https://studiegids.vu.nl/en/courses/2025-2026/S_CADCUpon completion of this course, the students is able to:use the statistical programming environment R for various data analytical techniques; gather and import data from different file types, APIs, and websites; clean and transform messy data into a tidy data format ready for analysis; link data from different sources to create new insights; conduct computational text analysis, in order to extract useful information from the vast range of textual (communication) data that is available online; perform advanced statistical analyses that allow to tackle more complicated data; study communication phenomena using advanced and computational approaches.In this course, students will learn how to use computational techniques to analyze communication by learning to speak the “language of data”. They will both expand their methodological toolkit and develop a conceptual framework for understanding key developments in society. This course introduces data science technologies and techniques to study communication processes and effects in novel and innovative ways. Learning data science and computational methods is useful because it provides new skills and much thought-after qualifications in the job market, but also allows us to tap into new areas of research and to gain a better understanding of today’s society. In the course, students will learn about common computational methods in Communication Science, how to use the statistical programming environment R to a) gather data from online sources, b) transform and wrangle data to get it ready for analysis, c) perform text analysis (including machine learning approaches), d) perform advanced statistical methods in line with their chosen specialization (e.g., time series analysis, multilevel analysis, factor analyses and structural equation modelling, analysis of variance based on experimental data). Based on a problem-based learning approach, each lecture introduces a new empirical problem and discusses methods and statistical approaches that can solve the problem. Students then learn how to conduct these methodological solutions in the practical sessions and are given the opportunity to further practice these approaches in further homework assignments.Lectures and practical sessions. The course consists of three cycles (1. Introduction and data wrangling, 2. Text analysis, 3. Specialization). Each cycle covers two weeks. Each week includes one lecture and two practical sessions in which students learn how to implement the concepts and approaches introduced in the lectures using R. Students will also do practical homework assignments each week.After the first two cycles, there will be a (graded) written exam (70% of the final grade). After each week, students are required to hand in a “homework”, which represents a practical application of some of the taught analysis methods (e.g. with a new data set, specific research question). Students need to reach “sufficient” on average to pass (10% of the final grade). At the end of the third cycle, students are required to present results from their working groups, conducted in their respective specialization, in a mini-conference. This presentation will be graded per group (20% of the final grade).van Atteveldt, W., Trilling, D. & Arcila, C. (2021). Computational Analysis of Communication. Wiley. Will be made available from https://cssbook.net. Grolemund, G., & Wickham, H. (2017). R for Data Science. O’Reilly Media. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), 267-297. Welbers, K., Van Atteveldt, W., & Benoit, K. (2017). Text analysis in R. Communication Methods and Measures, 11(4), 245-265.Master’s students in Communication ScienceThe language of instruction for this course is “bilingual”, which means that the lectures are held in English but the working groups are offered separately in English or Dutch. If you are following the English-language program, please sign up (if registration is possible) for the English-language working groups.Students should have participated in the course “Research methods for Communication Science” (P1) as this course extends knowledge and skills learned in this course.