Reading list
Readings will be posted for each week on Blackboard, but here’s all of them in one list as well.
Week 01
No readings
Week 02
- Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554. https://doi.org/10.2307/2288400
- Wilke, C. O. (2019). Fundamentals of Data Visualization. https://clauswilke.com/dataviz/, ch 2, 17, 20
- Yau, N. (2025). Defense against dishonest charts. In FlowingData. https://flowingdata.com/projects/dishonest-charts/
Week 03
- Muth, L. C. (2019). What to consider when creating tables. In Datawrapper (Data vis do’s & don’ts). https://www.datawrapper.de/blog/guide-what-to-consider-when-creating-tables
- Wilke, C. O. (2019). Fundamentals of Data Visualization. https://clauswilke.com/dataviz/, ch 6, 7, 10, 12.1, 22.3
Week 04
- Datawrapper (2024). What to consider when choosing colors for data visualization. https://academy.datawrapper.de/article/140-what-to-consider-when-choosing-colors-for-data-visualization
- Kirk, A. (2015). Make grey your best friend. In Visualising Data. https://visualisingdata.com/2015/01/make-grey-best-friend/
- 1+ TBD: video workshop from Albers Foundation
- Cesal, A. (2019). What Are Data Visualization Style Guidelines? In Nightingale. https://nightingaledvs.com/what-are-data-visualization-style-guidelines/
- Muth, L. C. (2024). How to choose an interpolation for your color scale. In Datawrapper (Data vis do’s & don’ts). https://blog.datawrapper.de/interpolation-for-color-scales-and-maps/
Week 05
- Muth, L. C. (2022). What to consider when using text in data visualizations. In Datawrapper (Data vis do’s & don’ts). https://blog.datawrapper.de/text-in-data-visualizations/
- Wilke, C. O. (2019). Fundamentals of Data Visualization. https://clauswilke.com/dataviz/, ch 14, 16, 23
- Bertini, E., & Stefaner, M. (2019). Visualizing Uncertainty with Jessica Hullman and Matthew Kay (No. 134). https://datastori.es/134-visualizing-uncertainty-with-jessica-hullman-and-matthew-kay/
- Aisch, G. (2016). Why we used jittery gauges in our live election forecast. In vis4.net. https://vis4.net/blog/jittery-gauges-election-forecast
- Kosara, R. (2019). The DataSaurus, Anscombe’s Quartet, and why summary statistics need to be taken with a grain of salt. https://www.youtube.com/watch?v=RbHCeANCbW0
Week 06
- Cesal, A. (2020). Writing Alt Text for Data Visualization. In Nightingale. https://medium.com/nightingale/writing-alt-text-for-data-visualization-2a218ef43f81?source=friends_link&sk=32db60d651933b5ac2c5b6507f3763b5
- Schwabish, J. (2021). Frank Elavsky (No. 208). https://policyviz.com/podcast/episode-208-frank-elavsky/
- Elavsky, F. (2022). The Chartability Workbook. In Chartability. https://chartability.github.io/POUR-CAF/ (workbook to browse)
- 1+ on audience TBD
Week 07
- Cairo, A. (2019). How charts lie: Getting smarter about visual information (First edition). W. W. Norton & Company., introduction on Blackboard
- Schwabish, J. (2018). Catherine D’Ignazio and Lauren Klein (No. 142). https://policyviz.com/podcast/episode-142-catherine-dignazio-and-lauren-klein/
- Makulec, A. (2020). Ten Considerations Before you Create another Chart about COVID-19. In Nightingale. https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8
- Yau, N. (2018). Visualizing incomplete and missing data. In FlowingData. https://flowingdata.com/2018/01/30/visualizing-incomplete-and-missing-data/
Week 08
No readings
Week 09
- Wilke, C. O. (2019). Fundamentals of Data Visualization. https://clauswilke.com/dataviz/, ch 15
- Muth, L. C. (2024). What to consider when creating small multiple line charts. In Datawrapper (Data vis do’s & don’ts). https://www.datawrapper.de/blog/what-to-consider-when-creating-small-multiple-line-charts
- Muth, L. C. (2024). Which fonts to use for your charts and tables. In Datawrapper (Data vis do’s & don’ts). https://blog.datawrapper.de/fonts-for-data-visualization/
- Ericson, M. (2011). When Maps Shouldn’t Be Maps. In ericson.net. https://www.ericson.net/content/2011/10/when-maps-shouldnt-be-maps/
- Sadler, R. C. (2016). How ZIP codes nearly masked the lead problem in Flint. In The Conversation. http://theconversation.com/how-zip-codes-nearly-masked-the-lead-problem-in-flint-65626
Week 10
- Wilke, C. O. (2019). Fundamentals of Data Visualization. https://clauswilke.com/dataviz/, ch 21
- Muth, L. C. (2024). How to choose a color palette for choropleth maps. In Datawrapper (Color in data vis). https://blog.datawrapper.de/how-to-choose-a-color-palette-for-choropleth-maps/
- Seaberry, C. (2024). Wage gaps in connecticut. DataHaven. https://ct-data-haven.github.io/wage-gap-24/ (Sorry for assigning something I made)
- Yau, N. (2017). One Dataset, Visualized 25 Ways. In FlowingData. https://flowingdata.com/2017/01/24/one-dataset-visualized-25-ways/
Week 11
- Seaberry, C. (2018). CT Data Story: Housing Segregation in Greater New Haven. DataHaven. https://ctdatahaven.org/reports/ct-data-story-housing-segregation-greater-new-haven (Sorry for assigning something I made again)
- Abbasi, O. (2022). Dr. Lawrence Brown: Baltimore’s Black Butterfly and White L. https://www.tableau.com/foundation/data-equity/economic-power/black-butterfly-baltimore
- Simmon, R. (2024). From Space to Story in Data Journalism, Nightingale. In Nightingale. https://nightingaledvs.com/from-space-to-story-in-data-journalism/
Week 12
- Rankin, W. (2020). Race and the Territorial Imaginary: Reckoning with the Demographic Cartography of the United States. Modern American History, 3(2-3), 199–230. https://doi.org/10.1017/mah.2020.15
Week 13
- kollectiva orangotango (2018). This is Not an Atlas. https://notanatlas.org/book/ - students’ choice of 1 article from 1st section
- D’Ignazio, C., & Klein, L. (2021). Who collects the data? A tale of three maps. MIT Case Studies in Social and Ethical Responsibilities of Computing. https://doi.org/10.21428/2c646de5.fc6a97cc
Week 14
No readings
Week 15
No readings