Software environment R
Course: Economic analysis and statistics
Structural unit: Faculty of Economics
Title
Software environment R
Code
1.7
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
1 Semester
Number of ECTS credits allocated
5
Learning outcomes
PLO 3. To communicate freely on professional and scientific issues in the state and foreign languages orally and in writing
PLO 8. Collect, process and analyze statistical data, scientific and analytical materials needed to solve complex economic problems.
PLO10. Apply modern information technologies and specialized software in socio-economic research and management of socio-economic systems.
PLO 17. Use modern educational and research technologies in the field of economics.
Form of study
Distance form
Prerequisites and co-requisites
1. Know: the theory of methods of statistical analysis (disciplines: Statistics, Statistics for economists).
2. Possess: programming methods (disciplines: Fundamentals of programming, programming, Python and others).
Course content
The curriculum consists of one module: Software environment R. How to program in R, how to use programming language R for effective analysis of economic data, installation and configuration of software required for statistical programming environment, description of general concepts of programming languages during their implementation at a high level of statistical language, practical issues of statistical calculations, including programming in R, reading data in R.
Recommended or required reading and other learning resources/tools
1. S. van Buuren. Flexible Imputation of Missing Data. Chapman & Hall/CRC Interdisciplinary Statistics. CRC Press LLC, 2018. ISBN 9781138588318
2. Dan E. Kelley. Oceanographic Analysis with R. Springer-Verlag, New York, October 2018. ISBN 978-1-4939-8842-6
3. Sarah Stowell. Using R for Statistics. Apress, 2014. ISBN 978-1484201404
4. Vikram Dayal. An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing. Springer, 2015. ISBN 978-81-322-2340-5
5. C. Sun. Empirical Research in Economics: Growing up with R. Pine Square, Starkville, Mississippi, USA, 1st edition, 2015
Planned learning activities and teaching methods
The purpose of the discipline - Formation of students' necessary theoretical knowledge and practical skills for the effective use of programming language R with the use of special libraries of analysis and visualization of data for further use in professional activities.
Assessment methods and criteria
Forms of student assessment: (max. Points 60 / min. Points 36)
1. Performance of practical and laboratory works - 48 points / 30 points;
2. Independent work - estimated individual task - 12 points / 6 points;
- final assessment in the form of an exam
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline