Digital Economics - 297 - 2nd year of master's degree

Python for Data Science

Langue du cours : Anglais

Machine Learning

ECTS : 9

Enseignant responsable : FABRICE ROSSI (https://www.ceremade.dauphine.fr/en/members/detail-cv/profile/fabrice-rossi.html)

Langue du cours : Anglais

Description du contenu de l'enseignement :

The course gives a thorough presentation of the machine learning field and follows this outline:

1. general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
2. machine learning as automated program building from examples (running example: decision trees)
3. machine learning as optimization:
   1. empirical risk minimization
   2. links with maximum likelihood estimation
   3. surrogate losses and extended machine learning settings
   4. regularisation and kernel methods (support vector machines)
4. reliable estimation of performances:
   1. over fitting
   2. split samples
   3. resampling (leave-one-out, cross-validation and bootstrap)
   4. ROC curve, AUC and other advanced measures
5. combining models:
   1. ensemble techniques
   2. bagging and random forests
   3. boosting
6. unsupervised learning:
   1. clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)
   2. outlier and anomaly detection

Compétences à acquérir :

After attending the course the students will

• have a good understanding of the algorithmic and statistical foundations of the main machine learning techniques
• be able to select machine learning techniques adapted to a particular task (exploratory analysis with clustering methods, predictive analysis, etc.)
• be able to design a model selection procedure adapted to a particular task
• report the results of a machine learning project with valid estimation of the performances of their model

Pré-requis obligatoires

• intermediate level in either Python or R. Students are expected to be able to perform standard data management tasks in Python or R, including, but not limited to:
  • loading a data set from a CSV file
  • recoding and cleaning the data set 
  • implementing a simple data exploration strategy based on pivot table and on graphical representation
• intermediate level in statistics and probability. Students are expected to be familiar with:
  • descriptive statistics
  • conditional probabilities and conditional expectations
  • core results from statistics: bias and variance concepts, strong law of large numbers, central limit theorem, etc. 

Mode de contrôle des connaissances :

• quizzes and tests during the course
• machine learning project

Coefficient : 3
6 (M2 Economie Internationale et Développement)
6 (M2 Diagnostic économique international)

Data Science Project

ECTS : 3

Enseignants : MADALINA OLTEANU, ARTHUR THOMAS
https://dauphine.psl.eu/recherche/cvtheque/olteanu-madalina
https://dauphine.psl.eu/recherche/cvtheque/thomas-arthur

Langue du cours : Anglais

Coefficient : 1

Industrial Organization

ECTS : 6

Enseignants : ANTOINE CHAPSAL, ANNA CRETI
https://dauphine.psl.eu/recherche/cvtheque/creti-anna

Langue du cours : Anglais

Description du contenu de l'enseignement :

Theory and practice of competition in network industries; antitrust issues; theory of network and network effects; two-sided platforms and pricing

Compétences à acquérir :

Understanding of competition and regulation issues in network and digital economics

Pré-requis obligatoires

Advanced Micro

Pré-requis recommandés

Industrial Organization

Mode de contrôle des connaissances :

Written exam

Coefficient : 2

Bibliographie, lectures recommandées :

Belleflamme-Peitz, Industrial Organization

Blockchain economics

ECTS : 6

Enseignant responsable : LOUIS BERTUCCI (https://dauphine.psl.eu/recherche/cvtheque/bertucci-louis)

Langue du cours : Anglais

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 Bitcoin, the first blockchain, in 2009. We will then review the detailed functioning of a blockchain. We will continue by discussing important current developments 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.

Compétences 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.

Pré-requis recommandés

The first prerequisite is coding. Knowledge in Python and/or Javascript will greatly help students perform the homework. Student less familiar with Python are expected to increase their Python skills by the end of the semester.

The second prerequisite is basic economics (competition, market economy, utility maximization).

While knowledge in computer science and economics is needed to properly understand what a blockchain is, 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”.

Mode de contrôle des connaissances :

The evaluation is composed of a group presentation (1/3), homework (1/3) and a final exam (1/3). Class participation can be highly rewarded especially for students who struggle with homework. Students are encouraged to actively interact during the class.

