Editing Data Into Insights (Spring 2021)
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:'''Instructor:''' [https://jeremydfoote.com Jeremy Foote] | :'''Instructor:''' [https://jeremydfoote.com Jeremy Foote] | ||
:'''Email:''' jdfoote@purdue.edu | :'''Email:''' jdfoote@purdue.edu | ||
:''' | :'''Office Hours:''' Fridays 10am-noon and by appointment | ||
<div style="float:right;">__TOC__</div> | <div style="float:right;">__TOC__</div> | ||
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** '''Data Visualization: A Practical Introduction''' by Kieran Healy. [https://socviz.co/index.html Web version (free!)] or [https://amzn.to/2vfAixM Print version (Amazon)] | ** '''Data Visualization: A Practical Introduction''' by Kieran Healy. [https://socviz.co/index.html Web version (free!)] or [https://amzn.to/2vfAixM Print version (Amazon)] | ||
** '''R for Data Science''' by Hadley Wickham and Garrett Grolemund. [https://r4ds.had.co.nz/index.html Web version (free!)] or [http://amzn.to/2aHLAQ1 Print version (Amazon)] | ** '''R for Data Science''' by Hadley Wickham and Garrett Grolemund. [https://r4ds.had.co.nz/index.html Web version (free!)] or [http://amzn.to/2aHLAQ1 Print version (Amazon)] | ||
** '''Effective Data Storytelling''' by Brent Dykes. | ** '''Effective Data Storytelling''' by Brent Dykes. [https://smile.amazon.com/dp/1119615712 Print version (Amazon)] | ||
* Other readings: Readings will be linked to from this page. Where necessary, they will be put on Brightspace | * Other readings: Readings will be linked to from this page. Where necessary, they will be put on Brightspace | ||
= Course logistics = | = Course logistics = | ||
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This course will follow "flipped" classroom model. I expect you to learn most of the content of the course asynchronously. The goal of our time together is not to tell you new things, but to consolidate knowledge and to clear up misconceptions. | This course will follow "flipped" classroom model. I expect you to learn most of the content of the course asynchronously. The goal of our time together is not to tell you new things, but to consolidate knowledge and to clear up misconceptions. | ||
The Tuesday meeting will be a collaborative, discussion-centric session. Typically, about half of each session will be devoted to going over assignments and the other half will be a discussion of the readings and videos from that week | The Tuesday meeting will be a collaborative, discussion-centric session. Typically, about half of each session will be devoted to going over assignments and the other half will be a discussion of the readings and videos from that week. | ||
The Thursday meetings will be more like a lab. Some of these sessions will include synchronous activities but they will often be more of a co-working time, where you can work synchronously on assignments and I can be available to answer questions. | The Thursday meetings will be more like a lab. Some of these sessions will include synchronous activities but they will often be more of a co-working time, where you can work synchronously on assignments and I can be available to answer questions. | ||
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Your first place to look for help should be each other. By asking and answering questions on Discord, you will not only help to build a repository of shared information, but to reinforce our learning community. | Your first place to look for help should be each other. By asking and answering questions on Discord, you will not only help to build a repository of shared information, but to reinforce our learning community. | ||
I will also hold office hours Friday mornings on Discord ([[ | I will also hold office hours Friday mornings on Discord ([[JFoote Office Hours|Sign up here]]). If you come with a programming question, I will expect that you have already tried to solve it yourself in multiple ways and that you have discussed it with a classmate (e.g., on Discord). This policy lets me have time to help more students, but it's also a useful strategy. Often [https://en.wikipedia.org/wiki/Rubber_duck_debugging just trying to explain your code] can help you to recognize where you've gone wrong. | ||
