European Master and Certification Program
in Risk Engineering and Management

Practical Example: Workshop Big Data

Course code: 181368
Language of instruction: English
Lecturers: Dr. Peter Klimek (European Virtual Institute for Integrated Risk Management), Prof. Dr. Aleksandar S. Jovanovic (Steinbeis Advanced Risk Technologies GmbH), Ph.D. Reto Schneider (SWICA Gesundheitsorganisation)
Assessment: Defined in the module

Short description

The course will cover several examples of where and how analytics of big data can be used to identify, understand and quantify novel types of risk or novel risk-risk interconnections. These example will cover natural language processing techniques to cluster large collections of unstructured data and its application in the detection of risk-risk interdependencies, mining social media in order to assess the impact and response to, both, endogenous and exogenous shocks, or how big and open datasets can be used to identify risks as well as opportunities in various contexts, for example for economic growth or in the management of supply chains. The course will also provide an overview of the methodological know-how behind these examples. Since many of the datasets for the discussed examples are available for free, the students will have the opportunity to repeat the analyses and gain hands-on experience.


At the end of the course the students are expected to have basic knowledge about:
• how to use big data analytics to identify, both, risks and opportunities from such data
• several examples of in which contexts big data analytics is especially useful
In addition, the students will have ample opportunity to gain hands-on experience in big data analytics.

Target Attendees / Participants

The course is dedicated to university students of Steinbeis European Master Program in Risk Engineering and Management, and similar programs.

Course Content by Units


Title & contents in brief

Duration (minutes)


Big data analytics

· Overview of quantitative, computational, and algorithmical methods to be used in this course.

· Network representations, time-series analysis, clustering, …

· Techniques for data visualization



Example I: Clustering unstructured data

· The bag-of-words approach: how to process textual data

· Network-based approaches to visualize and analyze large collections of documents.

· How to identify central elements and novel types of interconnections within such data



Example II: Social media mining

· Sources for social media mining: Twitter & Co

· Impact and response analysis in social media streams: Identifying and tracking events

· Statistical models for quantitative event analysis using social media streams



Example III: Complexity economics and growth opportunities

· You are what you produce: Mapping productive knowledge of countries and organizations.

· Identifying growth opportunities and innovation potential in markets.



Review of main course issues and preparation for the final exam

Teaching Methods

The course
• is illustrated by number of examples,
• presents commonly used methods and tools, and
• provides exercises and preparation for the final exam.


1. C.A.Hidalgo, B.Klinger, A.-L. Barabási, R. Hausmann, Science 317, 482 (2007)
2. C.A. Hidalgo, R. Hausmann, PNAS 106(26), 10570 (2009)
3. P. Klimek, W. Bayer, and S. Thurner, Physica A 390, 3870-3875, (2011)
4. R. Crane, F. Schweitzer, D. Sornette, Phys. Rev. E, 81 (2010), 5, 056101.
5. P.S. Dodds, K. Harris, I. Kloumann, C. Bliss, C. Danforth PLoS ONE 2011, 6(12): e26752

For more information about the European Master and Certification Program in Risk Engineering and Management in general, go the Homepage.
For more information about the European Master Program in Risk Engineering and Management in general, go the Master Study page.
To see more courses in the curriculum, go to The curriculum page, or by date and topic go to the Calendar of Courses page.
Contact: via email or phone +49 711 1839 781 or +49 711 1839 647
(Course profile ID: XIII-E-R55:, generated on November 19, 2018)