Big Data Analytics for Economics and the Sciences

Organizations, industry and science increasingly rely on data-based decisions, whereas data itself is growing in volume and variety.

Exact, large-scale and time-sensitive data can be harnessed for the progress in organizations, but these massive amounts of data require both new technical approaches in quantitative and qualitative analytics as well as new processing skills. This transformation will be accompanied by Big Data Analytics for Economics and the Sciences. We aim to develop a center on big data research and offer transdisciplinary teaching.

Research Projects

Development of High-Performance Display Systems (R. Pajarola)

To meet the need to analyze and explore expansive data collections, visualization is a key element. While computers better process and analyze data numerically, the human mind has a far better capability to visually recognize patterns, clusters and spatial relations. One of the growth areas in visualization is certainly the bio-medical field, where three-dimensional images ought to be displayed at the cellular resolution level. This requires novel high-performance and high-resolution display systems. Our research tackles the challenge of how to exploit high-performance computing resources to generate high-resolution imagery at interactive rates, shared on a variety of display systems, ranging from virtual-reality installations down to handheld ubiquitous devices.Crowdsourcing markets like Amazon’s Mechanical Turk have grown immensely in recent years. Yet, the allocation and pricing of workers in these markets is still very simple, as most markets only offer a fixed-priced wage per task. These simple market mechanisms.

Further research projects

DIVA - Data Intensive Visualization and Analysis (R. Pajarola)
ViSTA-TV (A. Bernstein)
Tameus (M. Böhlen)
SOSYA - Systems of Systems Analysis of Software Data (H. Gall)
Signal/Collect (A. Bernstein)
Distributed Parallel Rendering Services (R. Pajarola)
Analysis and Visualization of Large-Scale Stock Correlation Networks (D. Hu)
SIMPOL - Financial Systems Simulation and Policy Modelling (S. Battiston)

Connections to courses

Students interested in how knowledge gained from massive data sets can transform and accelerate business development, industry production and scientific research will find this topic to be the right focus of their studies. A number of courses and projects related to large data management, efficient processing and analysis as well as interactive visualization are offered at the Bachelor’s and Master’s level to interested students. The following list provides examples of courses particularly related to our topic.

List of courses

More detailed information on each module can be found by copying the 8-digit code into the search field of the University’s course catalogue.

Course catalogue

Bachelor's level

Datenbanksysteme	BINF2160
Praktikum Datenbanksysteme	BINFPR01
Seminar: Database Systems	BINFS133
Seminar: Graphics and Multimedia	BINFS130
Marketing and Social Networks	BOEC0326
Introduction to Data-Driven Marketing	BOEC0320

Master's level

Distributed Systems	MINF4211
Database Management and Performance Tuning	MINF4537
Data Mining zur Wissensgewinnung aus Datenbanken	MINF4230
Mainframe & Parallel Programming	MINF4528
Practical Artificial Intelligence	MINF4529
Business Network Analysis & Applications	MINF4533
Market Research: Multivariate Methods	MOEC0151
Time Series Analysis	MOEC0028
Advanced Statistics	MOEC0303
Case Studies in Management Science: Stochastic Models	MOEC0155
Empirical Methods for Business Administration	MOEC0380
Computational Economics and Finance	MFOEC167
Optimierungsmethoden	MOEC0145

Doctoral level

Business Analytics and Big Data	DOEC0464
PhD Seminar in Quantitative Market Research	DOEC0384

Faculty members involved

The following Faculty members research and/or teach in Big Data Analytics for Economics and the Sciences.

Faculty of Business, Economics and Informatics

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