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. Basic R Skills are required.
“A Connected World: Data Analysis for Real-World Network Data” will take place July 19, 2023 at the Leibniz Supercomputing Centre in Garching (Munich).
Program 10:00 – 11:30 Introduction to Network Data Analysis 11:30 – 12:00 Coffee Break 12:00 – 13:30 Exponential Random Graph Models 13:30 – 14:30 Lunch Break 14:30 – 16:00 Latent Space Models 16:00 – 16:30 Coffee Break 16:30 – 18:00 R Lab
Der Machine Learning Bootcamp in R vermittelt in einzeln buchbaren Modulen elementare Methoden und Konzepte zur Anpassung und Optimierung von Vorhersagemodellen aus dem Bereich Predictive Modeling und Supervised Machine Learning (Modul 1), zum Auffinden von Strukturen in den Daten aus dem Bereich des Unsupervised Machine Learning (Modul 2), sowie praktisches Wissen zum interpretierbaren maschinellen Lernen (Modul 3). Beschreibung der Module:
In this session we will introduce you to R, a programming language that allows you to edit, visualize and analyze data. We will give you an introduction to the programming environment RStudio, teach you the basics of syntax and show you examples of the possibilities that open up with the use of R. No previous knowledge is assumed.
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.
How can we make use of new data sources and data science methods to enhance public statistics?
This course gives an overview of advanced topics in official statistics such as Big Data, machine learning, and microsimulations. The benefits and downsides of using Big Data as a data source for official statistics production are discussed and examples of its use are given, including machine learning applications.
In addition, the course provides insights into microsimulation and gives an overview of the past, the present, and the future state-of-the-art of microsimulation methods and applications within official statistics.
This online course uses a flipped classroom design, which means that you can watch the weekly hour of video lectures according to your own schedule. In the weekly one-hour online meetings you have the chance to discuss the material and hands-on applications with the instructors from destatis and Statistics Netherlands.
Basic R knowledge is required. Having some familiarity with the official statistics system as taught in Walter Radermacher’s BERD Academy workshop series “Statistics for the Public Good” can be helpful.
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