 ===  
Line 861: Line 635:
=== 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 individial lectures for students who have learning differences that make audiovisual notes preferable to written ones.
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 individual 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.


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].
=== Assignments and coursework ===
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]]
[[Category:Groceryheist drafts]]

Latest revision as of 23:20, 10 November 2020

Course Title
Data Science and Organizational Communication:
Instructor
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 Description[edit]

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 appreciation 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 organizational 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.

This version of the syllabus is designed around a weekly schedule.

Learning Objectives[edit]

By the end of this course, students will be able to:

  • Understand what it means to analyze large and complex data effectively and ethically with an understanding of human, societal, organizational, and socio-technical contexts.
  • Consider the account ethical, social, organizational, and legal considerations of data science in organizational and institutional contexts.
  • Combine quantitative and qualitative data to generate critical insights into human behavior.
  • Discuss and evaluate ethical, social, organizational and legal trade-offs of different data analysis, testing, curation, and sharing methods.

Schedule[edit]

Course schedule (click to expand)

This page is a work in progress.




Week 1[edit]

Introduction to Human Centered Data Science
What is data science? What is human centered? What is human centered data science?
Assignments due


Readings assigned
Homework assigned
  • Week 2 reading reflection
  • Attend week 1 of CDSW




Week 2[edit]

Ethical considerations
privacy, informed consent and user treatment
Assignments due
  • Week 2 reading reflection
  • Attend week 1 of CDSW


Readings assigned
Homework assigned
  • Week 3 reading reflection
  • Attend week 2 of CDSW




Week 3[edit]

Reproducibility and Accountability
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 3 reading reflection
  • Attend week 2 of CDSW
Readings assigned
Homework assigned
  • Week 4 reading reflection
  • Attend week 3 of CDSW
  • A1: Project proposal and data aquisition



Week 4[edit]

Interrogating datasets
causes and consequences of bias in data; best practices for selecting, describing, and implementing training data


Assignments due
  • Week 4 reading reflection
  • Attend week 3 of CDSW
  • A1: Project proposal and data aquisition


Readings assigned (Read both, reflect on one)
  • Barley, S. R. (1986). Technology as an occasion for structuring: evidence from observations of ct scanners and the social order of radiology departments. Administrative Science Quarterly, 31(1), 78–108.
  • Orlikowski, W. J., & Barley, S. R. (2001). Technology and institutions: what can research on information technology and research on organizations learn from each other? MIS Q., 25(2), 145–165. https://doi.org/10.2307/3250927
Homework assigned
  • Week 5 reading reflection
  • A2: Data analysis (due week 6)




Week 5[edit]

Technology and Organizing
Assignments due
  • Week 5 reading reflection
  • Assignment 1: Project proposal and data aquisition
Readings assigned
  • Passi, S., & Jackson, S. J. (2018). Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects. Proc. ACM Hum.-Comput. Interact., 2(CSCW), 136:1–136:28. https://doi.org/10.1145/3274405
Homework Assigned
  • Week 6 reading reflection
  • A2: Data analysis (due week 6)



Week 6[edit]

Data science in Organizational Contexts

And a crash course on qualitative research

Assignments due
  • Week 6 reading reflection
  • A2: Data analysis
Readings assigned (Read both, reflect on one)
Homework assigned
  • Week 7 reading reflection
  • A3: Final project proposal



Week 7[edit]

Introduction to mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowd-sourcing


Assignments due
  • Week 7 reading reflection
  • A3: Final project proposal


Readings assigned (Read both, reflect on one)


Homework assigned
  • Week 8 reading reflection
  • A4: Final presentation (due week 11)



Week 8[edit]

Algorithms
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Week 8 Reading reflection
Readings assigned
Homework assigned
  • Week 9 Reading reflection
  • A4: Final presentation (due week 11)



Week 9[edit]

Data science for social good
Community-based and participatory approaches to data science; Using data science for society's benefit
Assignments due
  • Week 9 reading reflection
Readings assigned
Homework assigned
  • A4: Final presentation (due week 11)
  • Reading reflection
Resources




Week 10[edit]

User experience and big data
Design considerations for machine learning applications; human centered data visualization; data storytelling
Assignments due
  • Week 10 reading reflection


Readings assigned
  • NONE
Homework assigned
  • A4: Final presentation



Week 11[edit]

Final presentations
course wrap up, presentation of student projects


Assignments due
  • A4: Final presentation
Readings assigned
  • none!
Homework assigned
  • A5: Final project report (by 11:59pm)




Week 12: Finals Week (No Class Session)[edit]

  • NO CLASS
  • A5.: FINAL PROJECT REPORT DUE BY 11:59PM


Assignments and coursework[edit]

Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete.

Unless otherwise noted, all assignments are due before 5pm on the following week's class.

Unless otherwise noted, all assignments are individual assignments.

