Program Year


  • Machine Learning

    Machine Learning

    Ects : 6
  • Industrial Organization

    Industrial Organization

    Ects : 6
  • Computer Science Project

    Computer Science Project

    Ects : 6
  • Digital currencies & Blockchain

    Digital currencies & Blockchain

    Ects : 6
    Compétence à acquérir :
    The objective of this class is to give students a deep theoretical overview of what a blockchain is. Nonetheless we will also use mock-blockchains, write smart contracts and interact with them, through some computer sessions. This will help solidify the knowledge learned and de-mystify the functioning of a blockchain.
    Students will gain a deep knowledge of how a blockchain works internally. They will also be very aware of the different issues and perhaps they will be able to spot new use cases for a blockchain.
    Mode de contrôle des connaissances :
    The evaluation can still change but will likely be composed of class participation (10%), a group presentation (25%) with a final exam and/or a project (65%). Students are encouraged to actively interact during the class.
    Pré-requis recommandés :
    Knowledge in computer science and economics is needed to properly understand what a blockchain is, but we will go through what is needed just to make sure everyone is on the same page. In particular we will go through asymmetric cryptography, distributed networks, consensus, game theory, financial markets and corporate finance. Although students with knowledge in any of those topics will be more confortable, I intend to present them “from scratch”.
    Pré-requis obligatoires :
    The only prerequisite is basic economics (competition, market economy, utility maximization).

    Description du contenu de l'enseignement :
    While this is a fairly recent technology, this class will take students through the fundamentals of blockchains as well as implications regarding financial, economic or social interactions. The class will start by some history needed to understand what lead to the creation of the first blockchain in 2009. We will then review the detailed functioning of a blockchain. We will continue by discussing important current applications in the industry as well as implications for the economic environment. Lastly, we will discuss potential future developments and how blockchains will impact a broad range of industries. Students will also be introduced to recent academic work related to blockchains.
    Students will be asked to pick a blockchain project from a list and present it briefly during the presentation session in front of the class (group presentation). When reaching this presentation session, students will be expected to be able to assess the pros and cons of a given blockchain project, and have a critical opinion on this project.

    Bibliographie, lectures recommandées
    Melanie Swan, Blockchain: Blueprint for a new economy, O’Reilly, 2015
    Andreas Antonopoulos, Mastering Bitcoin, 2nd edition, O’Reilly, 2017
    Primavera De Filippi/ Aaron Wright, Blockchain and the Law: The Rule of Code, Harvard University Press, 2018
  • Ethics, Algorithm and data

    Ethics, Algorithm and data

    Ects : 6


  • Conference: Big Data and regulation

    Conference: Big Data and regulation

    Ects : 3
  • Internship


    Ects : 9
    Compétence à acquérir :
    The students should also complete an end-of-studies internship lasting at least 4 months. The curriculum includes guest lectures by visiting professionals on issues related to Big Data, providing another means of connecting with relevant business circles.
    Mode de contrôle des connaissances :
    The internship will conclude with a report to be reviewed by a committee.

    Description du contenu de l'enseignement :
    Students must intern at a company (semester 4) for at least 4 months.

Mandatory - Specialisation 1: Marketing and Network

  • Database marketing

    Database marketing

    Ects : 6
    Compétence à acquérir :
    Ability to conduct data science on marketing data
    Mode de contrôle des connaissances :
    Students will be evaluated in groups based on datasets handled with Python.

    Description du contenu de l'enseignement :
    This course provides a general introduction to the methods and practice of Machine Learning (ML) in Marketing. Machine learning is a subfield of artificial intelligence. It develops algorithms to learn and understand large amounts of data and to make predictions based on specific questions asked. In this course, ML is applied to issues related to
    Customer Relationship Management. More specifically, we will use ML algorithms for: (1) customer acquisition, (2) customer retention, and (3) customer development in order to optimize the customer's Lifetime Value.