I will also keep an eye on Discord during normal business hours. I encourage you to post questions there, and to use it as a space where we can help and instruct each other. In general, you should contact me there. I am also available by email. You can reach me at [mailto:jdfoote@purdue.edu jdfoote@purdue.edu]. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours. | I will also keep an eye on Discord during normal business hours. I encourage you to post questions there, and to use it as a space where we can help and instruct each other. In general, you should contact me there. I am also available by email. You can reach me at [mailto:jdfoote@purdue.edu jdfoote@purdue.edu]. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours. | ||
= Assignments = | = Assignments = | ||
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== Discussion Questions == | == Discussion Questions == | ||
This course will have two "modes". For much of the class, we will be reading about theories of communication and rhetoric, about principles of data visualization, etc. For these sessions, you will be required to submit 1-2 discussion questions on Discord on Monday by noon. I will then curate some of these questions (and add some of my own) to use to guide our discussion on Tuesday. | This course will have two "modes". For much of the class, we will be reading about theories of communication and rhetoric, about principles of data visualization, etc. For these sessions, you will be required to submit 1-2 discussion questions on Discord on Monday by noon. I will then curate some of these questions (and add some of my own) to use to guide our discussion on Tuesday. | ||
Questions should engage with the readings and either connect to other concepts or to the "real world". Here are some good example questions: | Questions should engage with the readings and either connect to other concepts or to the "real world". Here are some good example questions: | ||
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* The readings this week talked a lot about how data visualizations can be misleading. How can we tell when visualizations are intentionally trying to mislead versus when they are just poorly designed? | * The readings this week talked a lot about how data visualizations can be misleading. How can we tell when visualizations are intentionally trying to mislead versus when they are just poorly designed? | ||
* I was confused by the reading on counterfactuals. We obviously can't really know what would have happened in different conditions, so why even try? | * I was confused by the reading on counterfactuals. We obviously can't really know what would have happened in different conditions, so why even try? | ||
* Imagine you were asked to create an ad campaign to recruit students to Purdue. What types of appeals would you use and why? | * Imagine if you were asked to create an ad campaign to recruit students to Purdue. What types of appeals would you use and why? | ||
During other weeks, we will be more focused on learning practical skills (mostly data manipulation and visualization in R). On those weeks, discussions will center around identifying places where folks are still confused and students will be randomly selected to share their responses to homework questions. | During other weeks, we will be more focused on learning practical skills (mostly data manipulation and visualization in R). On those weeks, discussions will center around identifying places where folks are still confused and students will be randomly selected to share their responses to homework questions. | ||
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* Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week. | * Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week. | ||
* Compose complete and sufficiently detailed reflections. | * Compose complete and sufficiently detailed reflections. | ||
* Complete | * Complete many of the homework assignments. | ||
