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Programming Tools in Data Science

  • Teacher(s):  
  • Course given in: English
  • ECTS Credits:
  • Schedule: Autumn Semester 2019-2020, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

    Master of Science (MSc) in Management, Orientation Marketing

    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Business Analytics

 

Objectives

The objective of this course is to provide an introduction to programming using the R language. It will also provide students with notions of data management, manipulation and analysis as well as of reproducible research, result-sharing and version control (using GitHub). At the end of the class, students should be able to construct their own R package, make it available on GitHub, document it using literate programming and render it visible by making a website.

Contents

This class is intended to introduce to the students a wide range of programming tools using the R language. Tentative list of topics that will be discussed in this class are listed below:

  • Reproducible research: knitr and rmarkdown
  • Version control: GitHub
  • Introduction to programming: Data structures, logical operators, control structures and functions
  • Visualizations: Exploratory data analysis with Base R and ggplot2
  • R packages: Construction of R-packages using devtools, roxygen2 and pkgdown
  • Communication: webiste creation via blogdown, Web application via shiny
  • Webscrapping: Automatic extraction of data from websites using SelectorGadget, rvest and quantmod
  • High performance computing: R and C++ integration via Rcpp, parallel computing.

No IT background is assumed from the students but a strong will to learn useful and practical programming skills.

References

This class is based on the textbook: “An Introduction to Statistical Programming Methods with R” , which is available here: http://r.smac-group.com.

The following texts will be heavily referenced:

Check the website of the course for more references.

Pre-requisites

This course is complementary to the Data Science in Business Analytics class, taught in Spring 2019. Although not mandatory, we recommend the students to follow the Data Science in Business Analytics class prior to ours as it will facilitate they learning curve and diminish the importance of the workload that this class represents.

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

The learning outcomes are continuously assessed during the semester with the homeworks (group), the project (group) and the participation (individual) (check this link for more details).

Retake

Exam:
Without exam (cf. terms)  
Evaluation:

A second opportunity to pass the homeworks and/or the project is proposed to the failing students/groups.



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