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Data Into Insights (Spring 2021)
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= Schedule = '''NOTE''' This section will be modified throughout the course to meet the class's needs. Check back in weekly. == Week 1: Introduction == January 19 '''Assignment Due:''' * [[/Discord signup|Sign up for Discord]] and introduce yourself * Take [https://forms.gle/spJzcKBCsERVLHNSA this very brief survey] '''Readings (before class):''' * None '''Class Schedule:''' * Class overview and expectations — We'll walk through this syllabus. January 21 '''Assignment Due:''' * Read the entire syllabus (this document) == Week 2: Storytelling and Narratives == January 26 '''Assignment Due:''' * [[#Discussion Questions|Discussion questions]] '''Readings (before class):''' * Zak, P. (2013). [https://greatergood.berkeley.edu/article/item/how_stories_change_brain How stories change the brain] * 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] * Purdue OWL [https://owl.purdue.edu/owl/general_writing/academic_writing/rhetorical_situation/aristotles_rhetorical_situation.html Aristotle's Rhetorical Situation] * [http://www.openculture.com/2014/02/kurt-vonnegut-masters-thesis-rejected-by-u-chicago.html Kurt Vonnegut's Shapes of Stories] * Lafrance, A. [https://www.theatlantic.com/technology/archive/2016/07/the-six-main-arcs-in-storytelling-identified-by-a-computer/490733/ The Six Main Arcs in Storytelling, as Identified by an A.I.] * (Optional) A Rulebook for Arguments (link on Brightspace) '''Class Schedule:''' == Week 3: Data insights and data stories == February 2 '''Assignment Due:''' * [[#Discussion Questions|Discussion questions]] '''Readings:''' * Effective Data Storytelling (EDS) Ch. 1--3 ([https://purdue-primo-prod.hosted.exlibrisgroup.com/permalink/f/vjfldl/PURDUE_ALMA51860241510001081 Purdue libraries copy]) * Matei, S. [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/4750659/View What is a (data) story?] * [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5392546/View Counterfactuals and Storytelling lecture ] [4:49] * (Optional) 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/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] '''Class Schedule:''' * Identifying insights * Counterfactual thinking * The role of statistics == Week 4: The ethics of data stories (Part I) == February 9 '''Assignment Due:''' * Turn in your [[Self Assessment Reflection]] on Brightspace * [[/Purdue WP Case|Case Study]] (Be prepared to talk about this case, based on the readings and the class so far) * No Discussion Questions (but feel free to have discussions on Discord!) '''Readings:''' * 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] * McNulty, K. (2018). [https://drkeithmcnulty.com/2018/07/22/beware-of-storytelling-in-data-and-analytics/ 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) '''Class Schedule:''' * Ethical frameworks * What are ethical data stories? * When do analysts need to make ethical decisions? * Transparency, respect, beneficence, honesty == Week 5: Where does data come from? == February 16 '''Assignment Due:''' * [[#Discussion Questions|Discussion questions]] '''Readings:''' * [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5431820/View Where data comes from lecture] [14:02] * 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] * Salganik, M. [https://www.bitbybitbook.com/en/1st-ed/observing-behavior Observing behavior] in ''Bit by Bit'' * EDS Chapter 5 * Perkel, J. [https://www-nature-com.ezproxy.lib.purdue.edu/articles/d41586-018-05990-5 A toolkit for data transparency takes shape] * (Optional) Tayi, G. K. and Ballou, D. P. (1998). [https://www.researchgate.net/publication/27297579_Examining_Data_Quality Examining Data Quality] '''Class Schedule:''' == Week 6: Introduction to R == February 23 '''Assignment Due:''' * [[/R Lab 1|R Lab 1]] ** [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5457615/View Video to help with lab] [7:39] '''Readings:''' * [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5477440/View Why Programming + Intro to R lecture] [12:53] * [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] (Optional) * [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ Unit 1: Basic Basics (R Ladies Sydney)] * [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Intro to R tutorial (Aaron Shaw)] '''Class Schedule:''' == Week 7: Making figures in R == March 2 '''Assignment Due:''' * [[/R4DS Chapter 3 Exercises|R4DS Chapter 3 Exercises]] ** [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5507580/View Video overview of how to do assignment + ggplot explanation] [13:33] '''Readings:''' * [https://r4ds.had.co.nz/data-visualisation.html R4DS Chapter 3] * [https://socviz.co/gettingstarted.html DV Chapter 2] '''Class Schedule:''' * ggplot2 == Week 8: Manipulating and Aggregating Data == March 9 '''Assignment Due:''' * Start [[/R4DS Chapter 5 Exercises|R4DS Chapter 5 Exercises]] ** [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5562641/View Video explanation of