Master 2

Data Science


Overview


Nowadays, the major players in the economic world are becoming increasingly aware of the potential of their data and are looking for ways to exploit and extract as much useful information as possible from them. To help them in this task, datascientists (literally data scientists) are the people in charge of retrieving, storing, organizing, processing this mass of information in order to extract value.
The datascientist is a new kind of benefit, resulting from the convergence of statistics and IT. Giving a precise definition of what the word datascientist covers remains a challenge. What certainly characterizes him best is the variety of skills he must master. This is a hybrid profile, which must have a solid background in mathematics and statistics, but also master the IT tools or infrastructure necessary for data management and processing. They must have the curiosity and thirst to understand the sector in which they work. The objective of this master’s degree is to prepare students to become the datascientists of tomorrow, both in the academic and industrial world. A large proportion of our students choose to pursue doctorate studies.

In addition to the regular course offering, this M2 also proposes a PhD track and an apprenticeship program.

Language of instruction: English and French
ECTS: 60 (lectures 8×5 ECTS, traineeship 20 ECTS)
Oriented: Industry and research
Duration: 1 year
Courses Location: IP Paris


Educational objectives


Big data marks the beginning of a major transformation, which will profoundly affect all sectors (from e-commerce to scientific research, finance and health). The exploitation of these immense masses of data requires sophisticated mathematical techniques to extract relevant information. All these methods form the basis of « data science ». This transition from data to knowledge brings many challenges that require an interdisciplinary approach. Data science » relies heavily on the statistical processing of information: mathematical statistics, numerical statistics, statistical learning or machine learning. From the analysis of exploratory data to the most sophisticated techniques of inference (hierarchical graphical models) and classification or regression (deep learning, support vector machine), a wide range of mathematical and numerical statistics and learning methods are used. These methods, in order to be developed on a mass data scale, require the mastery of data distribution mechanisms and very large-scale calculations. Applied mathematics (functional analysis, numerical analysis, convex and non-convex optimization) also has an essential role to play. From an application point of view, « data science » has a strong impact on many sectors. There is currently a large worldwide shortage of « Data Scientists » and « Data Analysts ». Students from data science and « Big Data » courses are therefore eagerly awaited on the job market. This labour market is global and concerns both developed and emerging economies. Like all fields of breakthrough innovation (biotechnology, e-medicine), the need for very high-level engineers and doctoral candidates is also important.


Program structure


Courses take place on 3 periods alternating with internship in academy or industry. The last fourth period corresponds to the final internship period of 14 weeks minimum.

• Optimization for Data Science
• Bayesian Learning for partially observed dynamical systems
• Introduction to Bayesian learning
• Statistical Learning Theory
• Convex Analysis and Optimization Theory
• Machine Learning
• Visualization and Visual Analytics for Data Science
• Introduction to Graphical Models
• Deep Learning I
• Big Data Frameworks
• Statistique en grande dimension
• Apprentissage et optimisation Séquentiels
• Modèles à chaîne de Markov cachée et méthodes de Monte Carlo séquentielles
• Estimation non paramétrique
• Data Camp

• Optimization for Data Science
• Apprentissage en ligne et Agrégation
• Statistique en grande dimension
• Apprentissage et optimisation Séquentiels Modèles à chaîne de Markov cachée et méthodes de Monte Carlo séquentielles
• Estimation non paramétrique
• Bootstrap and resampling methods in machine learning
• High dimensional matrix estimation
• Partially observed Markov chains in signal and image
• Convex Analysis and Optimization Theory
• Reinforcement learning
• Graphical models for large scale content access
• Theoretical guidelines for high-dimensional data analysis
• Generalisation properties of algorithms in ML
• Optimisation non différentiable et méthodes proximales

• Deep learning I
• Optimisation sous-différentiable et méthodes proximales
• Missing Data and causality
• Audio and music information retrieval
• Tail events analysis: Robustness, outliers and models for extreme values
• Structured Data : learning and prediction
• Recherche opérationnelle et données massives
• Systems for Big Data Analytics
• Multi-object estimation and filtering
• Modèles à effets mixtes
• Stochastic approximation and reinforcement learning
• Projet Big Data & Assurance
• Infrastructure de données
• Kernel Techniques with Information Theoretical Applications ?

Final internship in academy or in a company, minimum duration 14 weeks starting in April.

Phd track in Data Science and Artificial Intelligence: Additional activities including research seminar, research mentoring by Professors of IP Paris, a summer school to prepare students to carry out cutting edge research in Data Science and Artificial Intelligence.

Apprenticeship program: Theoretical courses at IP Paris combined with internship periods in companies to better prepare students to a successful carreer in business and industry.

Courses offering:
List of courses


IP Paris labs involved


LTCI: Information Processing and Communications Laboratory (Télécom Paris),
SAMOVAR  (Télécom SudParis),
CMAP: Applied Mathematics Center (Ecole Polytechnique),
CREST: Center for Research in Economics and Statistics (ENSAE Paris),
UMA: Applied Mathematics UER (ENSTA Paris)


Admissions


Application guidelines for a master’s program at IP Paris

Academic prerequisites

  • Completion of the 1st year of a master program (Master 1)

Application timeline

Deadlines for the Master application sessions are as follows:
– First session: February 28, 2020
– Second session: April 30, 2020
– Third Session (optional): June 30, 2020 (only if there are availabilities remaining after the 2 first sessions)
Applications not finalized for a session will automatically be carried over to the next session.

You shall receive an answer 2 months after the application deadline of the session.


Tuition fees


International Master: EU students: 4250 euros / Non-EU students: 6250 euros


Contact


Le Pennec Erwan

Karim Lounici

Pascal Bianchi

Guillaume Lecue
Email