Week 14: Where to take it from here? Discussion of Open Questions and Paper
Congratulations! You made it through the semester. After focusing on the use of Twitter data in research, today’s session will extend our scope a bit. Digital trace data is of increasing importance in various areas far beyond science. In this session, we will discuss how you could build on what you have learned so far. Topics we will discuss are:
Using other data sources:
While using Twitter data is all well and good, Twitter is far from the only or even the most interesting data source available to you. After learning the general workflow of working with digital trace data in this course, there is much to discover beyond this. The following readings might help you getting started.
- Mitchell, R. (2018). Web scraping with python: Collecting more data from the modern web (2nd ed.). Sebastopol, CA: O’Reilly Media.
- Russell, M. A. (2018). Mining the social web (3rd ed.). Sebastopol, CA: O’Reilly Media.
- Salganik, M. J. (2017). Bit by bit: Social research in the digital age. Princeton, NJ: Princeton University Press.
Extending your analytical skill set:
The course focused strongly on getting data from Twitter an preparing them for analysis. In this, the discussion of analytical approaches has been fallen somewhat short. The following readings might make up for this by offering introductions to some of the most promising innovative analytical approaches for digital trace data.
- Callegaro, M., Manfreda, K. L., & Vehovar, V. (2015). Web survey methodology. London, UK: SAGE Publications.
- Donoho, D. (2015). 50 years of data science. Paper Presented at the Tukey Centennial Workshop.
- Efron, B. & Hastie, T. (2016). Computer age statistical inference: Algorithms, evidence, and data science. Cambridge, UK: Cambridge University Press.
- Flach, P. (2012). Machine learning: The art and science of algorithms that make sense of data. New York, NY: Cambridge University Press.
- Gerber, A. S. & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. New York, NY: W. W. Norton & Company.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: The MIT Press.
- McKinney, W. (2017). Python for data analysis: Data wrangling with pandas, numpy, and ipython (2nd ed.). Sebastopol, CA: O’Reilly Media.
- Mutz, D. C. (2011). Population-based survey experiments. Princeton, NJ: Princeton University Press.
- Raschka, S. & Mirjalili, V. (2017). Python machine learning: Machine learning and deep learning with python, scikit-learn, and tensorflow (2nd ed.). Birmingham, UK: PACKT Publishing.
- Salganik, M. J. (2017). Bit by bit: Social research in the digital age. Princeton, NJ: Princeton University Press.
How might you employ these skills outside of academia:
Finally, the following readings offer perspectives on how to use your new-won skills in contexts beyond resarch and academia:
- Benenson, F. (2016). On to the next 2,271 days. . .. Medium: Hackernoon.
- Davenport, T. H. & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review.
- Lau, O. & Yohai, I. (2016). Using quantitative methods in industry. PS: Political Science & Politics, 49(3), 524–526. doi:10.1017/S1049096516000901.
- Nickerson, D. W. & Rogers, T. (2014). Political campaigns and big data. The Journal of Economic Perspectives, 28(2), 51–74. doi:10.1257/jep.28.2.51.
- Therriault, A. (2016). Finding a place in political data science. PS: Political Science & Politics, 49(3), 531–534. doi:10.1017/S1049096516000925.