Hosted by the Chair of Frauke Kreuter and the Munich Center for Machine Learning (MCML), this 2-month full-time program joins forces of aspiring talents in the area of Data Science in small groups to work on projects with a positive societal impact.
The program is designed for two teams of 4-5 fellows, each working on a separate project for the social good. Both teams will be assisted by a Technical Mentor and a Project Manager. The participants receive a fellowship that covers living expenses for the time of the program from August 1st – September 30th.
The program is aimed at students, recent graduates or PhD students from diverse scientific, as well as geographical backgrounds. We therefore encourage applications from the fields of data science, computer science, statistics, but also in general the social and natural sciences.
Over the past decade, there has been a growing public fascination with the complex “connectedness” of networks. This connectedness is found in ubiquitous situations: in the rapid growth of the Internet, in the ease with which global communication now takes place, and in the ability of news and misinformation as well as financial and political crises to spread around the globe.
To adequately capture and understand such phenomena, network analysis has proven to be extremely useful. In this context, methodological research on network analytical models picked up a lot of traction in recent years, due to the growing need for ways to handle network data.
This workshop intends to provide interested scholars with an overview of different research strains in the field of network data analysis.
In particular, we will work with data relating to international political interactions, such as the international trade of weapons, migration, and conflicts but also with classical social network data.
Participants will be introduced to the analysis of network data from both a substantive and statistical perspective. In a hands-on session, you will learn to analyze a real-world network dataset through the use of existing, readily available software packages.
How to analyze research data? In this introduction, we will show you the first steps you can take to analyze your research data and will give you an overview of the most important methods of descriptive and inferential statistics.
Der Data Analysis Bootcamp in R vermittelt in einzeln buchbaren Modulen Grundkenntnisse in R (Modul 1), praktisches Wissen zur deskriptiven Datenanalyse, der statisischen Inferenz und Modellierung in R (Modul 2), sowie fortgeschrittene Kenntnisse zur effizienten Nutzung von R (Modul 3). Beschreibung der Module:
Over the past decade, there has been a growing public fascination with the complex “connectedness” of networks. This connectedness is found in ubiquitous situations: in the rapid growth of the Internet, in the ease with which global communication now takes place, and in the ability of news and misinformation as well as financial and political crises to spread around the globe.
To adequately capture and understand such phenomena, network analysis has proven to be extremely useful. In this context, methodological research on network analytical models picked up a lot of traction in recent years, due to the growing need for ways to handle network data.
This workshop intends to provide interested scholars with an overview of different research strains in the field of network data analysis.
In particular, we will work with data relating to international political interactions, such as the international trade of weapons, migration, and conflicts but also with classical social network data.
Participants will be introduced to the analysis of network data from both a substantive and statistical perspective. In a hands-on session, you will learn to analyze a real-world network dataset through the use of existing, readily available software packages.
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