Mandatory courses for 1st year students of Data Science and Business Analytics (course group defined by Faculty of Economic Sciences)
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2024Z - Winter semester 2024/25 2024L - Summer semester 2024/25 2025Z - Winter semester 2025/26 (there could be semester, trimester or one-year classes) |
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| 2024Z | 2024L | 2025Z | |||||||
| 2400-DS1AE | n/a |
 
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Classes
Summer semester 2024/25
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Brief description
The lecture and exercises on econometrics are to familiarize students with advanced econometric techniques, their properties and the most important applications. The lecture concerns: models estimated on the time series and panels as well as the applications of the MLE and GMM estimators. The lecture is intended for students of the Data Science programme. The lecture uses concepts in the field of linear algebra, mathematical analysis, probability calculus, descriptive and mathematical statistics and basic econometrics. |
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| 2400-DS1APR | n/a |
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Classes
Summer semester 2024/25
Groups
Brief description
The aim of the course is to teach advanced programming methods in R, create complex scripts / programs, and evaluate their time complexity, as well as create own functions and packages. Firstly, the functions of the dplyr package will be presented to effectively aggregate and analyze data in subgroups, and use pipe operator %>% for a more readable code representation of several nested commands. Then the main focus will be on the automation of repetitive activities. In this context, while and for loops will be discussed, as well as the alternative R functions of the apply family. Also, elements will be introduced to conditionally execute program fragments and run code in batch mode. We will also discuss creating own functions and packages. An important part of the course will be showing tools for analyzing the code, evaluating its time effectiveness, and identifying and handling errors. The use of C ++ basics in R (Rcpp package) will also be discussed for example to replace slow R loops |
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| 2400-DS1AL | n/a |
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Classes
Summer semester 2024/25
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Brief description
The objective of this course is to teach the students basic algorithms and data structures. In each topic, the students will learn the underlying theory as well as the application of the concepts in commonly used programming languages (C++, Python). The final grading is based on grades from programming tasks, short tests, and the written exam. |
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| 2400-DS1AMA |
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Classes
Winter semester 2024/25
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Brief description
The aim of this 45-hours lecture is to present modern macroeconomic methods and models used both by researchers and analysts to understand and predict macroeconomic phenomena. Upon completion students will be able to use acquired tools to explain and interpret the workings of the macroeconomy at an advanced level. The course is composed of three parts. The first part is devoted to static (intersectoral) general equilibrium analysis. The second part covers core growth facts and theories. The third part is devoted to analyzing and modeling business cycles phenomena. |
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| 2400-DS1AMI |
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n/a |
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Classes
Winter semester 2024/25
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Brief description
The aim of this course is to present a new approach to microeconomics analysis, which is based on experimental and computational methods. |
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| 2400-DS1CA |
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Classes
Winter semester 2024/25
Groups
Brief description
No brief description found, go to course home page to get more information.
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| 2400-FIM1POWI |
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n/a | n/a |
Classes
Winter semester 2024/25
Groups
Brief description
The aim of the course is to familiarize students with the basic information in the field of copyright and certain general aspects of industrial property rights. Course participant will acquire knowledge about the sources of law and fundamental concepts relating to intellectual property rights and will be able to apply them at basic level. Students will be aware of protection of intellectual property rights (in particular in the field of copyright) and the complexity of these issues. In the 2020/21 academic year, the lectures are held in the form of distant learning due to the epidemiological situation. |
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| 2400-DS1DS |
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Classes
Winter semester 2024/25
Groups
Brief description
This course provides a brief introduction to Data Science. The main goal is to shine a light on all areas related to Data Science starting from description of most popular tasks like data wrangling and exploration, task automation and predictive modeling through explanation of true meaning of “Big Data” description of most popular data science software and finishing with data science applications and reality of everyday work. |
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| 2400-DS1ML1 | n/a |
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Classes
Summer semester 2024/25
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Brief description
This course provides a broad perspective on application of Machine Learning methods in supervised learning for regression and classification problems. It includes both the description of theoretical background and practical examples and illustrations. The course covers the basis of machine learning including measuring performance, model testing, details of validation methods, feature engineering and selection, simple linear and logistic regression, discriminant analysis as well as K-nearest neighbors, Support Vector Machines, ridge and Lasso regression modelling methods. |
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| 2400-DS1SQL |
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Classes
Winter semester 2024/25
Groups
Brief description
The course consists of two parts: introduction to SQL (Part1) and introduction to Python (Part2). The first part of the course provides participants with a broad introduction to SQL in the following topics :data administration (to create tables, indexes) data manipulation (to add, modify, delete and retrieve data),query construction to extract useful information. In the second part , the material covers the use of data structures, data manipulation and visualization in Python. The course ends with presentation of methods for joining SQL queries and Python program. |
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| 2400-DS1R |
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Classes
Winter semester 2024/25
Groups
Brief description
The aim of the course is to introduce the statistical program R-CRAN. The course is dedicated to master's program students of Econometrics and Informatics, Data Science programs and for all who want to learn advanced statistical software and use it both in business analytics and in scientific work. The course is realized in a computer lab. Passing requirements: the final exam. |
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| 2400-DS1ST |
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Classes
Winter semester 2024/25
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Brief description
The laboratory gives theoretical basis for basic statistics analysis and explanatory data analysis. Participants will acquire theoretical knowledge about basics concepts and tools used in statistics and EDA. Every topic discussed during the lecture will be illustrated with case study examples and exercises to be solved by students. The course is obligatory for students of Data Science. |
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| 2400-DS1UL |
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Classes
Winter semester 2024/25
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Brief description
Unsupervised learning course is focused on exploring data structure and extracting crucial information from unlabeled data without any particular variable of interest. The course is based on three subfields of unsupervised learning: clustering, dimension reduction and association rule learning. Both theoretical and practical aspects are going to be discussed. The course is realized in a computer lab. Passing requirements: group projects. The course is dedicated for graduate students (Econometrics and Informatics, Data Science). |
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| 2400-DS1WSMS | n/a |
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Classes
Summer semester 2024/25
Groups
Brief description
The Web Scraping class is dedicated to extracting data from the web in an automated way. Through the course, students will gain the knowledge and skills to extract data from the web. During the course, key web scraping techniques will be presented. In particular, course participants will be able to select appropriate tools and prepare a scraping programme according to their needs. The course will focus on the practical aspects of web scraping. The course is conducted in the Python programming language in the form of a laboratory. |
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