homework] [26:45] * Turn in your [[Self Assessment Reflection]] on Brightspace '''Readings:''' * [https://r4ds.had.co.nz/workflow-basics.html R4DS Chapter 4 - Workflow Basics] * [https://r4ds.had.co.nz/transform.html R4DS Chapter 5 - Data transformation] == Week 9: Visualization Principles == March 16 '''Assignment Due:''' * [[/R4DS Chapter 5 Exercises|R4DS Chapter 5 Exercises]] * [[#Discussion Questions|Discussion questions]] '''Readings:''' * [https://datavizm20.classes.andrewheiss.com/content/02-content/ Graphic Design] by Andrew Heiss. Make sure to watch all 4 videos. * EDS Chapter 7 * Healy, K. [https://socviz.co/lookatdata.html Data Visualization Chapter 1] * (Optional) 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 '''Class Schedule:''' March 18 - READING DAY == Week 10: Visualization Principles II and Exploratory Data Analysis == March 23 '''Assignment Due:''' * [[Data_Into_Insights_(Spring_2021)/Final_project#Step_1:_Identify_a_dataset|Submit the data source for your final project]] * [[Data_Into_Insights_(Spring_2021)/Visualization Project|Visualization Project]] '''Readings:''' * [https://socviz.co/groupfacettx.html#groupfacettx DV Chapter 4: Show the right numbers] * EDS Chapter 8 * 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]. * (Optional) Review [https://r4ds.had.co.nz/transform.html R4DS Ch 5] '''Class Schedule:''' * Summarize and discuss readings * Peer feedback on data source + visualization project * R4DS Chapter 5 (continued) == Week 11: Text as data == March 30 '''Assignment Due:''' * [[#Discussion Questions|Discussion questions]] - One discussion question and one or more examples of "bad" visualizations that you found '''Readings:''' * Grimmer, J., & Stewart, B. M. (2013). [https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F7AAC8B2909441603FEB25C156448F20/S1047198700013401a.pdf/text-as-data-the-promise-and-pitfalls-of-automatic-content-analysis-methods-for-political-texts.pdf Text as data: The promise and pitfalls of automatic content analysis methods for political texts]. Political Analysis. * Reagan, A. J., Mitchell, L., Kiley, D., Danforth, C. M., & Dodds, P. S. (2016). [https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-016-0093-1 The emotional arcs of stories are dominated by six basic shapes]. EPJ Data Science. '''Class Schedule:''' * Guest lecture by [https://ryanjgallagher.github.io/ Ryan J. Gallagher] == Week 12: Advanced visualizations in R == April 6 '''Assignment Due:''' * [[Self Assessment Reflection]] * [[/Story Time|Story Time Mini-project]] '''Readings:''' * [https://socviz.co/maps.html#maps DV Chapter 7: Maps] * [https://r4ds.had.co.nz/graphics-for-communication.html R4DS Ch. 28] '''Class Schedule:''' * Maps * [https://jeremydfoote.com/Communication-and-Social-Networks/week_6/ggraph_walkthrough.html Networks] * Annotations == Week 13: Importing and cleaning data == April 13 READING DAY * Synchronous session moved to April 15 April 15 '''Assignment Due:''' * [[Data Into Insights (Spring 2021)/Final project#Step_2:_Explore_the_data_and_write_a_proposal|Proposal for final project]] * [[/R4DS Chapter 12|R4DS Chapter 12 (12.2 and 12.3)]] '''Readings:''' * [https://r4ds.had.co.nz/data-import.html R4DS Chapters 11--12] * (Optional) Wickham, H. (2014). [http://vita.had.co.nz/papers/tidy-data.pdf Tidy Data]. Journal of statistical software, 59(10), 1-23. * (Optional) Huntington-Klein, N. [https://www.youtube.com/watch?v=CnY5Y5ANnjE&t=785s Data Wrangling with R and the Tidyverse] '''Class schedule:''' * Provide peer feedback on final project proposal == Week 14: Crafting data stories == April 20 '''Assignment Due:''' * [[#Discussion Questions|One discussion question]] * [[Data_Into_Insights_(Spring_2021)/Final_project#Step_2:_Explore_the_data_and_write_a_proposal|New version of final project proposal]] (edited following peer feedback) * [[/R4DS Chapter 12|R4DS Chapter 12 (12.4-12.6)]] '''Readings:''' * Kim, Y. et al. (2017). [http://users.eecs.northwestern.edu/~jhullman/explaining_the_gap.pdf Explaining the Gap: Visualizing One’s Predictions Improves Recall and Comprehension of Data]. * Knaflic, C. N. (2019). [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 == Week 15: Ethics of data stories (Part II) == April 27 '''Assignment Due:''' * 1 [[#Discussion Questions|Discussion question]] * [[Data_Into_Insights_(Spring_2021)/Final_project#Step_3:_Write_a_rough_draft|Final project rough draft]] for peer feedback '''Readings:''' * Re-read McNulty, K. (2018). [https://drkeithmcnulty.com/2018/07/22/beware-of-storytelling-in-data-and-analytics/ Beware of 'storytelling' in data and analytics] and reflect on how you see this differently now that you know more about data storytelling '''Topics:''' * What does an ethical data story look like? April 29 '''Assignment Due:''' * Peer feedback (via email or Discord) == Week 16: Finals week == '''Assignment Due:''' * [[{{PAGENAME}}/Final project|Final Project]] - Due Thursday, May 6 * Turn in your [[Final self reflection]] on Brightspace
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