Programme du Master in Economics, Business and Data Analytics

Le programme du Master se divise en trois blocs de cours et un mémoire de Master, pour un total de 90 ECTS :

  • Data Analytics (27 ECTS, 3 modules obligatoires)
  • Markets and Decision Making (21 ECTS, 3 modules obligatoires)
  • Applied Topics in Business and Economics (21 ECTS, trois modules à choix parmi six). Vous pouvez choisir de vous spécialiser en prenant soit les trois modules de management, soit les trois modules d’économie, ou ne pas vous spécialiser et choisir librement la combinaison de module qui vous convient.
  • Mémoire de Master (21 ECTS)

Vous trouverez des informations plus détaillées sur chaque module ci-dessous.

La liste des modules et le programme complet figure également aux annexes au règlement d'études.

Module(s) obligatoire(s)

Crédits ECTS9
SemestreSemestre de printemps et d'automne
Public cibleÉtudiant-e-s du semestre 1
Description

This course module discusses several of the most practically relevant econometric and statistical methods for empirical research and data analytics (e.g. in economics and social sciences) along with their underlying assumptions and properties. It also presents applications of these methods in real-world data using the statistical software “R (studio)”. The course module consists of a lecture and 4 PC lab sessions (quizzes). This course consists of asynchronous independent study periods, 5 online meetings, and 4 PC labs and quizzes. The topics covered in the lecture include:

 

1. The difference between causation (e.g. education has a causal effect on wage) and correlation (subjects with higher education have higher wages, but this may be driven by other factors than education as for instance ability); the intuition of experiments for assessing causation.

 

2. Linear regression (OLS - ordinary least squares) to assess the association of one or several variables (e.g. education, age,...) with an outcome of interest (e.g. wage).

 

3. Quantile regression to conduct empirical analyses at particular outcome ranks (e.g. for the median earner in the wage distribution).

 

4. Nonlinear regression (probit regression for binary outcomes like working vs. not working, tobit regression for censored outcomes).

 

5. Flexible methods for causal analysis (such as for evaluating the effect of training on earnings): matching, inverse probability weighting, doubly robust estimation.

 

6. Instrumental variable regression to assess the effect of endogenous variables (that are not random).

 

7. Regression discontinuity designs to assess the effect of a variable (e.g. health treatment) which is assigned based on a running variable (e.g. health index).

 

8. Panel data regression and “Differences-in-Differences” estimation when subjects are observed at several points in time.

 

9. Synthetic control methods for evaluating the effect of an intervention (e.g. of a health policy) on a single unit (e.g. a single region introducing the health policy).

 

10. Bootstrapping (resampling from the original data, e.g. for variance estimation).

 

11. Time series regression, e.g. for modeling stock prices or GDP growth over time.

Lectures recommandées

Mandatory literature

- Martin Huber: Causal Analysis. Impact Evaluation and Causal Machine Learning with Applications in R. The MIT Press (2023).

- Jeffrey M. Wooldridge: Econometric Analysis of Cross Section and Panal Data. Second Edition. The MIT Press (2010).

 

Excerpts of both texts are available in electronic from (PDF) on the Moodle platform of this module.

 

(Spring Semester 2025)

Chargé-e de cours

Prof. Dr Martin Huber

Assistant-e(-s)

MSc Emma Bacci
MSc Emma Bacci

Crédits ECTS9
SemestreSemestre de printemps et d'automne
Public cibleÉtudiant-e-s dès le semestre 2
Description

This course provides an exploration of key issues and concepts in machine learning, with an emphasis on practical applications in business and management. Students will understand the difference between predicting outcomes and identifying cause-and-effect relationships, study regularised regression techniques such as Lasso and Elastic Net, explore tree-based methods such as Random Forest, and learn about resampling methods such as bagging and cross-validation. The course also covers deep learning and unsupervised machine learning techniques. In addition, students will be introduced to some advanced topics such as causal machine learning and LLMs. Using a hands-on approach and the industry-standard software R, the course facilitates the practical application of theoretical concepts and ensures that students gain practical skills in implementing machine learning models.

