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Comparison of machine learning methods for credit risk analysis
Bušo, Bohumír ; Kolman, Marek (advisor) ; Vacek, Vladislav (referee)
Recently, machine learning has been put into connection with a field called ,,Big Data'' more and more. Usually, in this field, a lot of data is available and we need to gather useful information based on this data. Nowadays, when still more and more data is generated by use of mobile phones, credit cards, etc., a need for high-performance methods is serious. In this work, we describe six different methods that serve this purpose. These are logistic regression, neural networks and deep neural networks, bagging, boosting and stacking. Last three methods compose a group called Ensemble Learning. We apply all six methods on real data, which were generously provided by one of the loan providers. These methods can help them to distinguish between good and bad potential takers of loans, when the decision about the loan is being made. Lastly, the results of particular methods are compared and we also briefly outline possible ways of interpretation.
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Hadoop and Business Intelligence
Kerner, Josef ; Šperková, Lucie (advisor) ; Augustín, Jakub (referee)
The main purpose of this thesis is to describe how an integration of a Hadoop platform into currently existing Business Intelligence technologies and processes can augment its data processing and analysis capabilities while encountering Big Data. Furthermore, it describes reasons why the whole Hadoop application ecosystem was founded and informs the reader about the functionality of its primary components. It continues with provision of overview about Hadoop higher-level components architecture and their use in existing Business Intelligence processes such as data ingestion, transformation and analysis. In the last theoretical chapter it focuses itself on describing specific areas of utilization of the Hadoop platform and Big Data in data warehousing, text mining and predictive analytics. From the practical point of view, a particular use case is provided, an implementation of Big Data ETL process in the field of financial markets and trading with a detailed explanation of the corresponding necessities such as data model, ETL code and proposed metrics, which can be further implemented for achieving increased return on investments.
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ICT trends in China and Taiwan
Kačala, Dominik ; Samec, Marek (advisor) ; Potančok, Martin (referee)
This bachelor thesis deals with the actual trends in the area of information and communication technologies, focusing on Far East countries with emphasis on China and Taiwan. The theoretical foundation of this work is to provide an overview of the current situation in the ICT market and its subsectors in individual countries. The specifics of countries in relation to information technology are also briefly described. The second part focuses on the actual analysis of selected ICT trends in China and Taiwan and compares specific approach to them from different angles. In the last part of the thesis conclusions and results of the analysis from the second chapter are applied in comparison with the West, USA and EU specifically and examined trends are evaluated in the global context. The contribution of this paper is to provide a comprehensive overview of the area of selected ICT trends in the countries concerned.
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Application of Big Data in the banking environment
Dvorský, Bohuslav ; Chlapek, Dušan (advisor) ; Palovská, Helena (referee)
This thesis addresses the principles and technologies of Big Data and their usage in the banking environment. Its objective is to find business application scenarios for Big Data for purposes of delivering added value for the bank. Finding the scenarios have been achieved by studying literature and consultation with experts, they were also subsequently modeled by the author. Possibilities of application of these scenarios in the banking busi-ness environment were subsequently verified by the survey, which interviewed profession-als on issues relating to the found business scenarios. The thesis first explains the basic con-cepts and approaches of Big Data, the status of this technology compared to traditional technologies and issues of integration into the banking environment. After this theoretical beginning the business scenarios are found and modeled followed by the exploration and evaluation. Selected business scenarios are further verified for the purpose of determining the suitability or unsuitability for implementation using technologies and principles of Big Data. The contribution of this work is to find a real use of Big Data in banking, where most of the materials on this topic is very general and vague. This thesis verifies two business scenarios that can big a bank institution high added value if they are implemented with Big Data platform.
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Big Data analysis in healthcare
Nováková, Martina ; Kučera, Jan (advisor) ; Chlapek, Dušan (referee)
This thesis deals with the analysis of Big Data in healthcare. The aim is to define the term Big Data, to acquaint the reader with data growth in the world and in the health sector. Another objective is to explain the concept of a data expert and to define team members of the data experts team. In following chapters phases of the Big Data analysis according to methodology of EMC2 company are defined and basic technologies for analysing Big Data are described. As beneficial and interesting I consider the part dealing with definition of tasks in which Big Data technologies are already used in healthcare. In the practical part I perform the Big Data analysis task focusing on meteorotropic diseases in which I use real medical and meteorological data. The reader is not only acquainted with the one of recommended methods of analysis and with used statistical models, but also with terms from the field of biometeorology and healthcare. An integral part of the analysis is also information about its limitations, the consultation on results, and conclusions of experts in meteorology and healthcare.
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New Trends in Business Intelligence - Focus on Big Data and Hadoop
Korkisch, Josef ; Vacek, Martin (advisor) ; Pour, Jan (referee)
The thesis is focused on several specific areas. The first area is the identification of new trends in the field of Business Intelligence in recent years. Furthermore, the thesis focuses on Big Data (Hadoop). The main purpose is to show the possible use of the Hadoop technology on real-world examples. What is also important, is a section dedicated to comparison of Business Intelligence tools for working with Big Data as it is the section which proofs standpoint from several significant aspects -- angels (analysis, visualization and connectivity). The work is divided into several parts in which the components of Business Intelligence, new trends in BI, Hadoop technology in the context of BI, Hadoop components are described, as well as examples of using Hadoop and comparison of selected BI tools to work with BI is introduced within the thesis. The main purpose of the thesis is to serve as a guide to the world of BI and Big Data (Hadoop).
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Towards Complex Data and Information Quality Management
Pejčoch, David ; Rauch, Jan (advisor) ; Máša, Petr (referee) ; Novotný, Ota (referee) ; Kordík, Pavel (referee)
This work deals with the issue of Data and Information Quality. It critically assesses the current state of knowledge within tvarious methods used for Data Quality Assessment and Data (Information) Quality improvement. It proposes new principles where this critical assessment revealed some gaps. The main idea of this work is the concept of Data and Information Quality Management across the entire universe of data. This universe represents all data sources which respective subject comes into contact with and which are used under its existing or planned processes. For all these data sources this approach considers setting the consistent set of rules, policies and principles with respect to current and potential benefits of these resources and also taking into account the potential risks of their use. An imaginary red thread that runs through the text, the importance of additional knowledge within a process of Data (Information) Quality Management. The introduction of a knowledge base oriented to support the Data (Information) Quality Management (QKB) is therefore one of the fundamental principles of the author proposed a set of best
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Big Data Analytics tools
Miloš, Marek ; Pour, Jan (advisor) ; Andrle, David (referee)
The thesis covers the term for specific data analysis called Big Data. The thesis firstly defines the term Big Data and the need for its creation because of the rising need for deeper data processing and analysis tools and methods. The thesis also covers some of the technical aspects of Big Data tools, focusing on Apache Hadoop in detail. The later chapters contain Big Data market analysis and describe the biggest Big Data competitors and tools. The practical part of the thesis presents a way of using Apache Hadoop to perform data analysis with data from Twitter and the results are then visualized in Tableau.
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Trends in analytical CRM
Heřmanský, Michal ; Šperková, Lucie (advisor) ; Jašek, Pavel (referee)
This thesis describes major trends in the field of analytical CRM. The goal is to identify those trends and compare them with current situation on the CRM market. The thesis is devided among several parts. In the opening part is described Customer Relationship Management and architecture of CRM system. The next part discribes analytical CRM and its standard ways of using. The main part of the thesis is identification of trends. Idetificated trends are characterized and compared with situation on the market. The contribution of this thesis is summarizing current trends in CRM analytics and comparsion of those trends with current CRM market situation.
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