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A Master’s thesis is a scientific research project of limited scale that is intended to exhibit the student’s capacity to work independently and carry out research. The intension of the thesis is to make a genuine contribution of new…

A Master’s thesis is a scientific research project of limited scale that is intended to exhibit the student’s capacity to work independently and carry out research. The intension of the thesis is to make a genuine contribution of new…

The aim of this course is to provide students with an in-depth knowledge of sociology of law while explaining how various theoretical traditions can be employed in analyzing concrete social issues which we confront today. The course will…

Module 1: The Multidisciplinarity of Sociology of Law You learn about the multidisciplinary research tradition within the sociology of law. The lectures cover the rich tradition of sociology of law, its concepts and perspectives, and how…

This course on Bayesian statistics covers methodology, major programming tools and applications in this field. The course starts with a review of conditional probability and Bayes’ Theorem. Introduction to the Bayesian approach will follow…

Analytics refers to our ability to collect and use data to generate insights for fact-based decision-making. The course is designed for students with a basic knowledge of statistics, and the content of the course will be of practical…

In this course, you will learn how to construct graphs and visualisations according to the theory Grammar of Graphics. You will also learn how to create visualisations yourself using the software R and its package ggplot2. A central part…

This course teaches you the basics of programming. You’ll learn core concepts such as data types, control flow (like loops and if-statements), and how to organise code using functions and classes. You’ll also explore programming techniques…

In this course you will write a special assignment, which could consist of a general investigation, a more specific problem, or a research topic. Using your previous skills and knowledge in Statistics, you will be trained in independently…

The fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning Feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network …

In this course you will learn modern statistical computing as viewed in data science through implementations in popular computing platforms such as R and Python. It covers the following topics: The R and Python environment: R packages…