 

Agenda:

• Block 1: Introduction

• Block 2: Regularised Regresssion

• Block 3: Trees and Deep Learning

• Block 4: Unsupervised ML

• Block 5: Advanced ML Topics

Lectures recommandées

Teaching videos and accompanying slides, exercises, codes and research papers will be posted on Moodle.

 

Main Textbook: “Introduction to Statistical Learning with Applications in R”, James Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, download on Moodle.

 

(Spring Semester 2025)

Professeur-e

Prof. Anthony Strittmatter
Prof. Anthony Strittmatter

Assistant-e(-s)

MSc Vitor Krasniqi
MSc Vitor Krasniqi

Crédits ECTS9
SemestreSemestre de printemps
Public cibleÉtudiant-e-s dès le semestre 4
Description

This module addresses practical issues of data analytics, e.g., data processing and data visualisation, and discusses empirical examples (or use cases) in a seminar format. To this end, various research projects might be assigned to different groups of students and the group work is to be presented in the course. The learning goal is that students get acquainted with practical issues of data analytics (such as data cleaning, plausibility checks, etc.) in a hands-on manner and learn to realise their own empirical projects by working through all steps from data acquisition and processing to the quantitative analysis and the interpretation of the results, using R, Python, or other appropriate statistical software.

Professeur-e

Prof. Anthony Strittmatter
Prof. Anthony Strittmatter

Crédits ECTS7
SemestreSemestre d'automne
Public cibleÉtudiant-e-s dès le semestre 1
Description

A major part of economic activity takes place within or between organisations. In particular, many complex transac-tions can only be carried out with the involvement of organisations. It is thus crucial for economists and managers to understand the economic purpose of organisations, how organisations function, and how they should be de-signed.

 

The module consists of two parts. The first sheds light on the fundamental questions of why firms exist and what their boundaries should be. We cover classic and more recent theories of the firm to understand firm scope and to analyse decisions of vertical integration and outsourcing. The second part investigates the internal organisation of firms and how they should be designed to reach their goals. We rely on theories from economics, management, and related fields to analyse how explicit and implicit incentives affect performance and how organisations can design their incentive systems to motivate employees. Across all topics, we rely on empirical evidence in the form of field data, experiments, and case studies to critically evaluate and, if possible, further develop the theories.

 

Key themes and concepts:

 

Part I – Foundations and Boundaries of Organisations:

• Incomplete contracts

• Relationship-specific investments and hold-up

• Transaction Cost Economics

• Property Rights Approach

• Contracts as Reference Points

 

Part II – Motivation Problem and the Internal Organisation of Firms:

• Principal-Agent model

• Performance pay

• Multi-tasking

• Ratchet effect

• Tournament theory

• Intrinsic motivation and social motivators

 

(Autumn Semester 2024)

Professeur-e

Prof. Dr Manuel Grieder
Prof. Dr Manuel Grieder

Assistant-e(-s)

Dr Krishna Srinivasan
Dr Krishna Srinivasan

Crédits ECTS7
SemestreSemestre de printemps
Public cibleÉtudiant-e-s dès le semestre 2
Description

This module introduces students to economic approaches for understanding, analysing, and evaluating human decision making. Session I starts a selected collection of common bias and heuristics that are often observed in human decision making. Session II introduces the economic foundations of decision making such as basic concepts of preference, axioms, states, acts, and outcomes. We will use decision tree to describe simple decisions under uncertainty, and discuss several systematic, empirical deviations from the expected utility theory (EUT). Session III deals with decision making for future selves. Session IV investigates decision making with strategic interactions. We will introduce game theory and a variety of typical strategic constellations as well as several useful solution concepts.

Finally, Session V presents organisational decision making and a touch of policy implications from natural experiments.

Lectures recommandées

Teaching videos and accompanying research papers and book chapters will be posted on Moodle before each session.

The case studies will be made available on Moodle at the beginning of the semester.

