PhD

Research methods and data science

Short description

The purpose of the course is to fill in and expand the methodological, and to a lesser extent, computational skills of PhD researchers at the Hertie School. The focus will be broadly quantitative and will assume intermediate R skills (or a willingness to work with someone with such skills for practicals). While this will not be a ‘data science’ course, some of that material may appear in proportion to its topic relevance.

How this course works

Unlike other Hertie School courses, this one runs ‘year round’ and is substantially open-ended; we will start with topics of the instructor's choosing but expect to continue with topics of relevance to PhD researchers' projects. Participants should feel free to request more detail on any topic, and if there appears to be enough general interest, the course schedule will be rearranged to cover it. The format of the course is a mixture of lectures, practical work, and discussion.

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Target group

PhD researchers from all years.

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Goals

The purpose of the course is to fill in and expand the methodological, and to a lesser extent, computational skills of PhD researchers.

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Instructor

  • Dr. William Lowe is Senior Research Scientist at the Hertie School. His research spans legislative politics, political economy, and most recently public policy, focusing on the causal inference behind estimates of racial bias in policing. Methodologically he is interested in statistical models of text and in causal inference. He joins the Hertie School from Princeton University where he was Senior Research Specialist and a Lecturer in the Department of Politics. He has a PhD in Cognitive Science from the University of Edinburgh, a Bachelor of Arts in Philosophy from the University of Warwick, and has previously held positions at Harvard University, Trinity College Dublin, the University of Nottingham, and the MZES.