The Erasmus Data Service Centre (EDSC) offers one of the most comprehensive portfolios of financial and economic datasets across Dutch Universities and provides an internationally competitive reference point for finance, economics, and social science research.
The Datateam provides data retrieval support for students and researchers, with a focus on collection, processing, and structuring of financial and macroeconomic data for statistical analysis. The team is also responsible for the acquisition of complex datasets.
Access the EDSC full list of financial databases and topic-specific tutorials through the links below:
The Datateam provides individual support and will answer your financial data enquiries via firstname.lastname@example.org
For roughly a decade there has been a growing and specific interest in automated learning from digital data. Since the breakthrough of machine learning in 2012, many perspectives and conceptualizations of learning have been articulated and applied. Well-established and fine-grained learning models are now readily available for e.g., (un-)supervised, reinforcement learning, deep learning and transfer learning, to name a few. Examples of learning applications include image and speech recognition, automated driving, navigation and automated content generation.
Though learning as scientific concept originates from psychology and cognitive science research, learning from data encompasses both traditional statistical approaches, current data science methodologies as well as the latest insights in knowledge representation, cognitive science, and AI. Mastering the data skills that will enable you to (gradually) apply the learning principles in practice requires effort, perseverance and learning from others. Whether you are a novice or an expert in the field the section below provides an overview of data resources, platforms and communities that are relevant to learning from data and building your data skills.
Though MATLAB requires a paid license subscription, many additional software packages, modules and code examples are available free of charge to MATLAB users.The curated list below highlights a variety of freely accessible resources for students and staff to explore.
EUR provides access to sophisticated applications for analysing large bodies of data, develop algorithms, and create models.
Below you will find the software which can be used on campus or downloaded to your personal computers.
SAS on WRDS provides a fast way of performing complex calculations and data management tasks like joining tables and can handle very large amounts of data (into the hundreds of Gigabytes). SAS comes with hundreds of common statistical procedures built-in that are highly accurate.
R at WRDS
WRDS provides a direct interface for R access, allowing native querying of WRDS data right within your R program. All WRDS data is stored in a PostgreSQL database and is available through R via a native R Postgres driver.
WRDS provides a direct interface for Python access, allowing native querying of WRDS data right within your Python program. All WRDS data is stored in a PostgreSQL database, and is available through Python, on PyPI via a pip install.
This topic guide provides a curated list of resources that acts as a filter to data, tools and information resources for all research that involves geo-referenced and spatial characteristics. Economic activity is closely linked to geographical characteristics, whether studied at a macro-, meso- or micro-level.
The “End of Geography” foreseen by Richard O’Brien (1992) seems nothing more than a shadow from the past. At the same time Paul Krugman’s writing that “The location of production in space is a key issue both within and between nations” is very much alive. Whether you approach geospatial characteristics, data and information from an economic, business, multi-national or data science perspective, GIS offer many opportunities for innovative research, business applications and fundamental insights in regional and multinational development.
EUR has a long-standing tradition for research in spatial economics and urban economics. Well-known EUR researchers are prof. Jan Tinbergen (publlcations), prof. Jean Paelinck and prof. Frank van Oort. Several EUR researchers are also involved in the Tinbergen Spatial Economics Research Group:
Matlab, R and Stata provide specific spatial packages, functions and scripts to read, process and analyze geospatial data.