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Business Intelligence and Analytics

  • Teacher(s):   M.Vlachos   S.Orso  
  • Course given in: French
  • ECTS Credits: 4.5 credits
  • Schedule: Spring Semester 2021-2022, 2.0h. course + 2.0h exercices (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Bachelor of Science (BSc) in Economics

    Bachelor (BSc) in Economic Sciences

    Bachelor of Science (BSc) in Management
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Enterprises collect large amounts of data about their clients: historical purchases, responses to marketing events, web search logs, etc. In today’s data-driven economy, data can assist enterprises to better understand their customers, and to take more informed decisions about the business.

The objective of the course is to introduce concepts in Business Intelligence and Data Analytics. During the course we will review several methods to extract and manipulate data, visualize data and make predictive decisions from data. We will also highlight potential pitfalls. Students will get hands-on experience using Python code.

  • Group A: English
  • Group B: French


Students completing the course will understand concepts in:

  • Data Cleaning
  • Exploratory Data Analysis
  • Data visualization
  • Concepts from data mining and machine learning
  • Regression
  • Clustering
  • Association rules
  • Classification


The lecture material will consist of slides given by the instructor.

Recommended reading (but not required)

  • “Business Analytics: The Science of Data-Driven Decision Making”, U. Dinesh Kumar (Publisher: Wiley)


  • You should have taken the course "Modèles Informatiques".
  • Knowledge of programming in Python.

Term project

- Type of project : -
- Maximum number of projects admitted for this course : -
- Deadline for applying to course professor for project : -
- Deadline for submitting finished project : -
- Method of evaluation (including resit options) : -
- Other information : -


First attempt

Without exam (cf. terms)  
  • Mid-term exam (Moodle): 30min - 45min
  • Final exam (Moodle): ~1 hour. In the last week of class. There are absolutely no make-ups for the final exam.
  • Evaluation: If the grade of the mid-term exam is higher than the grade of the final exam, it is taken into account as follows: the final grade is given by 70% of the grade of the final exam and 30% of the grade of the mid-term exam. If the grade of the mid-term exam is lower than the grade of the final exam, it is not taken into account, i.e., the final grade is the grade of the final exam. The same holds in the case of absence at the mid-term exam.
  • Personal Assignments (Moodle). Maximum 5% bonus points.


Written 1h30 hours
Allowed with restrictions
Allowed with restrictions

Moodle Quiz on all class contents (100% of grade).

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