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Business & Management (RSM)

This guide gives an overview of recommended sources for business and management research

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Erasmus Data Service Centre (EDSC)



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 

Open Educational Resources (OER)

Learning from Data: Machine learning, Deep learning and AI 

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.  

Data science, Machine Learning and AI

  • Steve Brunton - Teaching data science and deep learning, University of Washington. 
  • Machine Learning TV, Rich content, various providers. The channel is all about machine learning (ML). It contains all the useful resources which help ML learners and computer science students gain a better understanding of the concepts of this successful branch of Artificial Intelligence. (27.7K subscribers, Feb 2022)
  • Michael Bronstein group, provides an integrative theory of machine learning concepts, based on geomettric algebra. Advanced material!
  • Stanfordonline, Learning for a lifetime. Stanford Online is Stanford’s online learning provider, offering learners access to Stanford’s extended education and lifelong learning opportunities. A robust catalog of free and open content provides a variety of ways to expand learning, advance your career, and enhance your life. 
  • MIT OpenCourseWare, OCW is a free and open online publication of material from thousands of MIT courses, covering the entire MIT curriculum, ranging from the introductory to the most advanced graduate courses.
  • JHU Learning Theory, John Hopkins University (Spring 2014), basic course covering machine learning concepts     


  • UCL Centre for Artificial Intelligence, incl. David Barber - Bayesian reasoning and machine learning [pdf], 2010. One of the best free book resources for learning data science. The core aim of the UCL's Centre for Artificial Intelligence is to create new AI technologies and advise on the use of AI in science, industry and society. The Centre brings together researchers with a shared interest in fundamental challenges in Machine Vision, Machine Learning, Natural Language Processing, Machine Action, Interpretation and Knowledge Representation.
  • The Artificial Intelligence Channel. The channel is primarily focused on the future of artificial intelligence but also posts videos related to the technological singularity, transhumanism, anti-aging, synthetic biology, space exploration, technological unemployment, basic income and more, (113K subscribers, FEB22)
  •, is a place for professionals and enthusiasts working in AI to meet and discuss. The group has regular meetups and invited talks on a variety of AI-related topics.
  • Artificial Intelligence - All in One. Content related to Artificial Intelligence (AI), Deep Learning (DL), Machine Learning (DL), Natural Language Processing (NLP), Computer Vision (CV), and special topics related to data science (131K subscribers, FEB22).
  • Eye on AI, A biweekly podcast, hosted by longtime New York Times correspondent Craig S. Smith. Craig talks to people making a difference in artificial intelligence. The podcast aims to put incremental advances into a broader context and consider the global implications of the developing technology.
  • Numenta, Jeff Hawkins, incl. a new fundamental theory of the human brain: "A 1000's brains of intelligence"

Data Challenges, Data Thons, etc.,

  • Kaggle, write to if you consider taking part in a challenge yourself, and find out what the EDSC experts can do for you. If you have ideas about a data thon yourself, write to also. You can also write to

Learning from Data

Recommended MATLAB resources

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.    

  1. Steve Brunton - Teaching data science and deep learning with MATLAB, University of Washington 
  2. David Barber - Bayesian reasoning and machine learning [pdf], 2010. One of the best free book resources for learning data science with MATLAB
  3. Datathon 2022 support for Women in Data Science by providing complimentary MATLAB Licenses, tutorials, and resources to each participant
  4. MATLAB Central, landing page for file exchange, blogs, communities, answers and code examples   
  5. The MathWorks YouTube playlists, a good starting point for discovery and learning MATLAB
  6. Loren Shure - More than 30 years at MathWorks, interesting webinars on various (advanced) topics. Loren also has an informative blog worth following
  7. EMPossible - Great resource for e.g., in depth understanding of working with MATLAB graphics in detail [YouTubewebsite]
  8. Cristopher Lum - Need to know resource for creating video and animations in MATLAB
  9. Mike Fitzpatrick - Rock solid introduction to MATLAB [YouTube]
  10. David Hiebeler, MATLAB/R Reference, 2015. Recommended resource for comparing MATLAB to R.

Data Analytic Tools

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. 

Wharton Research Data Service (WRDS)

SAS Studio at WRDS 

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. 


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.  

Python at WRDS

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. 

Geodata Guide


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 Research

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.