BERD Academy

Join DataFest Germany, an 48-hour data competition held annually in Mannheim and Munich, where teams of students dive into a mystery dataset to uncover insights and create impactful analyses. Supported by experts from academia and industry, you have the chance to showcase your work to renowned data professionals and win prizes while networking with potential employers at the CareerFair.

Join this workshop in Leipzig to get to know state of the art AI applications to use text as data for social science research.

This online course provides an overview of Big Data, machine learning, & microsimulations; and its application to official statistics data. You will watch the online videos & do the assignments on your own pace and meet weekly with the course instructors.

How do I collect research data? What is the difference between qualitative and quantitative data? We will give you an overviewof the many ways in which research data can be obtained. Examples are given of the differences between various data sources and the theoretical and practical aspects that need to be taken into account in order to obtain the appropriate data for the research project.

How do I store research data correctly? How do I document research data? What does data security mean? In this introduction we will cover among other topics naming conventions as well as different storage locations and their advantages and disadvantages. So one thing is for sure: data loss was yesterday.

How do I find research data? And how do I get access to the important databases for my own field? We will give you a short introduction in which we will also show you how to identify usable data and how to cite it correctly.

In this online course, we will get your research project organized, under version control, stable, and published so that you can confidently say that your research is reproducible.

In this workshop, we will discuss the differences between data driven and question driven approaches. We will explore the relationship of data, facts, and policy. Let’s discuss the future of data and society!

In this workshop, you will gain a basic understanding of why ethics and values play such an important role when using data. You will get to know different ethical principles and discuss if they are still up-to-date to current developments and new data sources.

In this workshop, you will gain a basic understanding of data literacy and data culture, not only for data professionals but for all types of users of data and statistics.

How do I evaluate data quality for research data? What is a „good“ research question? In this short introduction we will outline ways to improve the quality of your reseach.

What is data literacy? We will provide a brief introduction to data literacy and show you how a planful approach to research data can contribute both to successful university studies and scientific research.

This talk will describe two software tools designed to address the challenges of collaborative, reproducible research: StatTag and StatWrap.

Join online sessions from the Data Science for Social Good (DSSG) lecture series for free!

Join online sessions from the Data Science for Social Good (DSSG) lecture series for free!

Join online sessions from the Data Science for Social Good (DSSG) lecture series for free!

Join online sessions from the Data Science for Social Good (DSSG) lecture series for free!

Join online sessions from the Data Science for Social Good (DSSG) lecture series for free!

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.

This workshop provides you with an overview of different research strains in the field of network data analysis. In a hands-on session, you will learn to analyze a real-world network dataset through the use of existing, readily available software packages.

Learn how to structure, tidy and anonymize raw geolocation data to create activity indicators using the data collected by the IAB-SMART-App.

In this webinar, you will get to know the new IAB-SMART data module with activity indicators from smartphone sensor data.

In this webinar, you will get an overview on how to collect smartphone data ethically and transparently with an Android app.

Women in Data Science (WiDS) elevates women in the field by providing inspiration, education, community, and support. Join the regional WiDS event in Munich to get to know outstanding women doing outstanding work in Data Science.

Findable, Accessible, Interoperable, and Reusable. These are the principles of FAIR Data and they have become a standard for good practice in research data management. In this 20 minute talk, we will cover how the reusability of your data can be achieved and why it can be beneficial for you to improve the reusability of your data.

Findable, Accessible, Interoperable, and Re-usable. These are the principles of FAIR Data and they have become a standard for good practice in research data management. In this 20 minute talk we will cover how the interoperability of your data can be achieved and why it can be beneficial for you to improve the interoperability of your data.

Findable, Accessible, Interoperable, and Re-usable. These are the principles of FAIR Data and they have become a standard for good practice in research data management. In this 20 minute talk we will cover how the accessibiliy of your data can be achieved and why it can be beneficial for you to improve the accessibiliy of your data.

Findable, Accessible, Interoperable, and Re-usable. These are the principles of FAIR Data and they have become a standard for good practice in research data management. In this 20 minute talk we will cover how the findability of your data can be achieved and why it can be beneficial for you to improve the findability of your data.

Are you a student from the social sciences, computer science or data science? Register your team now for DataFEST, where you will work with a large, not-publicly-available, real-world dataset for a weekend. Tutorials, workshops, mentors and professional consultants are available for support.

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.

In this session we will introduce you to Stata, a widely used statistical program that allows you to process, visualize and analyze data. We will give you an introduction to the most important functions and the basics of operation and use examples to show you the possibilities that open up when using Stata. No previous knowledge is assumed.

How do you visualize research data? Let’s face it: depending on the extent, research data can literally „overwhelm“ you at first glance. So how do you manage to present your data in an appealing way so that other people can also get a quick overview? We show you how to present data graphically.

Which ethical requirements must be observed when handling data? What are the ethical obligations for researchers along the data lifecycle? We will find answers to these questions and delve into the basics of data ethics in this Data Literacy Essential.

Dive deep into Machine Learning with R in these webinar offered by Essential Data Science Training.

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.

Dive deep into Data Analysis with R in these webinars offered by Essential Data Science Training.

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.