C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement | C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement | ||
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* Be collegial and continue discussion, through asking simple or limited questions. | * Be collegial and continue discussion, through asking simple or limited questions. | ||
* Compose reflections with straightforward and easily manageable goals and/or avoid discussions of challenges. | * Compose reflections with straightforward and easily manageable goals and/or avoid discussions of challenges. | ||
* Not complete homework assignments or turn | * Not complete homework assignments or turn some in in a hasty or incomplete manner. | ||
D/F: These are reserved for cases in which students do not complete work or participate. Students may also be impeding the ability of others to learn. | D/F: These are reserved for cases in which students do not complete work or participate. Students may also be impeding the ability of others to learn. | ||
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'''Readings (before class):''' | '''Readings (before class):''' | ||
* Langston, C. [https://www.youtube.com/watch?v=3klMM9BkW5o How to use rhetoric to get what you want] (video) | * Langston, C. [https://www.youtube.com/watch?v=3klMM9BkW5o How to use rhetoric to get what you want] (video) | ||
* Leighfield, L. [https://boords.com/ethos-pathos-logos-aristotle-modes-of-persuasion Ethos, Pathos & Logos: Aristotle’s Modes of Persuasion] | * Leighfield, L. [https://boords.com/ethos-pathos-logos-aristotle-modes-of-persuasion Ethos, Pathos & Logos: Aristotle’s Modes of Persuasion] | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* | * Discussion questions (on Monday) | ||
'''Readings:''' | '''Readings:''' | ||
* Effective Data Storytelling (EDS) Ch. 1--3 | * Effective Data Storytelling (EDS) Ch. 1--3 | ||
* Matei, S. [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/4750659/View What is a (data) story?] | * Matei, S. [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/4750659/View What is a (data) story?] | ||
* | * Levy, J. (2015). [https://www-tandfonline-com.ezproxy.lib.purdue.edu/doi/full/10.1080/09636412.2015.1070602 Counterfactuals, Causal Inference, and Historical Analysis] | ||
* (Optional) [https://towardsdatascience.com/storytelling-for-data-scientists-317c2723aa31 Storytelling for Data Scientists] | * (Optional) [https://towardsdatascience.com/storytelling-for-data-scientists-317c2723aa31 Storytelling for Data Scientists] | ||
* (Optional) [https://towardsdatascience.com/how-to-properly-tell-a-story-with-data-and-common-pitfalls-to-avoid-317d8817e0c9 How to properly tell a story with data — and common pitfalls to avoid] | * (Optional) [https://towardsdatascience.com/how-to-properly-tell-a-story-with-data-and-common-pitfalls-to-avoid-317d8817e0c9 How to properly tell a story with data — and common pitfalls to avoid] | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* Turn in your [[Self Assessment Reflection]] on Brightspace | * Turn in your [[Self Assessment Reflection]] on Brightspace | ||
* [ | * Peruse [https://www.reddit.com/r/dataisugly/ r/dataisugly] on Reddit and share a few examples of misleading visualizations on Discord | ||
'''Readings:''' | '''Readings:''' | ||
* Salganik, M. (2017). [https://www.bitbybitbook.com/en/ethics/ethics-intro/ Chapter 6: Ethics] from ''Bit by Bit''. | * Salganik, M. (2017). [https://www.bitbybitbook.com/en/ethics/ethics-intro/ Chapter 6: Ethics] from ''Bit by Bit''. | ||
* Kassner, M. [https://www.techrepublic.com/article/5-ethics-principles-big-data-analysts-must-follow/ 5 ethics principles big data analysts must follow] | * Kassner, M. [https://www.techrepublic.com/article/5-ethics-principles-big-data-analysts-must-follow/ 5 ethics principles big data analysts must follow] | ||
* McNulty, K. (2018). [https:// | * McNulty, K. (2018). [https://towardsdatascience.com/beware-of-storytelling-with-data-1710fea554b0 Beware of 'storytelling' in data and analytics] | ||