Coefficient : 2

Bibliographie, lectures recommandées :

Melanie Swan, Blockchain: Blueprint for a new economy, O’Reilly, 2015

Andreas Antonopoulos, Mastering Bitcoin, 2nd edition, O’Reilly, 2017

Andreas Antonopoulos / Gavin Wood, Mastering Ethereum, 1st edition, O’Reilly, 2018

Primavera De Filippi/ Aaron Wright, Blockchain and the Law: The Rule of Code, Harvard University Press, 2018

Financial Data & Systemic risk

ECTS : 3

Enseignant responsable : Sylvain BENOIT (https://sites.google.com/site/sylvainbenoit87/)

Langue du cours : Anglais

Description du contenu de l'enseignement :

The course will equip students with the necessary knowledge to be able to undertake econometric analysis of the type commonly associated with modern financial econometrics research. Substantial emphasis will be placed on the development of programming skills in Python (or in MATLAB, especially for financial contagion and multivariate analysis).

Course outline:

1. Data collection (CRSP-Compustat, Yahoo-Finance, ECB data warehouse)
2. Market Risk Measurement (Value-at-Risk, Expected Shortfall) – ARCH/GARCH models – univariate time series
3. Backtesting tests for market-risk measurement (independence test, unconditional coverage test, conditional coverage test, super exception)
4. Systemic Risk and Macroprudential regulation (SIFIs identification, MES, SRISK, ?CoVaR) – multivariate time series
5. Principal Component Analysis (absorption ratio computation)
6. Contagion models (direct and indirect effects decomposition)

Compétences à acquérir :

The course provides a deep knowledge of the advanced time series techniques and their application to systemic risk. A technical presentation of these models will be given, before studying applications of these models to systemic risk. 

Pré-requis recommandés

Time Series Analysis. Python programming.

Mode de contrôle des connaissances :

 Individual homework assignment.

Coefficient : 1

Bibliographie, lectures recommandées :

Benoit, S., Colliard, J.-E., Hurlin, C. and C. Pérignon (2017) Where the Risks Lie: A Survey on Systemic Risk, Review of Finance, 21(1), 109-152.

Benoit, S., Hurlin, C. and C. Pérignon (2019) Pitfalls in Systemic-Risk Scoring, Journal of Financial Intermediation, 38, 19-44.

Campbell, S. D. (2004) A Review of Backtesting and Backtesting Procedures, Working paper, Federal Reserve Board.

Christofferson, P. and Pelletier, D. (2004) Backtesting Value-at-Risk: A Duration-Based Approach, Journal of Financial Econometrics, 2(1), 84-108.

Du, Z. and J. C. Escanciano (2015) Backtesting Expected Shortfall: Accounting for Tail Risk, Management Science.

Diebold, F.X. and K. Yılmaz (2009) Measuring Financial Asset Returns and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(1), 158-171.

Diebold, F.X. and K. Yılmaz (2012) Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers, International Journal of Forecasting, 28(1), 57-66.

Time Series and Anomaly Detection

ECTS : 6

Enseignant responsable : MADALINA OLTEANU (https://dauphine.psl.eu/recherche/cvtheque/olteanu-madalina)

Langue du cours : Anglais

Description du contenu de l'enseignement :

 This lecture is thought as an introduction to the analysis of complex data, and particularly to that
having a temporal component. Methods aimed at exploring and modelling time series, longitudinal data
and graphs with temporal components will be addressed. The issues of detecting patterns, breakpoints, changes of regimes, and
anomalies will be at the core of the different approaches.

The first chapters will be devoted to hidden Markov models. After having briefly recalled some definitions
and properties of Markov processes, we will define hidden Markov processes, illustrate them with several
examples and give some of their properties. Inference techniques using the EM algorithm and Bayesian
approaches will be presented and illustrated in practice. We will particularly focus on some specific
models which are extremely useful for segmenting time series stemming from the economics field, such as
autoregressive Markov switching models.

