Big Data Methods

  • Teaching

    Details

    Faculty Faculty of Management, Economics and Social Sciences
    Domain Economics
    Code UE-EEP.00142
    Languages English
    Type of lesson Lecture
    Level Master
    Semester SP-2021

    Schedules and rooms

    Summary schedule Thursday 11:15 - 14:00, Hebdomadaire
    Hours per week 3

    Teaching

    Teachers
    Assistants
    Description

    This course discusses quantitative methods for analyzing "big data", i.e. data sets that have either many observations or many variables or both. Firstly, the course covers flexible or "nonparametric" econometric methods for data with many observations, where "flexible" implies that the researcher aims at imposing as few behavioral assumptions as possible. These methods are often more accurate than standard approaches such as OLS, which assumes a linear relation between the explanatory and dependent variables that might not hold in reality.
    Secondly, the course discusses so-called "machine learning" approaches to deal with data that include many variables, in order to optimally exploit the vast information provided in variables. Separating relevant from irrelevant information is key in a world with ever increasing data availability.
    The following topics will be covered in the course:
    * Flexible (non/semiparametric) vs. parametric statistical (or econometric) models
    * Nonparametric regression methods: Kernel regression, series approximation, smoothing splines
    * Methods for choosing smoothing and bandwidth parameters
    * Testing: nonparametric specification and distribution tests
    * Machine learning based on shrinkage and variable selection: Lasso and ridge regression
    * Machine learning based on decision trees, bagged trees, and random forests
    * Introduction to further machine learners: boosting, support vector machines, neural nets, and ensemble methods
    The lecture is accompanied by 4 PC sessions based on the software package "R", in which the methods are applied to empirical data.

    Training objectives

    This course provides students with statistical methods for analyzing "big data" (data sets with many observations and/or variables) that are often more accurate than "standard" tools (such as OLS) hinging on rather restrictive behavioral assumptions. Students should understand the intuition of and differences between the various methods (but are not required to formally reproduce tedious proofs) and how to practically implement them in the statistical software package “R” in order to investigate real world data.

    Condition of access

    Knowledge of introductory econometrics/statistics

    Softskills
    Yes
    Softskills seats 10
    Off field
    No
    BeNeFri
    Yes
    Mobility
    Yes
    UniPop
    No

    Documents

    Bibliography

    J. Racine (2008): “Nonparametric Econometrics: A Primer”, Foundations and Trends in Econometrics, Vol. 3, No 1, pp. 1–88. https://www.nowpublishers.com/article/Details/ECO-009

    G. James, D. Witten, T. Hastie, and R. Tibshirani (2013): An Introduction to Statistical Learning with Applications in R, Springer, New York. http://www-bcf.usc.edu/~gareth/ISL/

    Further references are provided on the moodle site of the course.

  • Dates and rooms
    Date Hour Type of lesson Place
    25.02.2021 11:15 - 14:00 Cours PER 21, Room C130
    04.03.2021 11:15 - 14:00 Cours PER 21, Room C130
    11.03.2021 11:15 - 14:00 Cours PER 21, Room C130
    18.03.2021 11:15 - 14:00 Cours PER 21, Room C130
    25.03.2021 11:15 - 14:00 Cours PER 21, Room C130
    01.04.2021 11:15 - 14:00 Cours PER 21, Room C130
    15.04.2021 11:15 - 14:00 Cours PER 21, Room C130
    22.04.2021 11:15 - 14:00 Cours PER 21, Room C130
    29.04.2021 11:15 - 14:00 Cours PER 21, Room C130
    06.05.2021 11:15 - 14:00 Cours PER 21, Room C130
    20.05.2021 11:15 - 14:00 Cours PER 21, Room C130
    27.05.2021 11:15 - 14:00 Cours PER 21, Room C130
  • Assessments methods

    Written exam - SP-2021, Session d'été 2021

    Date 16.06.2021 11:00 - 12:30
    Assessments methods By rating
    Descriptions of Exams

    Examination time: 90 Min. and participation in PC labs

    Written exam - SP-2021, Session de rattrapage 2021

    Date 02.09.2021 11:00 - 12:30
    Assessments methods By rating
    Descriptions of Exams

    Examination time: 90 Min. and participation in PC labs

  • Assignment
    Valid for the following curricula:
    Complementary learnings in SES
    Version: ens_compl_ses
    803 - MA course offering for Mobility Students

    Doc - Economics
    Version: 2002/SA_V01
    Cours a choix > Wahlkurse UNIFR

    Doc - Economie quantitative
    Version: 2002/SA_V01
    Cours a choix > Wahlkurse UNIFR

    Doc - Management
    Version: 2002/SA_V01
    Cours a choix > Wahlkurse UNIFR

    Doc - Sciences sociales
    Version: 2002/SA_V01
    Cours a choix > Wahlkurse UNIFR

    Doc - Sciences économiques et sociales
    Version: 2002/SA_V01
    Cours a choix > Wahlkurse UNIFR

    European Studies 30 [MA]
    Version: SA14_MA_PS_bil_v01
    Enjeux économiques, politiques et sociaux en Europe > Modul "Economie" (Option A)