Weekly reading reflections[edit]

This course will introduce you to cutting edge research and opinion from major thinkers in the domain of human centered data science. By reading and writing about this material, you will have an opportunity to explore the complex intersections of technology, methodology, ethics, and social thought that characterize this budding field of research and practice.

As a participant in the course, you are responsible for intellectually engaging with all assigned readings and developing an understanding of the ideas discussed in them.

The weekly reading reflections assignment is designed to encourage you to reflect on these works and make connections during our class discussions. To this end, you will be responsible for posting reflections on the previous week's assigned reading before the next class session.

There will generally be multiple readings assigned each week. You are responsible for reading all of them. However, you only need to write a reflection on one reading per week. Unless your instructor specifies otherwise, you can choose which reading you would like to reflect on.

These reflections are meant to be succinct but meaningful. Follow the instructions below, demonstrate that you engaged with the material, and turn the reflection in on time, and you will receive full credit. Late reading reflections will never be accepted.

Instructions
  1. Read all assigned readings.
  2. Select a reading to reflect on.
  3. In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?"
  4. Using full sentences, list at least 1 question that this reading raised in your mind, and say why the reading caused you to ask this question.
  5. Post your reflection to Canvas before the next class session.

You are encouraged, but not required, to make connections between different readings (from the current week, from previous weeks, or other relevant material you've read/listened to/watched) in your reflections.


Project Assignments[edit]

This section provides basic descriptions of all scheduled course assignments.

In assignments 1 and 2 you will build on the skills you'll learn in the community data science workshop to analyze data of your own substantive interests. The goals are to reinforce learning from the workshop and to give you hands on experience that will help you think about how data science might apply to your own community or organization.

Assignments 3, 4, and 5 scaffold your final project for the course in which you will conduct a qualitative study of data science in an organizational context. I strongly recommend for you to make arrangements to conduct observations and interviews with a data science team as soon as possible.

A1: Project proposal and data aquisition[edit]

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 the data can help answer, and explain how you will answer them.
  • What results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself).
  • 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, see this page with examples of freely available datasets that you can use for this project.

Evaluation and Rubric[edit]

Dataset identification: 20%
Explaination of data source: 20%
Example questions: 20%
Anticipated results and their significance: 20%
Summary statistics: 20%

A2: Data analysis[edit]

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? Communicate 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?
  • 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 underlie your interpretation of it? How might (or might not) this analysis influence or mislead its audiences?

Evaluation and Rubric[edit]

Presentation of data analysis: 40%
Appropriate chart or table: 5%
Explanation of applicability to community or organization: 20%
Critique in terms of improving the analysis : 10%
Critique in terms of application : 10%
Writing quality (see the writing rubric): 15%


A3: Final project plan[edit]

For this assignment, you will write up a study plan for your final class project. The goal of this project is for you to apply what you have learned about data science studies to understanding and improving data science practice in an organizational context. The plan will cover a variety of details about your final project.

Specifically your plan should:

  • 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.
  • Identify a research question that you don't already know the answer to, but where project can realistically help you answer it. This should be specific and tied to a particular aspect of how data science is practiced or is used in this organization. Effective research questions will often raise issues or problems with the organization.
  • 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).

A reasonable amount of data collection for this project is about 4 30 minutes interviews with different team members, about 4 hours of workplace observation, or an equivalent combination of interviews and observation.

Maximum length: 1500 words.

Evaluation and Rubric[edit]

Organization and identification: 20%
Summary of what you already know: 20%
Research question: 20%
Data collection plan: 20%
Anticipated results and their significance: 20%


A4: Final project presentation[edit]

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 organization or team you studied
  • Your research question about how data science functions in this organization
  • Your findings: what you learned about your research question from your data in relation to course material.
  • Who you observed or interviewed
  • At least one quotation or anecdote from your qualitative data that support your findings
  • Tentative recommendations to the organization based on your findings

Evaluation and Rubric[edit]

Presentation organization and design: 15%
Explaination and justification of a research question: 15%
Presentation of evidence: 25%
Findings: 25%
Recommendations to the organization: 20%


A5: Final project report[edit]

In the final report, I expect you to take feedback from your presentation and and report on your project in up to 3000 words. You can organize your paper however you want, but it should do the following:

  • Introduce the organization or team you studied
  • Document how you collected data with the team (who you interviewed or observed, for how long, describe their jobs and roles).
  • Motivate your project in terms of your substantive interest, curiosity, and course concepts.
  • Introduce and articulate a specific research question about how data science functions in this organization. This will often be driven by a particular challenge or issue the team you are studying faces.
  • The bulk of your report (about 2000 words) should argue for an understanding of the research question based on
  • Your empirical findings: what you learned about the organization and the practice of data science from your own observations or interviews. Use anecdotes and quotes as appropriate to support your argument.
  • Any course material relevant to the challenge or issue at hand.
  • Make recommendations to the organization based on your findings.