 

(Spring Semester 2025)

Chargé-e de cours

Dr Lingqing Jiang

Assistant-e(-s)

MSc Laura Mangold

Crédits ECTS7
SemestreSemestre d'automne
Public cibleÉtudiant-e-s dès le semestre 3
Description

This comprehensive module is designed to equip aspiring entrepreneurs and innovators with the essential knowledge and skills needed to transform ideas into successful ventures. It combines academic discussion with hands on experience. Throughout this course, you will explore key concepts such as identifying business opportunities, developing compelling value propositions, creating effective business models, and defining a funding strategy to scale your startup. By the end of the module, you will have a solid foundation in innovation and entrepreneurship, ready to turn your visionary ideas into reality.

 

The module covers five main themes:

1. Introduction to Innovation and Entrepreneurship

o Key Terminology: Understanding essential terms such as startup, business model, innovation, and market analysis.

o Importance of Innovation: Exploring how innovation drives business success and competitive advantage in various industries.

o Entrepreneurial Process: Overview of the basic steps involved in launching a startup, including idea generation, market research, business model development, securing funding, and scaling.

 

2. Identifying Opportunities

o Methods for Identification: Analytical methods like market research, SWOT analysis, and trend analysis to identify potential business opportunities. Creativity methods like Scamper.

o Types of Opportunities: Differentiating between market-driven, technology-driven, and problemsolving opportunities.

o Viability Analysis: Conducting analyses on case studies to evaluate the feasibility of different business ideas.

 

3. Developing a Value Proposition

o Value Proposition Concept: Understanding what a value proposition is and its critical role in business strategy.

o Components of a Value Proposition: Identifying key elements such as customer segments, customer jobs, pains, gains, products & services, pain relievers, and gain creators.

o Creating a Value Proposition: Using the Value Proposition Canvas to develop a clear and compelling value proposition for a business idea.

 

4. Creating a Business Model

o Business Model Components: Learning about the nine components of the Business Model Canvas: Customer Segments, Value Propositions, Channels, Customer Relationships, Revenue Streams, Key Resources, Key Activities, Key Partnerships, and Cost Structure.

o Building a Business Model: Constructing a comprehensive business model using the Business Model Canvas.

o Evaluating Business Models: Critiquing and providing feedback on business models to improve their effectiveness and feasibility.

 

5. Funding and Scaling Up

o Sources of Funding: Exploring different funding options available to entrepreneurs, including venture capital, angel investors, crowdfunding, and bootstrapping.

o Developing a Funding Strategy: Creating a funding strategy that outlines potential investors, funding stages, and capital needs.

o Pitch Deck Creation: Designing a professional pitch deck to present a business idea to potential investors, including key elements such as problem statement, solution, market opportunity, business model, traction, financial projections, and funding request

Lectures recommandées

Articles, videos, cases, and exercises will be posted on Moodle.

No specific book

 

Autumn Semester 2024

Chargé-e de cours

Prof. Dr Emmanuelle Fauchart

Modules à choix - Domaine d'application Management

Crédits ECTS7
SemestreSemestre de printemps
Public cibleÉtudiant-e-s dès le semestre 2
Description

This module provides master students with a comprehensive introduction to marketing and consumer behavior. The conceptual foundations of marketing and consumer behavior form the core of the course. The course is structured around four different perspectives on marketing.

 

• Theoretical perspective

• Information-related perspective

• Strategic perspective

• Instrumental perspective (marketing mix)

 

 

Within this structure, current developments in marketing and consumer behavior are discussed (e.g., AI in marketing, current research findings), and different tools to measure and analyze consumer behavior are introduced in R exercises. It is demonstrated how you can draw sound conclusions from such analyses and translate these conclusions into a coherent marketing strategy.

Lectures recommandées

Resources will be posted on Moodle:

• Teaching videos

• Slides

• Research papers

• Exercises / case studies

• R scripts

• Supplementary materials (links, books, etc.)

 

(Spring Semester 2025)

Chargé-e de cours

Prof. Dr Reto Hofstetter

Assistant-e(-s)

MSc Lucas Nann

Crédits ECTS7
SemestreSemestre d'automne
Public cibleÉtudiant-e-s dès le semestre 3
Description

This class will be building on major leadership and business ethics theories with the aim to offer concrete recom-mendations for individuals who want to become more leader-like, effective, and ethical in a formal leadership role. This module is made of two different parts, which are inherently linked with one another.