The second part of the lecture will tackle the issue of change-point detection methods. We will start
by introducing the change-point detection issue. More specifically, we will consider several frameworks and derive inference
procedures for computing and locating change-points : online vs. offline strategies, single vs. multiple
change point detection, known vs. unknown number of change points, parametric vs. non-parametric
approaches.

The third chapter will be aimed at introducing the issue of anomaly detection in the context of temporal
data. After having defined what an anomaly is, we will start by assessing whether and how hidden-Markov
models and change-point analysis may be useful for detecting anomalies. Then, we will compare these
two approaches with other techniques, stemming either from the field of computational statistics, or from
that of machine learning. During this chapter, we will also consider the questions of detecting patterns
and clustering temporal data.

The fourth chapter will address data that can be modelled as a graph or a temporal graph. We will start
by introduce some definitions and summaries for characterising the network (degree distribution, centrality
indices, ...). Afterwards, we will tackle the questions of community detection and graph clustering.
Eventually, we will address the issues of random networks and associated tests for randomness.
Models and methods introduced in this lecture will be practiced using existing implementations in R and
“real-life” datasets.

Compétences à acquérir :

Gain some background and perspective of time series analysis from a data science point of view.

Be able to handle temporal data subject to anomalies and change-points.

Have some basic knowledge about graphs and temporal graphs mining. 

Pré-requis obligatoires

Students are expected to have some notions of probabilities, statistical inference theory and time series analysis (ARIMA models). An intermediate

knowledge of R and/or Python is also desirable.

Mode de contrôle des connaissances :

Data challenge. A project to be done individually or by two, analysing real life data.

Coefficient : 1

NLP for economic decisions

ECTS : 3

Enseignant responsable : Yannick LE PEN (https://dauphine.psl.eu/recherche/cvtheque/le-pen-yannick)

Langue du cours : Anglais

Coefficient : 1

Neural Networks

ECTS : 3

Enseignant responsable : JOSEPH RYNKIEWICZ

Langue du cours : Anglais

Coefficient : 1

Private Cryptocurrencies

ECTS : 3

Enseignant responsable : ROMAIN PLASSARD (https://dauphine.psl.eu/recherche/cvtheque/plassard-romain)

Langue du cours : Anglais

Coefficient : 1

Solidity and smart contract development

ECTS : 3

Langue du cours : Anglais

Description du contenu de l'enseignement :

This course introduces all major uses cases of the blockchain industry from a technical perspective. The course begins with an introduction of Github and Solidity coding fundamentals before diving into smart contract development. Participants will learn the most common ERC standards for tokens and NFTs before building more complex contracts for DAOs. Finally, a deep dive into the EVM and an outlook into the future of Blockchain - L2s.

The course schedule is as follows:

Lecture 1 - Blockchain Basics and Development

Lecture 2 - Solidity Fundamentals

Lecture 3 - Contracts and Complex Data Structures

Lecture 4 - ERC20 Tokens and Tokenomics

Lecture 5 - Intro to DeFi

Lecture 6 - Further DeFi Applications

Lecture 7 - NFTs

Lecture 8 - ReFi and NFT applications (Guest Lecture)

Lecture 9 - SDLC, Security and Testing

Lecture 10 - DAOs and Governance

Lecture 11 - Assembly and Gas Optimization

Lecture 12 - Scaling the future of Ethereum: L2s

Compétences à acquérir :

At the conclusion of this course, participants will gain a solid foundation of Solidity programming and smart contract development, enough to be considered a junior blockchain developer. Participants will also gain an understanding of the open source philosophy and collaboration style.

Pré-requis obligatoires

 Knowledge of at least 1 software programming language.

Pré-requis recommandés

Javascript knowledge and experience in software development, testing and deployment.

Open source collaboration, especially Github.

Some awareness of the Blockchain industry.

