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Data Science for Social Good

Registration ended (Feb 15, 2023)

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.

If you are interested in joining the program or have students, friends or colleagues in mind that could be interested, check out the website and apply by February 15th: https://sites.google.com/view/dssgx-munich-2023/startseite

Please also share this call in your network, in classes you teach or approach people directly.

In case of any concerns please feel free to reach out via dssg2023@stat.uni-muenchen.de

About the Host and the organizing Institution

This Event is hosted by the Chair of Frauke Kreuter, who is a professor of Statistics and Data Science in the Social and Behavioral Sciences at LMU Munich. In her research, she focuses on statistical methods related to labor market and occupational research, as well as data science. In addition to her academic work, she is the founder or co-founder of several programs that address evolving data environments and data-driven research. The Munich Center for Machine Learning (MCML) is one of six national AI Competence Centers and brings together the leading ML researchers from LMU, TUM and associated institutions.

A Connected World: Data Analysis for Real-World Network Data

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

About the Instructors

While Giacomo De Nicola is a research assistant and doctoral student at the Institute of Statistics at LMU Munich, Cornelius Fritz is a postdoctoral fellow at Penn State, focusing mainly on developing novel data analysis techniques by combining statistical and machine learning with solid theoretical foundations. Göran Kauermann has been a full professor of Statistics at LMU Munich since 2011 and heads the chair for Statistics in Economics, Business, and Social Sciences there. Additionally, he serves as the chairman of the German Data Science Society (GDS).

Data Science with Python

You want to learn Python for Data Science, but don’t find the time to visit synchronous courses regularly?

Register for this free self-paced course and learn all you need to start with Python on your own schedule!

Introductory Tutorials

  • Python Introduction
  • Basic Scripting in Pyhton
  • Functions and Packages

Advanced: Data Management and Visualisation

  • Introduction to Pandas
  • Data Exploration in Pandas
  • Visualisation with Matplotlib
  • Advanced Plotting

Advanced: Working with Libraries

  • Numpy/Scipy
  • Working with Web Documents
  • Machine Learning with scikit-learn

About the Instructors

Sven Hertling and Nicolas Heist both work at the University of Mannheim as researchers or scientific staff. While Hertling holds a Master’s degree in Computer Science and primarily researches semantic technologies/semantic web, linked data, and knowledge graphs, Heist’s research interests primarily focuses on Semantic Web technologies, Knowledge Graphs, and Linked Data.

Data Science with R

You want to learn R for Data Science, but don’t find the time to visit synchronous courses regularly?

Register for this free self-paced course and learn all you need to start with R on your own schedule!

Introductory Tutorials

  • The True Basics of R
  • Data Manipulation in R
  • Data Visualization in R

Advanced Data Manipulation in R

  • Data Management
  • Subsets & Aggregation
  • Advanced Programming
  • Testing in R

Advanced Data Visualization in R

  • The Concept of Visualization & Advanced base R Graphs
  • Introduction to ggplot2
  • Advanced ggplot2

About the Instructor

Leonie Gehrmann is a doctoral student at the University of Mannheim in the field of Marketing. Her research interests primarily focus on machine learning applications in marketing, economics of data, and consumer psychology.

Data Literacy Essentials: Analyze Data

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.

Data Analysis Bootcamp in R (€)

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:

Modul 1: R Crashkurs (27.02.2023)

Modul 2: Praktische Datenanalyse in R (28.02. – 01.03.2023)

Modul 3: Effiziente Datenverarbeitung und Programmierung in R (02.03. – 03.03.2023)

A Connected World: Data Analysis for Real-World Network Data

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.

About the Instructors

While Giacomo De Nicola is a research assistant and doctoral student at the Institute of Statistics at LMU Munich, Cornelius Fritz is a postdoctoral fellow at Penn State, focusing mainly on developing novel data analysis techniques by combining statistical and machine learning with solid theoretical foundations. Göran Kauermann has been a full professor of Statistics at LMU Munich since 2011 and heads the chair for Statistics in Economics, Business, and Social Sciences there. Additionally, he serves as the chairman of the German Data Science Society (GDS).