BPE_AVED Analysis and Visualization of Economic Data

Faculty of Economics and Administration
Autumn 2016
Extent and Intensity
0/2. 4 credit(s). Type of Completion: graded credit.
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
BPE_AVED/01: Thu 11:05–12:45 VT204, M. Kvasnička, Š. Mikula
Prerequisites
No previous knowledge of programming, R, statistics, or econometrics is required. Only basic ability to work with computers 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 45 student(s).
Current registration and enrolment status: enrolled: 0/45, only registered: 0/45, only registered with preference (fields directly associated with the programme): 0/45
fields of study / plans the course is directly associated with
there are 10 fields of study the course is directly associated with, display
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 not only while working on their theses, but in commercial practice too; in particular in the analysis of financial markets, international trade, market organizations, migration, transport economics, competition, microeconomic, macroeconomic, and other tasks. The course will also lay the necessary 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, which are mostly used in the analysis of economic data.
Examples of use of tools can be seen at https://is.muni.cz/auth/el/1456/podzim2016/BPE_AVED/um/62799066/priklad_analyzy_a_vizualizace_dat.html


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 into a convenient structure that allows the 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 systematically detect errors in data sets.
  • 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.


    Classes will be held in software R. 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 the use of additional auxiliary software (such as Excel, Gretl, Statistica, SPSS, etc.). R is used in both academia and in commercial practice. It is used by companies such as Google, Facebook, and Microsoft. R can be download 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.
    • Syllabus
    • 1. Introduction to R: installation, startup and shutdown of R, installation and use of libraries, help, vignettes, development environment (RStudio), writing and running scripts
    • 2. Data and variables: assigning data to variables; datatypes, coersion; arithmetic operations; loading and saving data
    • 3. Information about data: attributes, subsets, factors, dates, objects, and their classes
    • 4. Automatic Data Processing 1: function calls, basic mathematical and statistical functions and tests
    • 5. Automatic Data Processing 2: custom functions, functionals; sequences, sampling, and set operations
    • 6. Automatic Data Processing 3: Working with strings -- regular expressions, transformations of strings, and retrieving data from strings
    • 7. Automatic Data Processing 4: Well formatted data -- well formatted data structure and data conversion in a well-formatted data
    • 8. Automatic Data Processing 5:transformations, selections from data, data aggregation, linking information from different data sets
    • 9. Data Visualization 1: Grammar of graphs and visualization of univariate data
    • 10. Data Visualization 2: Visualizing relationships in data
    • 11. Data Visualization 3: Exploratory data analysis (EDA) and testing data consistency
    • 12. Introduction to Econometrics in R
    • 13. Reproducible research: producing concise and maintainable data structures and code, creating automatically generated documents that contain text and outputs of data analysis
    Literature
      required literature
    • WICKHAM, Hadley. Advanced R. Boca Raton: CRC Press, 2015, xxii, 456. ISBN 9781466586963. info
    • HADLEY, Wickham. Tidy Data. Advances in Business-Related Scientific Research Conference 2014 in Roma (ABSRC 2014 Roma). 2014, vol. 59, No 10. info
    • WICKHAM, Hadley. Ggplot2 : elegant graphics for data analysis. Dordrecht: Springer, 2009, viii, 212. ISBN 9780387981413. info
      recommended literature
    • KABACOFF, Robert. R in action : data analysis and graphics with R. Shelter Island, NY: Manning, 2011, xxiv, 447. ISBN 9781935182399. 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
    Lessons are held in the computer lab. The first shorter part of lessons is devoted to explaining the basic theory and concepts. The greater part of lessons is devoted to practical application of acquired skills to real or stylized data sets.
    Assessment methods
    The course is evaluated on the basis of attendance (20%), homework (40%) and a final practical exam (40 %). Homework and final practical exam include preparation, analysis, and visualization of given data. The successful completion of the course requires obtainint at least 50% of 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 test, it is possible to use all materials available in IDE, Internet, and personal notes. Communication with living people by all means is prohibited.


    Any copying, recording or taking out the tests, use of unauthorized devices and means of communication or other distortions 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 Dean initiate disciplinary proceedings which may result in up to graduation.
    Language of instruction
    Czech
    Further Comments
    Study Materials
    The course is taught annually.
    Listed among pre-requisites of other courses
    Teacher's information
    https://is.muni.cz/auth/el/1456/podzim2016/BPE_AVED/
    The course is also listed under the following terms Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022.
    • Enrolment Statistics (Autumn 2016, recent)
    • Permalink: https://is.muni.cz/course/econ/autumn2016/BPE_AVED