Optionally, you may make recommendations to me about the course material in relation to this project. Was there anything that you expected to see based on the course material that you didn't observe? Was there anything interesting that you observed that the course didn't address? How might you improve the course given what you learned?

Evaluation and Rubric[edit]

Writing quality (see the writing rubric): 15%
Explaination and justification of a research question: 15%
Presentation of evidence: 25%
Findings: 25%
Recommendations to the organization: 20%
Course feedback (Extra credit): 3%

Policies[edit]

The following general policies apply to this course.

Grades[edit]

Grades will be determined as follows:

  • 20% Participation
  • 20% Reading reflections
  • 5% Midterm proposal
  • 15% Midterm report
  • 5% Final project proposal
  • 10% Final project presentation
  • 25% Final project report

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.

Attendance[edit]

As detailed in this 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[edit]

Students are expected to treat each other, and the instructors, with respect. Students are prohibited from engaging in any kind of harassment or derogatory behavior, which includes offensive verbal comments or imagery related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, or religion. In addition, students should not engage in any form of inappropriate physical contact or unwelcome sexual attention, and should respect each others’ right to privacy in regards to their personal life. In the event that you feel you (or another student) have been subject to a violation of this policy, please reach out to the instructors in whichever form you prefer.

The instructors are committed to providing a safe and healthy learning environment for students. As part of this, students are asked not to wear any clothing, jewelry, or any related medium for symbolic expression which depicts an indigenous person or cultural expression re­appropriated as a mascot, logo, or caricature. These include, but are not limited to, iconography associated with the following sports teams:

  1. Chicago Blackhawks
  2. Washington Redskins
  3. Cleveland Indians
  4. Atlanta Braves


Devices in Class[edit]

Electronic devices (e.g., phones, tablets, laptops) are not going to permitted in class. If you have a documented need to use a device, please contact me ahead of time to let me know. If you do get permission to use a device, I will ask you to sit in the very back of the classroom.

The goal of this policy is to help you stay focused and avoid distractions for yourself and your peers in the classroom. This is really important and turns out to be much more difficult in the presence of powerful computing devices with brightly glowing screens and fast connections to the Internet. For more on the rationale behind this policy, please read Clay Shirky’s thoughtful discussion of his approach to this issue.


Electronic Mail Standards of Conduct[edit]

Email communications (and all communications generally) among UW community members should seek to respect the rights and privileges of all members of the academic community. This includes not interfering with university functions or endangering the health, welfare, or safety of other persons. With this in mind, in addition to the University of Washington's Student Conduct Code, I establishes the following standards of conduct in respect to electronic communications among students and faculty:

  • If, as a student, you have a question about course content or procedures, please use the online discussion board designed for this purpose. If you have specific questions about your performance, contact me directly.
  • I strive to respond to Email communications within 48 hours. If you do not hear from me, please come to my office, call me, or send me a reminder Email.
  • Email communications should be limited to occasional messages necessary to the specific educational experience at hand.
  • Email communications should not include any CC-ing of anyone not directly involved in the specific educational experience at hand.
  • Email communications should not include any blind-CC-ing to third parties, regardless of the third party’s relevance to the matter at hand.


Academic integrity and plagiarism[edit]

As a University of Washington student, you are expected to practice high standards of academic honesty and integrity. You are responsible to understand and abide by UW’s Student Governance Code on Academic Misconduct, and the UW’s Administrative Code on Academic Misconduct, and to comply with verbal or written instructions from the professor or TA of this course. This includes plagiarism, which is a serious offense. All assignments will be reviewed for integrity. All rules regarding academic integrity extend to electronic communication and the use of online sources. If you are not sure what constitutes plagiarism, read this overview in addition to UW’s policy statements.

I am committed to upholding the academic standards of the University of Washington’s Student Conduct Code. If I suspect a student violation of that code, I will first engage in a conversation with that student about my concerns. If we cannot successfully resolve a suspected case of academic misconduct through our conversations, I will refer the situation to the department of communication advising office who can then work with the COM Chair to seek further input and if necessary, move the case up through the College.

While evidence of academic misconduct may result in a lower grade, I will not unilaterally lower a grade without addressing the issue with you first through the process outlined above.

Other academic integrity resources:

Notice: The University has a license agreement with VeriCite, an educational tool that helps prevent or identify plagiarism from Internet resources. Your instructor may use the service in this class by requiring that assignments are submitted electronically to be checked by VeriCite. The VeriCite Report will indicate the amount of original text in your work and whether all material that you quoted, paraphrased, summarized, or used from another source is appropriately referenced.


Disability and accommodations[edit]

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 individual 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.

For more information on disability accommodations, and how to apply for one, please review UW's Disability Resources for Students.

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