Mandatory courses for 2nd year students of Data Science and Business Analytics (course group defined by Faculty of Economic Sciences)
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2023Z - Winter semester 2023/24 2023L - Summer semester 2023/24 2024Z - Winter semester 2024/25 2024L - Summer semester 2024/25 (there could be semester, trimester or one-year classes) |
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| 2400-DS2AV |
 
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Classes
Winter semester 2023/24
Groups
Brief description
The aim of the course is to teach advanced visualisation methods in R for design adequate and publication ready graphs. For this purpose ggplot2 package will be presented thoroughly from the basics to the advanced applications. Participants will learn about syntax rules of ggplot2, different geometries (e.g. geom_line, geom_point, geom_bar), editing each of the element of the graph (theme, scale functions). Finally the overview of advanced type of graphs will be presented including maps and 2 dimensional distribution. During the semester interactive graphs in various packages will be discussed. An important part of the course will be showing how to use R packages for data visualisation effectively. |
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| 2400-DS2AF |
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Classes
Winter semester 2023/24
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Brief description
Applied Finance course consists of a 30 hour lecture and 15 hour lab. It’s a patchwork course conducted by several lecturers and is covering different current topics in the area such as machine learning and statistical tools in algorithmic trading, path dependent option pricing, linear factor models, risk modelling in financial institutions, financial management and capital structure. The details may vary year to year depending on the professors invited to give lectures |
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| 2400-DS2BDA |
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Classes
Winter semester 2023/24
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Brief description
The laboratory gives practical experience in using Hadoop ecosystem technologies for Big Data Analytics. Participants will learn how to apply data analysis and machine learning techniques they have learned in previous courses to Big Data datasets. Student will not learn new techniques. The course will be focused on practical experience and understanding concepts behind used technologies. |
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| 2400-DS2ML2 |
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Classes
Winter semester 2023/24
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Brief description
The course covers more advanced methods of machine learning: decission trees and neural networks. Practical examples include methods measuring model performance, methods of model validating/testing, feature engineering and ensembling learning. Both theoretical background and practical empirical examples are discussed. Practical applications cover problems of classification and regression, image/pattern recognition, processing and forecasting of sequences, time-series analysis and deployment of methods in the cloud enviroment. |
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| 2400-DS2NEG |
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Classes
Winter semester 2023/24
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Brief description
The aim of the course is to transfer knowledge and practice practical skills related to efficient communication, solving conflicts and negotiations. Negotiations will be presented as effective method of solving conflicts and group decision-making, requiring high level of communications skills. The students will learn and practice building negotiation strategies and using techniques of conducting negotiations, including socio-economic and cultural context of negotiations. In order to obtain the educational effects concerning students competences of participating in negotiation processes, workshops will take place during the classes, enabling students taking part in negotiation games. The assessment of these team games and the assessment of an essay written by the students individually will be the basis for obtaining the credit for the subject. |
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| 2400-DS2RR | n/a |
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Classes
Summer semester 2023/24
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Brief description
The main goal of the course is to present the basic concepts of reproducibility and repeatability in research, their significance in scientific and commercial research and development processes, and to impart to students basic practical knowledge about several of the most popular modern tools for reproducibility in the industry. Upon completion of the course, students will understand the general concept of research reproducibility and comprehend which tool can be used in a given context. They will also acquire skills in using computer tools that enable achieving reproducibility and repeatability of research, and they will be able to apply the skills acquired during the course in participating in modern scientific and commercial data science projects. |
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| 2400-DS2TMS |
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Classes
Winter semester 2023/24
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Brief description
Considering the volume of text information over the business, research and the Internet, text mining becomes a key skill. Text mining focuses mostly on unstructured text data that comes in words. The main aim of text mining is to extract the actionable knowledge from the text. This course covers the important underpinnings of text mining, as well as its applications in exploring patterns and trends in social media usage throughout designing and carrying out own research of data. The tools supporting different methods of text processing and analysis will be applied in order to address various needs. Mainly practical aspects are going to be discussed. The course is dedicated for graduate students (Econometrics and Informatics, Data Science). |
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| 2400-DS2UB | n/a |
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Classes
Summer semester 2023/24
Groups
Brief description
Economic theory and business practice have been researching the functioning of enterprises for years, explaining their strategies of acting in the business environment and ways of gathering their know-how in order to support business decision-making. Therefore, understanding the functioning of business is crucial for working in the socio-economic space. The course covers the most important business issues in a knowledge-based economy, closely related to enterprises operating in diverse, international, multicultural environments. The course is conducted in close cooperation with enterprises, which allows for a practical discussion of the use of tools supporting business activities and gives an insight into one of the most dynamically developing sectors of the Polish economy - the sector of modern business services. The course is intended for second-cycle students (Data Science). |
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