    Course Content
    • Why do we need ML in Marketing?
    • How to do it?
    • Supervised ML Applications
    • Customer acquisition
    • Customer retention
    • Customer profitability
    • Regression and classification methods – e.g. logistic regression, decision trees and their extensions (bagging, boosting methods), Naive Bayes, Support Vector Machine (SVM), Neural networks, Deep learning, Survival Analysis (continuous time, discrete time)
    • Unsupervised ML Applications
    • Recommender systems (content-based, item-based, user-based, …)
    • Customer Segmentation
    • Natural language processing (NLP) applications
    • Brand value analysis
    • Customer satisfaction
    • Customer complaints
    • Text preprocessing, sentiment analysis, topic modeling, text classification based on Deep learning algorithms (e.g. word embeddings, recurrent neural networks, LSTM, convolutional Neural Nets, BERT)

    Bibliographie, lectures recommandées
    · Blattberg, Robert C., Kim, Byung-Do, Neslin, Scott A. (2008). Database Marketing: Analyzing and Managing Customers, Springer
    · V. Kumar, Werner Reinartz (2006). Customer Relationship Management: A Database Approach, Wiley
  • Revenue Management

    Revenue Management

    Ects : 6
  • Economics of the internet

    Economics of the internet

    Ects : 6

Mandatory - Specialisation 2 : Finance

  • Graphical analysis of financial data

    Graphical analysis of financial data

    Ects : 6
  • Robot & FinTech

    Robot & FinTech

    Ects : 6
    Compétence à acquérir :
    The objective of the course is to discuss the impact of the introduction of robo advice on individuals’ investment decisions and the recent developments in household finance (life-cycle portfolio choices and behavioural biases).
    After having attended the classes, the students will:
    - Understand the main determinants of individuals’ portfolio choices and the underlying life-cycle theory
    - Have a good understanding of the services offered by robo advisors and their impact on individuals’ investment behaviour
    Mode de contrôle des connaissances :
    Final examination
    Pré-requis recommandés :
    Standard finance course (portfolio choice)

    Description du contenu de l'enseignement :
    Robo-advisors became increasingly popular as investors are seeking low-cost and automated investment advice. By providing personalized investment portfolios depending on individual’s profile in a flexible way and at a low cost, they start to gradually replace or complement the services traditionally offered by human advisors in the banks. The objective of this course is to discuss the services offered by robo-advisors, their role in the financial industry, but also the issues and challenges they are facing. The course will present the theory of individuals’ optimal life-cycle portfolio choices, and the traditional behavioural biases of individuals making investment decisions. We will see how automated advice can improve the services offered by human advisors by rationalizing investment decisions and customizing the advice to each individual’s needs. Finally, we will discuss their impact on individuals’ behaviour by analysing the results of most recent academic research on the topic.

    Bibliographie, lectures recommandées
    Barber, B. M. and Odean, T. (2013), The behavior of individual investors, in Handbook of the Economics of Finance, Elsevier, 1533-1570
    Beshears, J. and Choi, J.J. and Laibson, D. and Madrian, B.C. (2018), “Behavioral Household Finance”, NBER Working Paper No. w24854.
    Campbell, J. Y. (2006), “Household Finance”, Journal of Finance, 61(4), 1553-1604.
    Cocco, J. F., Gomes, F. J., & Maenhout, P. J. (2005). Consumption and portfolio choice over the life cycle. Review of Financial Studies, 18(2), 491-533.
    D’Acunto, F. Prabhala, N. and Rossi, A. (2019), “The Promises and Pitfalls of Robo-advising” - Review of Financial Studies, 32(5), 1983–2020.
    Gargano, A. and Rossi A.G. (2018). "Does it Pay to Pay Attention?", Review of Financial Studies 31(12), 4595- 4649.
    Guiso, L. and Sodini, P. (2012), Household Finance: An emerging field, Discussion Paper.
    Merton, R. C. (1969), ‘‘Lifetime Portfolio Selection Under Uncertainty: The Continuous-Time Case,’’ Review of Economics and Statistics, 51, 247–257.
  • Algorithmics and high frequency trading

    Algorithmics and high frequency trading

    Ects : 6

Academic Training Year 2020 - 2021 - subject to modification

Teaching Modalities

All courses are taught in English, and the program is completed in two semesters:
The first semester is devoted to fundamental methods and techniques (using econometrics, operating large-scale databases, implementing suitable models, and evaluating parameters). All courses are mandatory, and amount to 30 ECTS credits. Courses and final exams end in December of the academic year.

During the second semester, students choose one of two specializations: Network Economics or Finance. They also attend a seminar on "Data, Firms and Regulation" where private sector’s practitioners and regulators expose the new practices and public policy challenges raised by the digital transformation. The students should also complete an end-of-studies internship lasting at least 4 months. The curriculum includes guest lectures by visiting professionals on issues related to Big Data, providing another means of connecting with relevant business circles.

Internships and Supervised Projects