PhD courses

Upcoming courses

Introduction to Machine Learning for the Social Sciences 

Course dates

Day 1 – Tuesday, 27 August 2024 – 9.00-16:00
Day 2 – Wednesday, 28 August 2024 – 9.00-16.00
Day 3 – Thursday, 29 August 2024 – 9.00-16.00 

Course description

This course will introduce the basics of big data and machine learning and how it can be used in the context of social science research. No prior knowledge is assumed, and it is well-suited for people with no prior experience using big data or machine learning. The course will cover different topics including how big data methods differ from inferential statistics, data cleaning and pre-processing, different machine learning models and explainable AI methods, data ethics and privacy, as well as bias and responsible AI.

Core machine learning principles such as cross-validation, out-of-sample prediction, and hyper-parameter tuning will be introduced. A key focus will be on interpretable machine learning models such as regression-based models, decision trees, and random forests.       

The assessment will involve completing a machine learning analysis, either in Python or in R. Therefore, some prior experience with one of these coding languages is preferred. A basic understanding of inferential statistics is also preferred. Participants can bring their own data or use the data that will be provided. 

Course organisers and teachers

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Hands-on introduction to datamanagement plans

Course dates

Day 1 – Wednesday, 6 November 2024 – 2:00 pm – 4:00/4:30 pm 
Day 2 – Wednesday, 20 November 2024 – 2:00 pm – 4:00 pm

Course description

Good research data management (RDM) practices are an essential part of good research practices. They increase the efficiency and quality of data collections and ensure secure handling and storage of data (to prevent data breaches, misuse, or loss) as well as compliance with requirements by research institutions, publishers, funders, and legislation (e.g. GDPR). This course provides a hands-on training on best practices in managing research data (including quantitative and qualitative data), during which the course participants draft data management plans (DMPs) for their current research projects.

Course organisers and teachers

  • Siri Völker, Special Consultant, Data Manager at the Social Sciences Data Lab, Copenhagen Center for Social Data Science, University of Copenhagen
  • Joseph Burgess, Senior Consultant, Data Steward at SODAS and Team Leader at the Social Science Data Lab, Copenhagen Center for Social Data Science, University of Copenhagen

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Previous courses

Network Science: Measures, models and causality

Course dates: 6th February – 12th March 2024

Course Description

Networks are all around us: We are ourselves, as individuals, the units of a network of social relationships of different kinds; the Internet and the highway system can be modelled as networks embedded in space; networks can be also entities defined in an abstract space, such as networks of acquaintances or collaborations between individuals.

This course aims to provide the computational concepts and methods to study these networks and form an advanced understanding of the current state of the art of network science and new methods from econometrics of networks.

The final objective is to have the students master computational techniques to solve advanced network problems, to be able to contribute to the development of network science, and to appreciate the future developments and limitations of scientific work dealing with network problems in real-world data.


Roberta Sinatra & Andreas Bjerre-Nielsen

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