MPE_AVED Analysis and Visualization of Economic Data

Faculty of Economics and Administration
Autumn 2023
Extent and Intensity
1/1/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Michal Kvasnička, Ph.D. (seminar tutor)
doc. Ing. Štěpán Mikula, Ph.D. (seminar tutor)
Guaranteed by
Ing. Michal Kvasnička, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable of Seminar Groups
MPE_AVED/01: Wed 8:00–9:50 VT204, except Wed 20. 9., except Wed 8. 11., M. Kvasnička, Š. Mikula
Prerequisites
! BPE_AVED Data Analysis & Visualization
No previous knowledge of programming or R language is required. Only the essential ability to work with computers and willingness to learn to write code is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 35/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50
fields of study / plans the course is directly associated with
Course objectives
The goal of this course is to provide students with practical tools for preparing, analyzing, and visualizing economic data.


Students can use the acquired skills while working on their theses and in commercial practice, particularly in analyzing financial markets, international trade, market organizations, migration, transport economics, competition, microeconomics, macroeconomics, and other tasks. The course will also lay the foundation for further study and application of advanced statistical methods, econometrics, data mining, and data science.


Emphasis is placed on the practical mastery of the tools mostly used to analyze economic data.


Examples of the use of tools can be seen at https://aved.econ.muni.cz/appetizer.html.



Classes will be held in R language. R is the world's most widely used statistical data analysis tool, which lets you apply all statistical, econometric, data-mining-and other methods without additional auxiliary software (such as Excel, Gretl, Statistica, SPSS, etc.). R is used in both academia and commercial practice. It is used by companies such as Google, Facebook, and Microsoft. R can be downloaded free of charge for all operating systems at https://cran.r-project.org/.



Most of the necessary literature (including the required one) is available legally online.

Learning outcomes
Completing the course, students will gain the following skills:

  • They will be able to retrieve data in almost any format and structure from local resources and online databases, clean it, and convert it into a convenient structure that allows easy analysis.
  • They will be able to handle large volumes of data: to transform them, aggregate them, and combine different data sets.
  • They will be able to detect errors in data sets systematically.
  • They will be able to visualize individual data of various types and relationships between them in an advanced way.
  • They will be able to carry out an initial exploratory data analysis and subsequent statistical and econometric analysis.
  • They will be able to handle their projects so that they can be replicated and updated automatically, for example, if they get new data.
    • Syllabus
    • Introduction to R: installation, startup, and shutdown of R, installation, use of libraries, help, vignettes, development environment (RStudio), writing and running scripts.
    • Data and variables: assigning data to variables; datatypes, coercion; arithmetic operations; loading and saving data.
    • Information about data: attributes, subsets, factors, dates, objects, and their classes.
    • Automatic Data Processing 1: function calls, basic mathematical and statistical functions, and tests.
    • Automatic Data Processing 2: custom functions, functionals; sequences, sampling, and set operations.
    • Automatic Data Processing 3: Working with strings -- regular expressions, transformations of strings, and retrieving data from strings.
    • Automatic Data Processing 4: Well formatted data -- well-formatted data structure and data conversion in a well-formatted data.
    • Automatic Data Processing 5: transformations, selections from data, data aggregation, linking information from different data sets.
    • Data Visualization 1: Grammar of graphs and visualization of univariate data.
    • Data Visualization 2: Visualizing relationships in data.
    • Introduction to Econometrics in R.
    • Exploratory data analysis (EDA).
    • Reproducible research: producing concise and maintainable data structures and code, creating automatically generated documents that contain text and outputs of data analysis (when there are 13 weeks in the semester).
    Literature
      required literature
    • Kvasnička--Mikula: Analýza a vizualizace dat v jazyce R, 2023, https://aved.econ.muni.cz/.
      recommended literature
    • WICKHAM, Hadley and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly, 2016, xxv, 492. ISBN 9781491910399. info
    • WICKHAM, Hadley and Carson SIEVERT. Ggplot2 : elegant graphics for data analysis. Second edition. Switzerland: Springer, 2016, xvi, 260. ISBN 9783319242750. info
    • HADLEY, Wickham. Tidy Data. Advances in Business-Related Scientific Research Conference 2014 in Roma (ABSRC 2014 Roma). 2014, vol. 59, No 10. info
    • KABACOFF, Robert. R in action : data analysis and graphics with R. Shelter Island, NY: Manning, 2011, xxiv, 447. ISBN 9781935182399. info
    • WICKHAM, Hadley. Advanced R. Boca Raton: CRC Press, 2015, xxii, 456. ISBN 9781466586963. info
    • KLEIBER, Christian and Achim ZEILEIS. Applied Econometrics with R. [New York]: Springer, 2008, x, 221. ISBN 9780387773162. info
    • VERZANI, John. Using R for introductory statistics. Boca Raton: Chapman & Hall/CRC, 2005, xvi, 414. ISBN 1584884509. info
    Teaching methods
    AVED consists of home preparation and tutorials. Students are assumed to watch video lectures and read lecture notes at home. Tutorials are held in a computer lab. They are devoted to applying acquired skills to real or stylized data sets.
    Assessment methods
    The course is evaluated based on attendance (10%), in-class tests (10%), homework (40%), and a final practical exam (40 %). Homework and final practical exam include preparation, analysis, and visualization of given data. Completing the course requires obtaining at least 50% of the points from the final exam and 50% of the total possible points.


    Homework is to be submitted on an ongoing basis in the form of code.


    During the exam, you can use all the materials available in the development environment, the course study materials, and your notes. Communication with living people and access to the Internet is prohibited.



    It is also possible to enroll in the course while studying abroad (e.g., Erasmus). The student will prepare homework during the semester and will write the final test after returning from abroad. Please get in touch with the teachers before departure.



    Any copying, recording, or taking out the tests, use of unauthorized devices and means of communication, or other distortions in the objectivity test (credit) will be considered a failure to meet the   course completion and a gross violation of study regulations. Consequently, the teacher closes the test (credit) score in   IS grade "F," and the Dean initiates disciplinary proceedings, which may result in up to graduation.

    Language of instruction
    Czech
    Further Comments
    Study Materials
    The course is taught annually.
    Teacher's information
    https://aved.econ.muni.cz/appetizer.html
    The course is also listed under the following terms Autumn 2024.
    • Enrolment Statistics (Autumn 2023, recent)
    • Permalink: https://is.muni.cz/course/econ/autumn2023/MPE_AVED