v2.2.8 (2209)

PA - C8 - MAP536R : R for Data Science

Domaine > Mathématiques appliquées.

Diplôme(s) concerné(s)

Parcours de rattachement

Format des notes

Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme Data Science for Business

Le rattrapage est autorisé
    L'UE est acquise si note finale transposée >= C
    • Crédits ECTS acquis : 3 ECTS

    Programme détaillé

    Syllabus – M1 BIg data X-HEC

     

    practical infos

     

    Lectures : 10 AM to 1 PM

    Lectures consist in presentations with small runnable examples

     

    Lab time : 2 PM to 5 PM

    During lab time, students are in smaller groups with a dedicated professor and are supposed to work on provided practical tutorials in relation with lectures.

     

     

    outline :

     

    26th september - INTRoduction

    Who : Diane, Vincent & Colin

     

                   - introduction to R

                   - workspace, working directory, RStudio project

                   - reproducible research

                   - the console

                   - R objects landscape

                   - good practices

                  

                   => lab time : practical tutorials in small groups

                  

    Learning goals :

    - master the R objects

    - set a clean and reproducible workspace

    - submit homework

     

     

    3rd october – DATA grasping and wrangling

    Who : Diane, Vincent & Sébastien

                   - importing data

                   - manipulating tables

                   - reshaping data

                   - parameterized reports

                   - data visualisation                       

                   - summarising data

                   - assignment n°1 (individual project)

                  

                   => lab time : practical tutorials in small groups

                  

    Learning goals :

    - feel at ease to debunk and grasp content of any dataset in any kind of database

    - gain a data-driven approach

    - detect and comprehend relations between variables

    - assess data quality and reliability

     

     

    10th october – Broaden data knowledge

    Who : Diane, Vincent & Sébastien

     

                   - Exploratory  multivariate analysis

                   - Maps and stats

     

                   => lab time : practical tutorials in small groups

     

    Learning goals :

    - go into dataset in depth

    - take advantage of spatial data

     

    12th november – R engineering

    Who : Diane, Colin & Sébastien

                   - programming (including tidyeval)

                   - mapping tasks (purrr)

                   - packaging

                   - assignment n° 2 (group projects)

                  

                   =>  feedbacks on assignment n° 1

                  

    Learning goals :

    - learn when and how to automate scripts

    - safely store your work in a package

     

    19th november -

    Who : Diane, Vincent et Sébastien

                   - Shiny !

                   => lab time : practical tutorials in small group with mentoring

                  

    26th november -

    26 novembre : Diane, Vincent et Sébastien

                   - API : querying and building

                   - webscraping

                   - text mining

                  

                   => lab time : practical tutorials in small group with mentoring

     

    Learning goals :

    - understand what an application programming interface is

    - ethically scrap the web when no API available

    - extract data from text

     

    10th december - mentoring

    Who : Diane, Vincent et Colin

    mentoring on projets all day

     

    17th december – group projects defense

    Who : Diane et Vincent

     

     

    grades

     

    40 % individual project

    60 % group project

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