Salta al contenuto principale della pagina

SOFTWARE R

CODE 106839
ACADEMIC YEAR 2022/2023
CREDITS
  • 3 cfu during the 1st year of 11267 ECONOMICS AND DATA SCIENCE (LM-56) - GENOVA
  • LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    PREREQUISITES
    Prerequisites (for future units)
    This unit is a prerequisite for:
    • ECONOMICS AND DATA SCIENCE 11267 (coorte 2022/2023)
    • STATISTICAL MODELS 41601
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course is aimed at providing the basic skills related to the use of the R software and, more generally, the concepts inherent to statistical programming with particular attention to economic and financial applications.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The main objective of the course is to provide knowledge of the programming basics of the software R in order to properly develop financial models and to perform statistical analysis 

    AIMS AND LEARNING OUTCOMES

    The course aims to make students acquire the technical skills and programming criteria essential to deal with mathematical-statistical models in the economic-financial field. As the main learning result, the course aims to provide the basic knowledge in order to deal with the construction, implementation, and data processing of economic and financial models.

    PREREQUISITES

    A basic knowledge deriving from a Bachelor’s degree, including the fundamentals of mathematical and statistical disciplines.

    TEACHING METHODS

    Lectures and exercises. Alternatively, depending on the sanitary situation, on-line learning lessons on TEAMS platform.

    SYLLABUS/CONTENT

    The course includes a series of topics developed sequentially according to the following program:

    Part I: R console and RStudio IDE
    Part II: Vectors – assignment operator; numeric-logic-character and index vectors
    Part III: Mode, Type, Classes and Attributes; recursive structures and object coercion
    Part IV: String manipulation, formats and regular expressions
    Part V: Factors: levels and categorical variables
    Part VI: Matrices and Arrays: bi-dimensional array, matrix operators and tensors
    Part VII: Lists, Data frames and Tibbles
    Part VIII: import and export of data
    Part IX: R packages – extending the R functionalities (libraries and namespaces)
    Part X: Graphics – low-level and high-level architectures, graphics layout
    Part XI: Programming – Functions, Debugging, Conditional Execution, Loops and Vectorization
    Part XII: Statistical Analysis and thematic discussion of codes that involve economic and financial models

    RECOMMENDED READING/BIBLIOGRAPHY

    Slides, multimedia supports and specific books are provided directly to students. In addition to the educational material available on the TEAMS channel (/ aulaweb) of the course, other bibliographic information will be provided upon request.

    TEACHERS AND EXAM BOARD

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    The exam is oral.

    The test consists in coding and commenting an exercise.

    ASSESSMENT METHODS

    The oral exam allows to verify the actual level of learning in accordance with the objectives and the expected results.

    Specifically, the student has to display a satisfactory theoretical approach to the discipline as well as programming skills.

    The examination assessment parameters include the quality of the developed code, the functionality, the reasoning and the ability to critically analyze the proposed solutions and the results obtained.