Foto: Matthias Friel
Data is becoming ever more important for students and researchers in the social sciences across different sub-disciplines including economics, business, political science, and sociology. Data skills are also in high demand on the job market including in fields as diverse as data science & programming, business intelligence, data journalism, public administration, international organizations, market research, and monitoring & evaluation.
This course is a hands-on, practical introduction to the R programming language for students of the social sciences. R is one of the fastest growing, most popular and extremely versatile statistics packages and programming languages in the world. It is free and has a large community that helps to improve it continuously. The course focusses on basic concepts in (descriptive) data analysis, management, visualization and reporting. Students will learn how to produce their own interactive data report and understand basic concepts in social data science. The course teaches the basics required for further delving into data science. The course will help students to make a decision whether to pursue more advanced quantitative courses in the future (or not).
Prior knowledge in statistics and data analysis is not required. The course is designed to show students that working with data is less scary than often perceived. It is aimed at beginners and those students curious to acquire a new skill.
Check out the course online: Intro to R for Social Scientists (jaspertjaden.github.io)
Check out intro video describing who this course is for: https://youtu.be/-rRQ6PZDqGg
Fogarty, B. J. (2018). Quantitative social science data with R: an introduction. SAGE Publications Limited.
Imai, K. (2018). Quantitative social science: An introduction. Princeton University Press.
Aydin, B., Algina, J., Leite, W. L., & Atilgan, H. (2018). An R Companion: A Compact Introduction for Social Scientists. Ankara: ANI Publishing. https://bookdown.org/burak2358/SARP-EN/
Wickham, H. & Grolemund, G. (2020). R for Data Science. https://r4ds.had.co.nz/
Huynh, Y.W. (2019). R for graduate students. https://bookdown.org/yih_huynh/Guide-to-R-Book/
Ansell, B. (2020). Introduction to R – tidyverse. https://bookdown.org/ansellbr/WEHI_tidyR_course_book/
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