    Ma - Accounting and Finance - 90 ECTS
    Version: 2021/SA_V01
    Course - 72 ECTS > Minimum 0 / maximum 1 optional master course offered at the University of Fribourg, if 72 ECTS not yet reached in the above modules > SES Master level courses
    Course - 72 ECTS > Modules "Data Analytics" and "Audit et Fiscalité": min. 3 courses > DAT: Data Analytics

    Ma - Business Communication : Business Informatics - 90 ECTS
    Version: 2020/SA_V01
    Courses - 60 ECTS > Option Group > Information Management > Cours > Modules management > DIG: Managing Digitalisation
    Courses - 60 ECTS > Option Group > Information Management > Cours > Modules management > DAT: Data Analytics

    Ma - Business Communication : Economics - 90 ECTS
    Version: 2021/SA_V01
    Courses > Option Group > Economics > Kurse der Module Master Volkswirtschaftslehre, ohne die Module 4 und 9

    Ma - Business Communication : Management - 90 ECTS
    Version: 2021/SA_V01
    Courses - 60 ECTS > Chosen Option > Management > 30 ECTS parmi les modules : > DIG: Managing Digitalisation
    Courses - 60 ECTS > Chosen Option > Management > 30 ECTS parmi les modules : > DAT: Data Analytics

    Ma - Business Informatics - 90 ECTS
    Version: 2020/SA-v01
    Classes - min. 45 ECTS > Modules management - max. 15 ECTS > DIG: Managing Digitalisation
    Classes - min. 45 ECTS > Modules management - max. 15 ECTS > DAT: Data Analytics

    Ma - Communication and Media Research - 90 ECTS
    Version: 2015/SA_V01
    Courses - 60 ECTS > Inter- and Transdisciplinary Perspectives > SES Master level courses

    Ma - Communication and Society - 90 ECTS
    Version: 2021/SA_V03
    Forschungsbereiche > Forschungsbereiche > Inter- & Transdisciplinary Perspectives > SES Master level courses

    Ma - Data Analytics & Economics - 90 ECTS
    Version: 2020/SA-v01
    Courses min 63 ECTS > Mandatory Modules (45 to 63 ECTS) > Module I: Data Analytics (Data)

    Ma - Economics - 90 ECTS
    Version: 2021/SA_V03
    Le choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Quantitative Economics > 369 - Option : "Quantitative Economics"
    Elective courses > 369 - Elective courses of the Master in Political Economy
    Elective courses > Wahlkurse der SES-Fakultät - max. 15 ECTS > SES Master level courses

    Ma - European Business - 90 ECTS
    Version: 2017/SA_v01
    Courses - 63 ECTS > Additional courses: Any Master courses of the Faculty of Economics and Social Sciences, as well as maximum 9 ECTS from all Master programmes of the University. > SES Master level courses

    Ma - Information Management - 90 ECTS
    Version: 2019/SA_V01
    Classes - min. 45 ECTS > Modules management - max. 15 ECTS > DIG: Managing Digitalisation

    Ma - International and European Business - 90 ECTS
    Version: 2021/SA_V01
    Courses > Modules > One complete module taken from the following list > Groupe d'élément de validation du Module DAT > DAT: Data Analytics
    Courses > Modules > One complete module taken from the following list > Groupe d'élément de validation du Module DIG > DIG: Managing Digitalisation
    Courses > Additional courses: Any Master courses of the Faculty of Economics and Social Sciences, as well as maximum 9 ECTS from all Master programmes of the University. > SES Master level courses

    Ma - Management - 90 ECTS
    Version: 2021/SA_V01
    Courses: min. 72 ECTS > Elective Courses : max. 18 ECTS > SES Master level courses
    Courses: min. 72 ECTS > Modules - min 54 ECTS > Groupe d'élément de validation du Module DAT > DAT: Data Analytics
    Courses: min. 72 ECTS > Modules - min 54 ECTS > Groupe d'élément de validation du Module DIG > DIG: Managing Digitalisation

    Ma - Management - 90 ECTS [MA]
    Version: 2017/SA_v01
    Courses: min. 63 ECTS > Cours facultatifs : max. 18 ECTS > SES Master level courses

    Ma - Marketing - 90 ECTS
    Version: 2021/SA_V02
    Courses > Groupe d'élément de validation du Module DIG > DIG: Managing Digitalisation
    Courses > Groupe d'élément de validation du Module DAT > DAT: Data Analytics
    Courses > Elective Master courses from the whole university > SES Master level courses

    Ma - Public Economics and Public Finance - 90 ECTS
    Version: 2015/SA_V01_MA_VWL_DD
    Cours > Up to 45 ECTS credits must fulfill the conditions required for the specialisation, including the modules 1, 2 and 6 with a min. of 12 ECTS in each. > Module 6: Quantitative Economics

    MiMa - Business Informatics - 30 ECTS
    Version: 2020/SA_V01
    Cours > Modules management > DIG: Managing Digitalisation
    Cours > Modules management > DAT: Data Analytics

    MiMa - Data Analytics - 30 ECTS
    Version: 2020/SA-v01
    À choix 18 crédits ECTS

    MiMa - Economics - 30 ECTS
    Version: 2021/SA_V01
    Elective courses > 369 - Elective courses of the Master in Political Economy

    MiMa - Gestion d'entreprise - 30 ECTS
    Version: 2021/SA_V01
    Elective courses - 30 ECTS > DIG: Managing Digitalisation
    Elective courses - 30 ECTS > DAT: Data Analytics