This Focused Tutorial will provide knowledge, methodological and technical expertise in the areas of data, metadata, taxonomies and standards with a view to the FAIR principles, and promotes a cross-disciplinary exchange and networking between the participating consortia.

Das Ziel des Workshops besteht darin, Forschenden der Ökonomie die Möglichkeit zu geben, sich zu neuen Datentypen und Entwicklungen der Datennutzung auszutauschen. Das umfasst folgende Aspekte:
– Big-Data-Analysen: Nutzung unstrukturierter Daten – aus: Internet (Stichwort: Webscraping), soziale Medien, Messdaten u. a. große Datenmengen – durch Umwandlung in strukturierte Forschungsdaten
– Generierung von Forschungsdaten durch Verknüpfungen von administrativen und/oder Befragungsdaten, z. B. via Algorithmen maschinellen Lernens
– Innovative Datenzugangswege in FDZ: z. B. FDZ-in-FDZ, Remote Access, Cloud

In this follow-up Data Literacy Snack we will tackle the question “When is GDPR applicable?”. Along with some practical examples we will introduce the interactive Virtual Assistant iVA: a tool, which helps researchers deal with data protection topics.

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 this Data Literacy Snack we will delve into the basics of privacy and data protection laws while focusing on the ever so relevant General Data Protection Regulation (GDPR).

Wie organisiert man Forschungsdaten? In dieser Einführung zeigen wir Ihnen, wie Sie Ihre Forschungsdaten organisieren und sichern. Dabei gehen wir sowohl auf Namenskonventionen als auch auf unterschiedliche Speicherorte sowie deren Vor- und Nachteile ein. So ist eines sicher: Datenverlust war gestern.

In der Einheit “Daten erheben” wird ein Überblick darüber gegeben, auf welche vielfältige Weise Daten gewonnen werden können. Dabei wird beispielhaft gezeigt, wodurch sich verschiedene Datenquellen unterscheiden und welche theoretischen sowie praktischen Aspekte zu beachten sind, um die für das Forschungsvorhaben passenden Daten zu gewinnen.

In this respect it is about more than just the application of statistical methods. Rather, the focus must be on the questions that a society wants to have answered with solid statistics for its current, pressing and conflict-laden issues. Other aspects then play a role here, namely whether politics values and finances this infrastructure, whether corresponding data literacy is available in the population at large, and so on.

Wie identifiziert man eigentlich die Qualität und Verwerndbarkeit von Forschungsdaten? Wir geben eine kurze Einführung. Hierbei zeigen wir, wie Sie “gute” Daten erkennen können und welche Kriterien hierfür wichtig sind.

New data sources and data science methods open up substantial opportunities for research and for improving statistics. However, the integration of traditional and newer methods requires more than the merging of methodology and technology. Rather, it is also a matter of further developing the other dimensions of good information quality, namely those of infrastructure, language and values, simultaneously in an integrative manner in this sense.

This workshop will provide a discussion of the five safes framework in helping conceptualize and implement the joint determination of risk and utility. It will describe the Coleridge Initiative’s use of the US FedRAMP framework, as well as the FedRAMP approach in more detail, in terms of minimizing risk. It will then work through the role of training classes in creating value.

Wie findet man Forschungsdaten? Und wie erhält man Zugang zu den wichtigen Datenbanken für das eigene Fachgebiet? Wir geben Ihnen eine kurze Einführung. Hierbei zeigen wir Ihnen außerdem, wie Sie nutzbare Daten identifizieren und richtig zitieren können.

You will get to know the conditions for evidence-based policy to contribute to shaping the transformation processes that arise in times of crisis and to reducing social conflicts to their minimum. In addition to its function as a common language for public (national and international) discourse, public statistics also serve as a data source and partner for research on individual and social behavior.

In this workshop series, you will learn about the DNA of official statistics, what quality means and how to achieve it, especially under the challenges of modern societies. Various use cases relevant to current policy at the national and international level will be used, in which the course participants will act as statistical stakeholders with … Continue reading Statistics for the Public Good – Infrastructures for Decision Making, Research, and Discourse Workshop SeriesStatistics for the Public Good →

In this workshop we will not just present the results, but have hands-on tutorials to work with the resulting API, dashboard, Jupyter Notebooks and visualization tools. The goal is to inspire BERD researchers to collaborate – or to develop new approaches to building an Amazon.com for European scientific and public data.

Was ist Datenkompetenz eigentlich genau? Wir geben eine kurze Einführung in das Thema Data Literacy und zeigen, wie ein planvoller Umgang mit Daten zu einem erfolgreichen Studium beitragen kann.

You want to learn R for Data Science, but don’t find the time to visit synchronous courses regularly? Register for this self-paced course and learn all you need to start with R on your own schedule!

This website offers an open and free introductory course on (supervised) machine learning. The course is constructed as self-contained as possible, and enables self-study through lecture videos, PDF slides, cheatsheets, quizzes, exercises (with solutions), and notebooks.

You want to learn Python for Data Science, but don’t find the time to visit synchronous courses regularly? Register for this self-paced course and learn all you need to start with Python on your own schedule!

Dive into Machine Learning (ML) on your own pace. This extensive online course from LMU Munich experts starts with the basics and guides you to the more advanced components of Machine Learning.