E. Keith Smith: Catalogue data in Autumn Semester 2021

Name	Dr. E. Keith Smith
Address	Internat. Beziehungen, Bernauer ETH Zürich, IFW C 45.2 Haldeneggsteig 4 8092 Zürich SWITZERLAND
Telephone	+41 44 632 62 68
E-mail	keith.smith@gess.ethz.ch
Department	Humanities, Social and Political Sciences
Relationship	Lecturer

Number	Title	ECTS	Hours	Lecturers
857-0052-00L	Comparative and International Political Economy Number of participants limited to 15. MACIS students are given priority. Registration required to koubi@ir.gess.ethz.ch	8 credits	2S	V. Koubi, E. K. Smith
Abstract	This research seminar complements the MACIS core seminar in Political Economy. It covers topics such as international trade, environmental policy, international finance and foreign direct investment, and welfare state policy. Students will, based on reading assignments and discussions in class, develop a research question, present a research design, and write a paper.
Learning objective	Students will acquire an advanced understanding of some of the key issues and arguments in comparative and international political economy. They will also prepare the ground for a high-quality MA thesis in political economy.
Content	Because the number of students will be very small, the Political Economy core course runs in parallel, and research interests will be heterogeneous, the general approach will be informal and decentralized. Before the seminar starts we will identify what research topics - within the broader field of Comparative and International Political Economy - the participating students are most interested in. In the first two weeks of the semester, we will meet twice for two hours each as a group to discuss how to write a good research seminar paper, and to identify more closely what each student will be working on. Each student will then receive a reading list, so that she/he can get familiar with the state-of-the-art in her/his area of interests and develop a research design in close consultation with Profs. Bernauer and Koubi as well as postdocs from Prof. Bernauer's group. The group as a whole meets again ca. in week 7 of the semester to discuss the provisional research designs. Research then continues in a decentralized fashion - again in consultation with Profs. Bernauer and Koubi as well as postdocs from Prof. Bernauer's group. The group as a whole meets again in the second to last week of the semester. Each student reports on progress in her/his research during that meeting. The research seminar paper must be finalized and submitted by the end of July 2015.
Prerequisites / Notice	This seminar is restricted to students enrolled in the MACIS program.
860-0041-00L	Statistics 1 Only for Science, Technology and Policy MSc.	4 credits	2V	E. K. Smith
Abstract	This course covers the necessary fundamentals for the use of statistics to understand policy. Theoretically the course will provide a survey of foundational concepts and techniques statistics and mathematics. The applied part of the course will focus on implementing these techniques in R, as well as the practical skills required to develop their own data based research projects.
Learning objective	Gain a familiarity with foundational concepts and techniques in statistics, and be able to apply these to new problems. Be comfortable independently conducting a variety of tasks in R, such as data cleaning, visualisation and analysis. Produce summaries of statistical analyses that non-specialists can understand.
Content	This course introduces students to the necessary fundamentals of statistics, and its application, to understand policy. Theoretically the course will provide a survey of foundational concepts and techniques statistics and mathematics. The applied part of the course will focus on implementing these techniques in R, as well as developing the practical skills in the language required to be able to independently conduct data based research projects. By doing so, students will gain a familiarity with foundational concepts and techniques in statistics, and be able to apply these to new problems. Students will also develop the requisite skills to be able to independently conduct a variety of tasks in R, such as data cleaning, visualisation and analysis. Finally, students will be able to produce summaries of statistical analyses that non-specialists can understand.