Factual description
KTU Big Data School is an annual workshop which combines practice and theory by providing theoretical background in lectures and improving skills during practical workshops. The main aim of the workshop is to update the participants on the most recent advances in the critical and fast developing area of big data. The covered topics are big data and financial analytics, machine learning, big data processing, etc. This workshop is dedicated to those who are familiar with mathematics, probability theory, statistics and algorithms on the level which is typically introduced at the bachelor’s degree studies in computer science and engineering programs.
In theoretical part of the workshop, researchers deliver lectures based on their research and publications. In the last workshop, the lecturers presented discoveries in the topics of Recurrent neural network and their application, Time Series Classification and Clustering, Big Data Science and Artificial Intelligence in Banking and Finance. Each lecturer presents a different topic, the complexity of which may lie in different aspects of it: the specifics of the problem, data (in their quality and quantity), applied methods, algorithms, computational and human resources. Thus, the features of complexity discussed by every lecturer may differ.
The problems solved in the practical part of the workshop should be classified as interdisciplinary problems. Each participant should be familiar with the specifics of the analyzed field (economics, finance, medicine, social sciences) together with the theoretical methods of mathematics and technical means of informatics. The Big Data school focuses on the analysis of the algorithms from the perspective of the type of problem the algorithm is suitable for, the selection of components of the algorithm, the variables and their functional relationships, the collection of data.
The methods presented by Big Data school help to optimize decision-making (assess investment risk in finance, make a diagnosis in medicine, identify significant economic indicators, etc.), automate processes. Such results contribute to environmental sustainability, reduced use of resources, efficiency of the health care system. The ethical dimension is considered by the means of impersonalized data and confidentiality to prevent damage from irresponsible usage of data.
Relevance in complex systems
The problems presented in this workshop refer to the problems in the real life. Although the problem field may differ, but they all must be analyzed as a part of a bigger system or a combination of components with respective functional links.
