Every year, an International Week is organised for our students.  It is our pleasure to welcome teachers from different foreign universities who will present material on various topics linked with STID studies in English throughout the week!

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27 January – 31 January 2020

Learn more about them...

Prof. Ota Novotný from University of Economics in Prague. Prof. Ota Novotny is in charge of « Business Intelligence ».

Kristyna-Vltavska_600x600_acf_croppedProf. Kristýna Vltavská from University of Economics in Prague. Prof. Kristýna Vltavská is in charge of Statistics practical with Prof. Petr Mazouch.

FilipVencovsky

Dr Filip Vencovsky works as an Assistant Professor of Informatics at the University of Economics in Prague. In 2018, he defended his PhD on the subject of text mining customers’ feedback from online reviews. He teaches software engineering, business analysis and analysis of unstructured data.

Zdenek-Vondra_600x600_acf_croppedProf. Zdeněk Vondra from University of Economics in Prague.

Petr-Mazouch_600x600_acf_croppedProf. Petr Mazouch from University of Economics in Prague. He is in charge of Statistics practical with Prof. Kristýna Vltavská.

Prof. Arnaud Legrand from LIG: Laboratoire Informatique de Grenoble in Grenoble. Prof Arnaud Legrand Introduction to reproducible Research

 

PolSocSquashDr Jacek Mosakowski from the College of Economics and Computer Science in Krakow. Dr Mosakowski works in programming for financial markets, and teaches object-oriented programming as well as mathematics. Earlier, he finished his Masters and PhD studies at the University of Cambridge in the UK.

 

The following topics will be addressed this year :

Economic information is important for controlling, making decisions, preparing future business plans or rating organization status. There are many ways how to get economic information form raw business data – e.g. with using business intelligence methods. When the information is discovered and delivered, another challenge is to set up the company strategy, execute and present it in the manner consistent with presentations, audience and situation.

We will provide a hands-on workshop, which will guide students from analysis of raw business data, through decision among available business scenarios to elaboration of business presentation.

Students will learn how to analyze raw business data with business intelligence methods. Then they will learn scenario elaboration, execution and evaluation, which tools and media types to use in different situations. Students will work in groups (4-5 members). These groups will compete with each other to provide the best output.

Reproducibility of experiments and analysis by others is one of the pillars of modern science. Yet, the description of experimental protocols, software, and analysis is often lacunar and rarely allows a third party to reproduce a study. Such inaccuracies have become more and more problematic and are probably the cause of the increasing number of article withdrawals even in prestigious journals and the realization by both the scientific community and the general public that many research results and studies are actually flawed and misleading. Open science is the umbrella term of the movement that strives to make scientific research, data and dissemination accessible to all levels of an inquiring society. Reproducible research encompasses the technical and social aspects of science allowing and promoting better research practices.

  • First-year students

The course introduces the methodology and the estimations of the main statistical indicators (namely un/employment, inflation rate, and gross domestic product) from the point of view of economic statistics. Students learn about the adequation gap and they are able to solve the adequation gap for the main economic indicators. The up-to-date statistics and international comparison using the main economic indicators is part of the course as well.

  • Second-year students

The course focuses on the data quality and the code of practise which is used in the European statistics and the estimation of the labour productivity among EU countries. Moreover, this course introduces composite indicators that are widely used for the ranking of the countries not just in the economy.  The ranking seems thorough and easy to interpret. However, many doubts have arisen in relation to the compilation of the composite indicator. This lecture aims to show the step-by-step procedure of compiling a composite indicator, i.e. choosing relevant variables, methods of standardisation, the weighting scheme, data aggregation, the sensitivity test, etc. This lecture points out the weaknesses and strengths of composite indicators and demonstrates how users should read them. Students prepare their own composite indicator towards the end of the lecture using MS Excel that determines the best country in terms of living. The subsequent alterations in their indicator show students how results may be manipulated by merely changing the weighting scheme or the aggregation method. Such work equips students with better skills for the interpretation of composite indicators for the future.

This course deals with investing in Bitcoin. Bitcoin is a purely digital currency, a phenomenon which has gained enormous attention in recent years due to its high increase rate, as well as spectacular jumps both upwards and downwards.

In this course, students will briefly learn the basics of investing in markets and how to analyze historical data. They will investigate if an analytical approach using computer software (Microsoft Excel) would help us to decide when to buy, and when to sell Bitcoin in order to obtain gains. No programming experience is required, only basic fluency with Excel.

Textual data, such as documents, websites, and conversations, makes a significant part of all data available. Textual data brings great potential for business purposes. On the other hand, harnessing such data requires an understanding of limitations and possibilities of text processing.

This course provides a brief overview of the possibilities and limitations that come with an analysis of textual data. The students will understand the most common business cases based on the analysis of textual data and will experience practical analysis of sentiment.