Congratulations! You made it trough the week. In our final session, we will focus on any open questions you may still have. Also, we will discuss some options where you might take your work with digital trace data from here.
The following readings might offer you some interesting perspectives on how to build on what you have learned in this course.
Using other data sources:
- Matthew A. Russell. Mining the Social Web. 2nd ed. Sebastopol, CA: O’Reilly Media, 2014.
Extending your analytical skill set:
- Mario Callegaro, Katja Lozar Manfreda, and Vasja Vehovar. Web Survey Methodology. 2015. SAGE. [Companion Site]
- David Donoho. 50 Years of Data Science. Paper presented at the Tukey Centennial workshop, Princeton, NJ. Sept. 18, 2015.
- Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. 2012. Cambridge, UK: Cambridge University Press.
- Alan S. Gerber and Donald P. Green. Field Experiments: Design, Analysis, and Interpretation. 2012. W.W. Norton.
- Wes McKinney. Python for Data Analysis. Sebastopol, CA: O’Reilly Media. 2013.
- Sebastian Raschka. Python Machine Learning. 2015. PACKT Publishing.
How might you employ these skills outside of academia:
- Fred Benenson. On to the next 2,271 days January 12, 2016.
- Thomas H. Davenport and D.J. Patil. “Data Scientist: The Sexiest Job of the 21st Century“. In: Harvard Business Review (2012, October).
- Olivia Lau and Ian Yohai. “Using Quantitative Methods in Industry“. In: PS: Political Science & Politics 49.3. 2016. pp. 524-526. doi: 10.1017/S1049096516000901
- David W. Nickerson and Todd Rogers. “Political Campaigns and Big Data“. In: The Journal of Economic Perspectives 28.2. 2014. pp. 51-73. doi: 10.1257/jep.28.2.51
- Andrew Therriault. “Finding a Place in Political Data Science“. In: PS: Political Science & Politics 49.3. 2016. pp. 531-533. doi: 10.1017/S1049096516000925
Course Material:
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