[Course] Bootcamp 1: The fundamentals in R

Data types and structures in R, inputting & outputting data and manipulating data sets.

Thierry Warin https://warin.ca/aboutme.html (HEC Montréal and CIRANO (Canada))https://www.hec.ca/en/profs/thierry.warin.html

Welcome to Bootcamp I of the Data Science and AI Signature Track offered by SKEMA Global Lab in AI. As a part of the Master in Management of the Grande École Program, the signature track leverages new 21st century tools and techniques, such as Data Science and Artificial Intelligence to train students to become tje new generation of leaders, ready to manage 21st-century companies in the 21st-century knowledge economy.

Bootcamp I is the first of the two Bootcamps in order to obtain the Fundamentals in Data Science Certificate issued by the the SKEMA Global Lab in Augmented Intelligence.


Students will become familiar with the Data Science workflows and with the SKEMA Quantum Studio platform.

In this Bootcamp, we will cover topics such as Reproducible Research with RMarkdown, Data Wrangling that includes data importation, cleansing and manipulation. Students will also be exposed to Data Visualization techniques as well as the use of APIs. Modeling techniques will also be covered in this first bootcamp.

Themes covered

  1. Reproducible Research
  2. Data Wrangling
  3. Data Visualization
  4. APIs
  5. Modeling

Practical information

Bootcamp I will take place on the SKEMA campus in Lille from Monday 6 January 2020 9am to Friday 10 January 2020 5pm.

We will be in room C111.

Bring your laptop and your motivation.




View Guidelines for the the Country Analysis Report

Community Values & Honor Code

Community Values

It Is Essential to Foster a Supportive Online Learning Environment.

At SKEMA Global Lab in AI, we believe it is essential for all participants to exemplify and uphold the SKEMA Quantum Studio Community Values in order to foster a supportive online learning environment where individuals can have open discussion, reflect on their thinking, and learn from each other.

The mission of the SKEMA Global Lab in AI is to educate leaders who make a difference in the world. Achieving this mission requires an environment of trust and mutual respect, free expression and inquiry, and a commitment to truth, excellence, and lifelong learning.

Students, program participants, faculty, staff, and alumni accept these principles when they join the SKEMA Quantum Studio community. In doing so, they agree to abide by the following Community Values:

SKEMA Global lab can and should be a living model of these values. To this end, community members have a personal responsibility to integrate these values into every aspect of their experience at SKEMA Global Lab

Through our personal commitment to these values, we can create an environment in which all can achieve their full potential.

SKEMA Global Lab Honor Code

A Commitment of Honor, Honesty, and Stewardship.

The SKEMA Global Lab Honor Code supplements the statement of Community Values and reflects the commitment participants make as members of the learning community to participate in, foster, and uphold the SKEMA Global Lab Learning Model.

By participating in a SKEMA Global Lab course or program, you agree to:

Failure to abide by the SKEMA Global lab Community Values or SKEMA Global Lab Honor Code may result in removal from a SKEMA Global Lab program and/or from the SKEMA Quantum Studio framework.

Curriculum Bootcamp I

Session 1. Presentation SKEMA Global Lab in AI & Data Science and AI Signature Track

Session 2. SKEMA Quantum Studio - The Power of a Platform

Session 3. Reproducible Research

Session 4. Data Management - The Power of Visualization

Session 5. Data Wrangling - Oil of Machine Learning

Session 6. Data Visualization

Session 7. Data Visualization II and Dashboard

Session 8. Dynamic Data - The World of APIs

Session 9. Data Modeling

Session 10. Friday Case

Yan Han, Hannah. 2019. “Mondrianify.” https://github.com/yanhann10/mondrianify.

  1. This final report will be assessed following these guidelines https://warin.ca/sessions/bootcamp/guidelinesCountryReport.html↩︎


For attribution, please cite this work as

Warin (2019, Dec. 7). Thierry Warin: [Course] Bootcamp 1: The fundamentals in R. Retrieved from https://warin.ca/posts/bootcamp1/

BibTeX citation

  author = {Warin, Thierry},
  title = {Thierry Warin: [Course] Bootcamp 1: The fundamentals in R},
  url = {https://warin.ca/posts/bootcamp1/},
  year = {2019}