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Datascience for Marketing

  • Enseignant(s):   T.Schlager  
  • Titre en français: Science de données en marketing
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre d'automne 2022-2023, 4.0h. de cours (moyenne hebdomadaire)
  •  séances
  • Formations concernées:
    Maîtrise universitaire ès Sciences en management, Orientation stratégie, organisation et leadership

    Maîtrise universitaire ès Sciences en management, Orientation comportement, économie et évolution

    Maîtrise universitaire ès Sciences en management, Orientation marketing

    Maîtrise universitaire ès Sciences en management, Orientation business analytics
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The objective of this course is to develop an initial understanding of how data scientific methods can not only aid companies on marketing-related challenges, but can be essential to dealing with those challenges. This goal thus can be divided in following objectives:

  1. Transform real-world marketing chellenges into data science problems.
  2. Get hands-on experience in the practice of data science for marketing topics and be able to decide when to use which data science method.
  3. Communicate statistical results to technical and non-technical audiences by using compelling figures and summaries of data.
  4. Apply the learned methods in a Team Project
  5. The final objective is to get students on an advanced level of command of the statistical package R.


Data Science deals with how enterprises, such as businesses, non-profits, and governments can use data to make more informed decisions. Business analytics is applied in many fields, one of which is marketing.

The ability to use data effectively and to make more infomed management decisions under uncertainty has been a critical strategic advantage for companies as diverse as the CERN in Geneva, Porsche, Amazon, Netflix, Disney, or Rolls Royce. With the increasing availability of large amounts of data ' i.e., 'Big Data' Data Science is becoming a critical capability for enterprises of all types and sizes.

In this course, you will first learn to identify, evaluate, and seize Data Science opportunities for marketing problems. We then continue by walking over the conceptual foundations of Data Science and discuss concrete cases of Data Science approaches in the marketing domain. Then, you will learn several methods for supervised and unsupervised learning. Finally, you will learn how to communicate statistical results effectively, or, how to build up a data heavy presentation.

To facilitate a deeper understanding of the fundamental principles, applications, and technical details of Data Science methods, the course will use following methods:

  • Lectures
  • Discussions
  • Invited speakers
  • Real-world applications and exercises
  • Team Project

Sessions will include the following topics:

  • Basics
    • Introduction to the course
    • Introduction to the Data Science process
    • Conceptual basics
  • Design phase
    • Introduction to R
    • Data collection
    • Data cleaning
  • Analysis
    • Unsupervised learning
      • Principal components analysis
      • Cluster analysis (k-means vs. hierarchical clustering)
    • Supervised learning methods
      • Advanced regression models,
      • Tree-based models
    • Model evaluation
  • Reporting phase
    • Visualization
    • Storytelling
    • Summary

Important notes

  • One part will consist of intensive theory sessions (partly pre-recorded lectures)
  • Another part will be focused on discussions concerning the Team project.
  • Other parts use hands-on applications and / or discussion sessions.
  • The course will include sessions with the statistical package R.


This course does not have a required textbook. All required readings, except those related to the Team Project, will be provided via Moodle. Purchase information for required cases will be provided in class.


Write a 1-page statement on why you are excited about this course, including your previous (working) experience in this domain (deadline: 13. 9. no exceptions are made concerning the deadline!).

Due to changes in the sanitary situation related to COVID-19, the study plans may be adapted during the semester.

This is a course with a heavy focus on marketing applications and is focused on students in the marketing orientation.


1ère tentative

Sans examen (cf. modalités)  

Team Project (70%), individual analytics tasks (30%).

Each group of 5 students will present the project at the end of the semester


Sans examen (cf. modalités)  

Team Project (70%), individual analytics tasks (30%).

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