Mode de contrôle des connaissances :

The level of mastery will be continuously assessed throughout the course by:

1. A weekly presentation on a topic more in depth than what is presented in the lecture material
2. Weekly homeworks
3. Final smart contract project with oral presentation

Coefficient : 1

Bibliographie, lectures recommandées :

Mastering Ethereum by Andreas Antonopoulos - https://github.com/ethereumbook/ethereumbook

Machine Learning for Economists

ECTS : 3

Enseignant responsable : Mathilde GODARD (https://sites.google.com/site/mathildegodard1/)

Langue du cours : Anglais

Description du contenu de l'enseignement :

Economic science has evolved over several decades toward greater emphasis on empirical work. Ever increasing mass of available data (’big data’) in the past decade is likely to have a further and profound effect on economic research (Einav and Levin, 2014). Beyond economic research, governments and the industry are also increasingly seeking to use ’big data’ to solve a variety of problems, usually making use of the toolbox from machine learning (ML).  

The question we ask in this course is the following : What do we (not) learn from big data and ML as economists? Is ML merely applying standard techniques to novel and large datasets? If ML is a fundamentally new empirical tool, how does it fit with what we know? In particular, how does it fit with our tools for causal inference problems? As empirical economists, how can we use big data and ML? We’ll discuss in detail how ML is useful to collect new data, for prediction in policy, and to provide new tools for estimation and inference.  

Compétences à acquérir :

Course objectives: 

1. Present a way of thinking about ML that gives it its own place in the econometric toolbox.

2 Develop an intuition of the problems to which it can be applied, and its limitations.

3. Think of unstructured data (text, image) as data we can use when economic outcomes are missing.   

3 Specific focus on application of ML to social policies (health/labor/taxation/environment etc.).

Pré-requis recommandés

Machine Learning, Microeconometrics.  

Mode de contrôle des connaissances :

Grading: 

1. In-class pairwise presentation of an academic paper (30% of overall grade).
2. Final exam (in-class written test). 70% of overall grade.  

Coefficient : 2

Bibliographie, lectures recommandées :

• Mullainathan, Sendhil and Jann Spiess (2017). “Machine learning: An applied econometric approach”. In: Journal of Economic Perspective 31.2, pp. 87-106.
• Kleinberg, Jon et al. (2015). “Prediction policy problems”. American Economic Review 105.5, pp. 491-495.  
• Athey, S. (2017): “Beyond prediction: Using big data for policy problems”, Science 355, 483–485.
• Athey, Susan, and Stefan Wager. 2021. “Policy Learning with Observational Data”, Econometrica, 89(1): 133-161.  
• Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. and S. Mullainathan (2018): “Human Decisions and Machine Predictions”, The Quarterly Journal of Economics, Volume 133, Issue 1, Pages 237–293.
• Susan Athey, Guido W. Imbens. 2019. Machine Learning Methods That Economists Should Know About. Annual Review of Economics 11:1, 685-725.
• Athey, Susan, and Guido Imbens. 2016. “Recursive Partitioning for Heterogeneous Causal Effects”. PNAS 113(27): 7353–60.
• Belloni, A., V. Chernozhukov, S. Mullainathan and J. Spiess and C. Hansen.(2014): “High-Dimensional Methods and Inference on Structural and Treatment Effects” Journal of Economic Perspectives, Volume 28, Number 2 – Spring 2014, Pages 29–50

Business Cases

Langue du cours : Français

Description du contenu de l'enseignement :

The lecture is a sequence of use-cases in the industry and in the service business performed by professionals.

Compétences à acquérir :

Knowing some of the most common use cases of data sciences in firms decision making business.

Mode de contrôle des connaissances :

Presentation of a resume of the lectures before a jury.

Coefficient : 1

Internship

ECTS : 12

Langue du cours : Anglais

Description du contenu de l'enseignement :

  Students must intern at a company (semester 4) for at least 4 months. 

Compétences 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.  

Coefficient : 4

Anonymization, privacy

ECTS : 3

Enseignant responsable : Pierre SENELLART

Langue du cours : Français

Coefficient : 1

Computational social choice

ECTS : 3

Enseignant responsable : JEROME LANG (https://www.lamsade.dauphine.fr/~lang/)

Langue du cours : Français

Coefficient : 1

Incremental learning, game theory and applications

ECTS : 3

Enseignant responsable : MOHAMMED RIDA LARAKI (https://dauphine.psl.eu/recherche/cvtheque/laraki-rida)

Langue du cours : Français

Coefficient : 1