(in Polish) Computational Social Science: An Introduction to Data Science

The development of Internet and social media, opens the whole new world of possibilities for social scientists to track human behavior. The questions that were hard to handle using traditional methods of data collection now can be addressed. Furthermore, the new possibilities allow for the formulation of new questions and tracking phenomena, which were impossible to follow before. However, the new sources of information require from social scientists to work on the verge of social science and computer science. This new area is usually called computational social science. Therefore, social scientists need to learn what type of data is available out there and how to collect it. It does not necessarily mean that they need to learn computer science because they might cooperate with computer scientists, but at least they need to understand the basic concepts to be able to plan adequate research.

This course is an introduction to data science for social scientists, therefore, it will introduce basic concepts only. It will not cover advanced methods, techniques, and theories. During the course following topics will be introduced: available data sources, data formats, popular computational tools, and environments, working with APIs, webscraping, and Natural Language Processing (NLP). However, the focus will be not on the technical aspect but on the possible applications for social scientists. Each topic will be illustrated with real-life examples, and students will have the possibility to not only learn basic concepts but also apply the methods in practice working on very simple examples.

The hands-on workshops will be based on prepared scripts that will require only simple configuration from students, however, some notion of Python and R programming languages is required (for example, finished course on Programming in Python for Social Scientists and Introduction to Statistics in R).

At the end of the course students will be able to understand basic concepts of computational social science, communicate with data scientists / computer scientists using adequate vocabulary, and foremost formulate research questions that can be addressed with computational methods and/or data extracted from existing web-based data sources.

Bibliography:

Main

 Vallacher, R. R., Read, S. J., & Nowak, A. (Eds.). (2017). Computational social psychology. Routledge.

 Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences of the United States of America, 110(15), 5802–5805.

Supplementary

 Guttag, J.V. (2021). Introduction to Computation and Programming Using Python. The MIT Press

 Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.

 Wickham, H. (2014). Advanced r. Chapman and Hall/CRC.

 Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. O'Reilly Media, Inc.

 Kosinski, M., Wang, Y., Lakkaraju, H., & Leskovec, J. (2016). Mining big data to extract patterns and predict real-life outcomes. Psychological methods, 21(4), 493--506.

Additional online resources and tools will be made available when appropriate during the course

The development of Internet and social media, opens the whole new world of possibilities for social scientists to track human behavior. The questions that were hard to handle using traditional methods of data collection now can be addressed. Furthermore, the new possibilities allow for the formulation of new questions and tracking phenomena, which were impossible to follow before. However, the new sources of information require from social scientists to work on the verge of social science and computer science. This new area is usually called computational social science. Therefore, social scientists need to learn what type of data is available out there and how to collect it. It does not necessarily mean that they need to learn computer science because they might cooperate with computer scientists, but at least they need to understand the basic concepts to be able to plan adequate research.

This course is an introduction to data science for social scientists, therefore, it will introduce basic concepts only. It will not cover advanced methods, techniques, and theories. During the course following topics will be introduced: available data sources, data formats, popular computational tools, and environments, working with APIs, webscraping, and Natural Language Processing (NLP). However, the focus will be not on the technical aspect but on the possible applications for social scientists. Each topic will be illustrated with real-life examples, and students will have the possibility to not only learn basic concepts but also apply the methods in practice working on very simple examples.

The hands-on workshops will be based on prepared scripts that will require only simple configuration from students, however, some notion of Python and R programming languages is required (for example, finished course on Programming in Python for Social Scientists and Introduction to Statistics in R).

At the end of the course students will be able to understand basic concepts of computational social science, communicate with data scientists / computer scientists using adequate vocabulary, and foremost formulate research questions that can be addressed with computational methods and/or data extracted from existing web-based data sources.

Bibliography:

Main

 Vallacher, R. R., Read, S. J., & Nowak, A. (Eds.). (2017). Computational social psychology. Routledge.

 Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences of the United States of America, 110(15), 5802–5805.

Supplementary

 Guttag, J.V. (2021). Introduction to Computation and Programming Using Python. The MIT Press

 Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.

 Wickham, H. (2014). Advanced r. Chapman and Hall/CRC.

 Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. O'Reilly Media, Inc.

 Kosinski, M., Wang, Y., Lakkaraju, H., & Leskovec, J. (2016). Mining big data to extract patterns and predict real-life outcomes. Psychological methods, 21(4), 493--506.

Additional online resources and tools will be made available when appropriate during the course