* (Optional) Steinmann, M., Matei, S. A., & Collmann, J. (2016). A Theoretical Framework for Ethical Reflection in Big Data Research. (On Brightspace) | * (Optional) Steinmann, M., Matei, S. A., & Collmann, J. (2016). A Theoretical Framework for Ethical Reflection in Big Data Research. (On Brightspace) | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
'''Readings:''' | '''Readings:''' | ||
* Pelz, W. [https://courses.lumenlearning.com/suny-hccc-research-methods/chapter/chapter-6-measurement-of-constructs/ Measurement of Constructs] in ''Research Methods for the Social Sciences''. | * Pelz, W. [https://courses.lumenlearning.com/suny-hccc-research-methods/chapter/chapter-6-measurement-of-constructs/ Measurement of Constructs] in ''Research Methods for the Social Sciences''. | ||
* [https://uxplanet.org/dirty-data-what-is-it-and-how-to-prevent-it-742accad081e Dirty Data article] | * [https://uxplanet.org/dirty-data-what-is-it-and-how-to-prevent-it-742accad081e Dirty Data article] | ||
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'''Class Schedule:''' | '''Class Schedule:''' | ||
* NYT Covid Dashboard case study | |||
== Week 6: Introduction to R == | == Week 6: Introduction to R == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* | * Install R and RStudio | ||
** | * Open the RMarkdown file | ||
* Complete the exercises | |||
'''Readings:''' | '''Readings:''' | ||
* [https://source.opennews.org/articles/what-i-learned-recreating-one-chart-using-24-tools/ What I Learned Recreating One Chart Using 24 Tools]. Lisa Charlotte Rost | * [https://source.opennews.org/articles/what-i-learned-recreating-one-chart-using-24-tools/ What I Learned Recreating One Chart Using 24 Tools]. Lisa Charlotte Rost | ||
* [https://r4ds.had.co.nz/introduction.html R4DS Ch. 1] | * [https://r4ds.had.co.nz/introduction.html R4DS Ch. 1] | ||
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'''Class Schedule:''' | '''Class Schedule:''' | ||
* Why programming? | |||
* Why R? | |||
* R Markdown | |||
* Functions | |||
* Variables | |||
* Data frames | |||
* Tidyverse | |||
== Week 7: Making figures in R == | == Week 7: Making figures in R == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
'''Readings:''' | '''Readings:''' | ||
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'''Class Schedule:''' | '''Class Schedule:''' | ||
* ggplot2 | * ggplot2 | ||
== Week 8: Manipulating and Aggregating Data == | == Week 8: Manipulating and Aggregating Data == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* Turn in your [[Self Assessment Reflection]] on Brightspace | * Turn in your [[Self Assessment Reflection]] on Brightspace | ||
'''Readings:''' | '''Readings:''' | ||
* [https://r4ds.had.co.nz/transform.html R4DS Chapter 5] | |||
* [https://r4ds.had.co.nz/transform.html R4DS Chapter 5 | |||
== Week 9: Visualization Principles == | == Week 9: Visualization Principles == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
'''Readings:''' | '''Readings:''' | ||
* EDS Chapter 7 | * EDS Chapter 7 | ||
* Healy, K. [https://socviz.co/lookatdata.html Data Visualization Chapter 1] | * Healy, K. [https://socviz.co/lookatdata.html Data Visualization Chapter 1] | ||
* | * Gelman, A. and Unwin, A. (2012). [http://www.stat.columbia.edu/~gelman/research/published/vis14.pdf Infovis and statistical graphics: Differrent goals, different looks]. | ||
* (Optional) Williams, R. (2008). [https://purdue-primo-prod.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=PURDUE_ALMA51793773920001081&context=L&vid=PURDUE&lang=en_US&search_scope=everything&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,The%20Non-Designer%27s%20Design%20Book&mode=Basic The Non-Designer's Design Book], Chapters 1-6 | * (Optional) Williams, R. (2008). [https://purdue-primo-prod.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=PURDUE_ALMA51793773920001081&context=L&vid=PURDUE&lang=en_US&search_scope=everything&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,The%20Non-Designer%27s%20Design%20Book&mode=Basic The Non-Designer's Design Book], Chapters 1-6 | ||
'''Class Schedule:''' | '''Class Schedule:''' | ||
