Computational Methods for Doctoral Research in Management


Lecturer(s) :

Younge Kenneth




The objective of this course is to introduce doctoral students to computational methods for data-driven empirical research in management.


The course complements courses in statistics and econometrics with a programmatic understanding of how to acquire, store, manipulate, measure, plot, analyze, and classify data. The course requires students to program in Python. The basics of Python language will be reviewed during the first session, but students who are unfamiliar with Python should review the Python 3 tutorials at: and complete the 4-hour tutorial by DataCamp at: prior to the start of class. You may also want to review the official Python 3 documentation at:


By the end of the course, students will understand how to work with data in python to conduct doctoral research in economics and management. The ultimate learning objective of the course is to build a toolkit that will elevate the empirical quality of each student's dissertation.




Data Processing, Visualization, Cloud Computing, Data Analysis, Text Analysis, Simulation, Machine Learning. 

Assessment methods

Students will be evaluated based on five, take-home assignments ' each worth 20% of the overall course grade. Each assignment should take 5 to 6 hours to complete. Each assignment is due before the start of the next class; the final assignment is due one week after the final class.


Create a new jupyter notebook for each assignment and commit it to your git repository. Name your jupyter notebooks sequentially as:


a1.ipynb   a2.ipynb   a3.ipynb   a4.ipynb   a5.ipynb




Please contact the TAs of the course for more information or a detailed syllabus:

In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German