This page deals with data mining and pattern recognition, which are methods in data science. A general purpose of data science is pattern discovery from unstructured and heterogeneous sources of data through data mining and machine learning. The content on this page discusses data wrangling, clustering analysis, regression trees, neural networks, sentiment analysis and topic models.
The Chapter summary video gives a brief introduction and summary of this group of methods, what SES problems/questions they are useful for, and key resources needed to conduct the methods. The methods video/s introduce specific methods, including their origin and broad purpose, what SES problems/questions the specific method is useful for, examples of the method in use and key resources needed. The Case Studies demonstrate the method in action in more detail, including an introduction to the context and issue, how the method was used, the outcomes of the process and the challenges of implementing the method. The labs/activities give an example of a teaching activity relating to this group of methods, including the objectives of the activity, resources needed, steps to follow and outcomes/evaluation options.
More details can be found in Chapter 17 of the Routledge Handbook of Research Methods for Social-Ecological Systems.
Lab teaching/ activity
Tips and Tricks
- Chollet, F. 2017. Deep Learning with Python. New York: Manning.
- Chollet, F., and J.J. Allaire. 2018. Deep Learning with R. New York: Manning.
- Mitchell, M. 2019. Artificial intelligence: A guide for thinking humans. London: Pelican Books.
- Pearl, J., and D. Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. London: Penguin.
- Wickham, H., and G. Grolemund. 2017. R for Data Science. Beijing: O’Reilly.