v1.16.1

PA - C8 - MAP536R : R for Data Science

Domaine > Mathématiques appliquées.

Diplôme(s) concerné(s)

Parcours de rattachement

Format des notes

Numérique sur 20

Pour les étudiants du diplôme Data Science for Business

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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