#BIBLIOGRAPHY
Here is a summary list of readings that may or may not be listed directly in the syllabus. Should you want to go more in depth or require additional clarity on certain topics, feel free to consult the following resources to learn more. We'll move fast this semester, I don't expect you to have time for all of this, but you might skim some of the resources as they are relevant to your projects.
- Boyer, Keefe, Lindberg, Park, Wu. _Five-minute field guide.
- Knight Center for Journalism in the Americas, Data visualization resources and ideas.
- Looney, M., Global data journalism resources by country
- Misra, R., 30 places to find open data on the web.
- Mulin, Guide to Internet Search.
- Guide to awesome/vailable datasets
- Brewer, C. et al, Color palettes: Color Brewer, Color Hexa, Geocolor.
- Jones, B., Clarity or aesthetics: A tale of 4 quadrants.
- Macwright, T., Perception Concerns.
- Meeks, E., Gestalt Theory.
- Cohen, S., Using visualizations to tell stories.
- Data Art Net, A Visual History of Data Visualization.
- Dubakov, M., Patterns for Information Visualization.
- Duke University, Chart Dos and Don'ts.
- Kosara, R., Storytelling: the next step for visualization.
- Moser, A. + Chartoff, B., Chart tools repository.
- Ward, M. et al., Interactive Data Visualization Handbook.
- Carr, D., At the front lines, bearing witness in real time.
- Dubakov, M., Visual Encoding.
- IRE, Excel tipsheet.
- Journalism Tools, List of data analysis tools.
- Kreibel, A., Stacked area vs. line chart.
- Suda, B., Different Charts tell different tales.
- Bahgat, K., Visualizing geodata: Python GIS resources.
- Crampton, J., An introduction to critical cartography.
- Eschbacher, A., Map Academy.
- Foster, M., The lost art of critical map reading.
- Lloyd, D. et al., Meditating geovisualization to potential users.
- Wiseman, A., When maps lie.
- Krempel, L., Network Visualization.
- Tamassia, R., Handbook of graph drawing and visualization.
- Tarawneh, R.M., et al., A general introduction to graph visualization techniques.
Most of this class will focus on the fundamentals of HTML/CSS/Javascript. The coding resources are oriented to that study with a nod to data visualization as an objective. The following should help you out.
- Bostock, M., D3 Tutorials.
- Code Academy.
- Cukier, J., D3 and Data Visualization Blog/Tutorials.
- Dashing D3.js.
- Lin, M., Essential coding skills for journalism students.
- Murray, S., D3 Tutorials.
- Processing Tutorials.
Should you like to do some additional exercises to boost your skills, here are some sources for supplemental MOOCs that can be taken in correspondence with this class.
- Cairo, A., Journalism Courses.
- Coursera.
- Johns Hopkins: "Computing for data analysis", starts late September.
- Canvas Network.
- Eschbacher, A., Map Academy.
- For Journalism.
- Udacity.
- edX.
- Mullin, B., 40 great journalism internships.
- NYTimes, Newsroom summer internships.