 

The first part will investigate leadership theories. It will focus on the mindful engagement model as a systematic model to approach one’s development as a leader, review the large literature on personality traits to better under-stand the origin of one’s own behaviours, and discuss the full range model of leadership with its four styles: Trans-actional, Instrumental, Transformational, and Charismatic. We will also discuss how the effectiveness of leadership styles depends on the specific context.

 

In the second part, we will start by discussing the relevance of business ethics for today’s managers. We will start by reviewing the major philosophical approaches that inform morality and underpin major ethical decisions. We will then apply these principles to different examples, helping students distinguish among the different morality ap-proaches. Finally, we will discuss contemporary cases and important ethical questions for managers.

 

Key concepts seen in this class include (among others):

Part 1:

• Leadership styles and behaviors

• Traits

• Charismatic leadership

• Contextual leadership

• Situational awareness

Part 2:

• Ethics

• Morality

• Utilitarianism

• Deontology

• Value-based leadership

Lectures recommandées

All materials posted on Moodle will be relevant for the final exam (unless explicitly stated). There is no compulsory book reading but students will be asked to read the following scientific articles throughout the semester:

• Ashford, S. J., & DeRue, D. S. (2012). Developing as a leader: The power of mindful engagement. Organi-zational Dynamics, 41(2), 146-154.

• Bastardoz, N. (2020). Signaling charisma. In Zuquete, P., The Routledge International Handbook of Charis-ma, 313-323.

• Lemoine, G. J., Hartnell, C. A., & Leroy, H. (2019). Taking stock of moral approaches to leadership: An in-tegrative review of ethical, authentic, and servant leadership. Academy of Management Annals, 13(1), 148-187.

• Vroom, V. H., & Jago, A. G. (2007). The role of the situation in leadership. American Psychologist, 62(1), 17-24.

 

Further readings for interested students will be provided but will not be exam material.

 

(Autumn Semester 2024)

 

Chargé-e de cours

Dr Nicolas Bastardoz

Assistant-e(-s)

MSc Antoine Panchaud
MSc David De Vitis

Crédits ECTS7
SemestreSemestre de printemps
Public cibleÉtudiant-e-s dès le semestre 4
Description

The course Competitive and Corporate Strategy introduces students to the concept of competitive analysis and its implications for strategic decision making in firms. To enable students to analyse and establish the position of a firm within an industry, it covers essential topics and theories, such as the creation of competitive advantage, horizontal integration, and the resource-based view of the firm. After completing this course, students can identify, analyse, and synthesise data and information that supports company decision-making to optimise a firm’s strategic positioning.

Chargé-e de cours

Prof. Dr Johannes Luger

Assistant-e(-s)

MSc Johanna Rupp

Modules à choix - Domaine d'application Economie

Crédits ECTS7
SemestreSemestre d'automne
Public cibleÉtudiant-e-s dès le semestre 3
Description

We live in an increasingly digital world and economy. Information technologies have already transformed the way business is done and will continue to do so. This course covers basic theory in digital economics, for instance, network effects, value creation models, digital business models and market modelling.

Chargé-e de cours

Dr Christian Peukert

Assistant-e(-s)

Jérémie Haese

Crédits ECTS7
SemestreSemestre de printemps
Public cibleÉtudiant-e-s dès le semestre 4
Description

The course introduces the most prominent theories and proposals related to economic growth and evaluates them critically from a sustainable development perspective. We analyse and model the relationships between economic, demographic, and environmental variables, and try to understand their implications for growth rates, inclusive growth, international development, and inequality.

Crédits ECTS7
SemestreSemestre d'automne
Public cibleÉtudiant-e-s dès le semestre 5
Description

This course focuses on the role of government policy in a market economy and on the challenges that arise in the context of the regulation of externalities and generating government revenue. It covers the theoretical and empirical evaluation of public policy as applied to a diverse set of important economic problems such as, for instance, firm concentration and market power, climate change and scarcity of natural resources, or inequality and redistribution.

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