== Week 10: Visualization Principles II and Exploratory Data Analysis == | == Week 10: Visualization Principles II and Exploratory Data Analysis == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* [[ | * [[/Data Source Assignment|Submit the data source for your final project]] | ||
* | * Submit 2 questions for take-home exam | ||
'''Readings:''' | '''Readings:''' | ||
* [https://socviz.co/groupfacettx.html#groupfacettx DV Chapter 4: Show the right numbers] | * [https://socviz.co/groupfacettx.html#groupfacettx DV Chapter 4: Show the right numbers] | ||
* EDS Chapter 8 | * EDS Chapter 8 | ||
* [https://r4ds.had.co.nz/transform.html R4DS Ch 5] | |||
* Hullman, J. [https://www-scientificamerican-com.ezproxy.lib.purdue.edu/article/how-to-get-better-at-embracing-unknowns/ How to get better at embracing unknowns] | * Hullman, J. [https://www-scientificamerican-com.ezproxy.lib.purdue.edu/article/how-to-get-better-at-embracing-unknowns/ How to get better at embracing unknowns] | ||
* Yau, N. [https://flowingdata.com/2018/01/08/visualizing-the-uncertainty-in-data/ Visualizing the uncertainty in data]. | * Yau, N. [https://flowingdata.com/2018/01/08/visualizing-the-uncertainty-in-data/ Visualizing the uncertainty in data]. | ||
'''Class Schedule:''' | '''Class Schedule:''' | ||
March 18 - READING DAY | |||
== Week 11: Text as data == | == Week 11: Text as data == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
'''Readings:''' | '''Readings:''' | ||
'''Class Schedule:''' | |||
* | * Guest lecture by [https://ryanjgallagher.github.io/ Ryan J. Gallagher] | ||
== Week 12: Advanced visualizations in R == | == Week 12: Advanced visualizations in R == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* [[Self Assessment Reflection]] | * [[Self Assessment Reflection]] | ||
* [[/ | * [[/Exam|Take-home Exam]] | ||
'''Readings:''' | '''Readings:''' | ||
* [https://socviz.co/maps.html#maps DV Chapter 7: Maps] | * [https://socviz.co/maps.html#maps DV Chapter 7: Maps] | ||
'''Class Schedule:''' | '''Class Schedule:''' | ||
* Maps | * Maps | ||
* | * Networks | ||
* Annotations | * Annotations | ||
== Week 13: Importing and cleaning data == | == Week 13: Importing and cleaning data == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* [[ | * [[/Final project proposal|Proposal for final project]] | ||
'''Readings:''' | '''Readings:''' | ||
* [https://r4ds.had.co.nz/data-import.html R4DS Chapters 11--12] | * [https://r4ds.had.co.nz/data-import.html R4DS Chapters 11--12] | ||
'''Class schedule:''' | '''Class schedule:''' | ||
* Provide peer feedback on final project proposal | * Provide peer feedback on final project proposal | ||
== Week 14: Crafting data stories == | == Week 14: Crafting data stories == | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* [[ | * [[/Final project proposal|New version of final project proposal]] (edited following peer feedback) | ||
'''Readings:''' | '''Readings:''' | ||
* Kim, Y. et al. (2017). [http://users.eecs.northwestern.edu/~jhullman/explaining_the_gap.pdf Explaining the Gap: Visualizing One’s Predictions | * Kim, Y. et al. (2017). [http://users.eecs.northwestern.edu/~jhullman/explaining_the_gap.pdf Explaining the Gap: Visualizing One’s Predictions ImprovesRecall and Comprehension of Data]. | ||
* Knaflic, C. N. ( | * Knaflic, C. N. (1029). [https://purdue.alma.exlibrisgroup.com/view/uresolver/01PURDUE_PUWL/openurl?ctx_enc=info:ofi/enc:UTF-8&ctx_id=10_1&ctx_tim=2020-06-13T12%3A39%3A32IST&ctx_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&url_ver=Z39.88-2004&rfr_id=info:sid/primo.exlibrisgroup.com-PURDUE_ALMA&req_id=_c20e3fe9e4a9a31b0162ece2023b8d45&rft_dat=ie=01PURDUE_PUWL:51807454010001081,language=eng,view=PURDUE&svc_dat=viewit&u.ignore_date_coverage=true&req.skin=PUWL&Force_direct=true&is_new_ui=true Storytelling with Data] Chapter 6 | ||
* EDS Chapter 9 | * EDS Chapter 9 | ||
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'''Assignment Due:''' | '''Assignment Due:''' | ||
* | * [[/Final project rough draft|Final project rough draft]] for peer feedback | ||
'''Readings:''' | '''Readings:''' | ||
'''Topics:''' | '''Topics:''' | ||
* | * Including uncertainty | ||
April 29 | April 29 |