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Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Short Bio Nils B. Weidmann is Professor of Political Science at the University of Konstanz, Germany. He received an MSc in Computer Science from the University of Freiburg in 2003, a MA in Comparative and International Studies from ETH Zurich in 2008 and a PhD in Political Science from ETH Zurich in 2009. He was a Marie Curie Fellow at the Centre for the Study of Civil War, International Peace Research Institute Oslo (2011-12), a Postdoctoral Associate and Lecturer at Yale University (2010-11), and a Postdoctoral Research Associate at Princeton University (2009-10). Personal webpage: http://www.cnc.uni-konstanz.de/people/weidmann Prerequisite knowledge Note from the Academic Convenors to prospective participants: by registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach again these prerequisite items. If you doubt whether you possess that knowledge to a sufficient extent, we suggest you contact the instructor before you proceed to your registration. There is no prerequisite knowledge required. Short course outline In the age of “Big Data”, computational processing of data is becoming ever more important in the social sciences. With the help of computers, data can be collected, extracted from webpages, cleaned, reformatted, geo-referenced, analysed and visualized. However, in order for this to be possible, social scientists need to be equipped with basic programming skills. This course introduces students to the all-purpose Python programming language, which has become the tool of choice for many social science projects. It provides a step-by-step introduction for people without prior programming experience, relying entirely on supervised hands-on exercises with some interspersed social sciences examples. Topics to be covered in the course include the setup of software and tools for Python programming, parameters and variables, reading and writing files, Boolean expressions, loops, data structures, and object-oriented programming.