Institut Polytechnique de Paris
Ecole Polytechnique ENSTA ENSAE Télécom Paris Télécom SudParis

Master Year 2 Data Science

Master Year 2 Data Science
Year

Master Year 2

Program

Data Science

ECTS Credits

60

Language

English and French

Orientation

Research and Industry

Location

Palaiseau Campus

Course duration

12 months, full time

Course start

September

Degree awarded

Master’s degree

WHY ENROLL IN THIS PROGRAM?

Asset n° 1 

Master key tools and skills for data scientists based on an interdisciplinary approach

Asset n°2

Lay the foundations of your future career by pursuing a PhD track in Data Science (link to PhD track Data Science) or following an apprenticeship program

Asset n°3

Open up numerous job opportunities in the context of a global shortage of data scientists and data analysts

Today, major players in the world of business are increasingly aware of the potential of their data and are looking for ways to extract as much useful information as possible. Data scientists are in charge of retrieving, storing, organizing, processing this mass of information to create value. This is a hybrid profile requiring a solid background in mathematics and statistics, mastery of data management and processing tools and infrastructure, as well as curiosity and a thirst to understand.

Exploiting this immense volume of data requires sophisticated mathematical techniques, which 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 and machine learning.

A wide range of mathematical and numerical statistics and learning methods are used from analyzing exploratory data to sophisticated  inference techniques (hierarchical graphical models) and classification or regression (deep learning, support vector machine). In order to be develop on a massive scale, these methods require the mastery of data distribution mechanisms and large-scale calculations. Applied mathematics (functional analysis, numerical analysis, convex and non-convex optimization) also plays  an essential role.

Objectives

This program allows students to:

  • Become the data scientists of tomorrow both in academia and industry - a large proportion of our students choose to pursue doctorate studies
  • Master sophisticated mathematical techniques to extract relevant information: statistical processing of information, analysis of exploratory data, techniques of inference and classification or regression, data distribution mechanisms and very large-scale calculations

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 global job market. Like all fields of breakthrough innovation (e.g. biotechnology and e-medicine), there is a high need for high-level engineers and doctoral candidates.

40 ECTS of courses to validate over the 3 quarters

20 ECTS for internship

Core courses

Data Camp

20h

5 ECTS

English

 

 

Elective courses     

Optimization for Data science       

40 h

5 ECTS

English

Markov Chain Monte Carlo- Theory and practical applications 

20 h

2.5 ECTS

English

Systems for Big Data Analytics 

20 h

2.5 ECTS

English

Convex Analysis and Optimization Theory      

40 h

5 ECTS

English

Machine Learning         

20 h

2.5 ECTS

English

Deep Learning I       

20 h

2.5 ECTS

English

Big Data Framework     

40 h

5 ECTS

English

Graphical Models for large scale content access   

20 h

2.5 ECTS

English

Statistical Learning Theory  

20 h

2.5 ECTS

Français/English

Modèles à chaîne de Markov cachée et méthodes de Monte Carlo séquentielles   

20 h

2.5 ECTS

Français/English

High-dimensional statistics     

20 h

2.5 ECTS

English

Estimation non paramétrique  

20 h

2.5 ECTS

Français/English

Enchères et Matching : apprentissage et approximations         

20 h

2.5 ECTS

Français/English

 

Partially observed Markov chains in signal and image  

20 h

2.5 ECTS

Français/English

Advanced learning for text and graph Data      

20 h

2.5 ECTS

Français/English

Reinforcement learning       

20 h

2.5 ECTS

English

Generalisation properties of algorithms in ML

20 h

2.5 ECTS

Français/English

Bootstrap and resampling methods in machine learning       

20 h

2.5 ECTS

English

Missing Data and causality    

20 h

2.5 ECTS

Français/English

High dimensional matrix estimation     

20 h

2.5 ECTS

English

Recherche opérationnelle et données massives      

20 h

2.5 ECTS

Français/English

Optimisation non différentiable et méthodes proximales

20 h

2.5 ECTS

Français/English

Introduction to Deep Learning with Python part 1

20 h

2.5 ECTS

English

Machine Learning, Business Case

20 h

2.5 ECTS

Français/English

Data Stream Processing

20 h

2.5 ECTS

Français/English

Computer Vision

20 h

2.5 ECTS

English

Natural Langage Processing

20 h

2.5 ECTS

English

Courses

Structured Data: Learning and Prediction      

20 h

2.5 ECTS

Français/English

Deep Learning II            

20 h

2.5 ECTS

English

Tail events analysis: Robustness, outliers and models for extreme  

20 h

2.5 ECTS

English

Stochastic approximation and applications to reinforcement learning

20 h

2.5 ECTS

English

Mixed effects models: methods, algorithms and applications in life sciences

20 h

2.5 ECTS

Français/English

Multi-object estimation and filtering    

20 h

2.5 ECTS

English

Audio and music information retrieval 

20 h

2.5 ECTS

English

Introduction mathématiques au Compressed Sensing  

20 h

2.5 ECTS

Français/English

Online learning and aggregation    

20 h

2.5 ECTS

Français/English

Optimal Transport: Theory, Computations, Statistics, and ML Applications        

20 h

2.5 ECTS

Français/English

Cours Projet Big Data & Assurance        

20 h

2.5 ECTS

Français/English

Infrastructure de données pour le Big Data

20 h

2.5 ECTS

Français/English

Time series for financial Data

20 h

2.5 ECTS

Français/English

Systèmes de recommandation

20 h

2.5 ECTS

Français/English

Introduction to Deep Learning with Python part 2

20 h

2.5 ECTS

English

Natural language processing

20 h

2.5 ECTS

English

Law and ethics of artificial intelligence

20 h

2.5 ECTS

Français/English

 

Internship

Internship of minimum 16 weeks

From April to end of August

20 ECTS

 

 

 

 

 

Admission requirements

Academic prerequisites

Completion of the first year of a Master in mathematics at Institut Polytechnique de Paris or equivalent in France or abroad.

Language prerequisites

  • English
  • French

How to apply

Applications can be submitted exclusively online. You will need to provide the following documents:

  • Transcript
  • Two academic references (added online directly by your referees)
  • CV/resume
  • Statement of purpose

You will receive an answer in your candidate space within 2 months of the closing date for the application session.

Fees and scholarships

  • EU/EEA/Switzerland students: 4243€
  • Non-EU/EEA/Switzerland students: 6243€
  • Engineer students enrolled in one of the five member schools of Institut Polytechnique de Paris (Ecole polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris and Télécom SudParis): 159€
  • Special cases: please refer to the "Cost of studies" section of the FAQs

Find out more about scholarships

Applications and admission dates

Coordinator

Erwan Le Pennec

Program office

Leyla Marzuk

Nicoletta Bourgeois

General enquiries

master-admission@ip-paris.fr

Description

Today, major players in the world of business are increasingly aware of the potential of their data and are looking for ways to extract as much useful information as possible. Data scientists are in charge of retrieving, storing, organizing, processing this mass of information to create value. This is a hybrid profile requiring a solid background in mathematics and statistics, mastery of data management and processing tools and infrastructure, as well as curiosity and a thirst to understand.

Exploiting this immense volume of data requires sophisticated mathematical techniques, which 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 and machine learning.

A wide range of mathematical and numerical statistics and learning methods are used from analyzing exploratory data to sophisticated  inference techniques (hierarchical graphical models) and classification or regression (deep learning, support vector machine). In order to be develop on a massive scale, these methods require the mastery of data distribution mechanisms and large-scale calculations. Applied mathematics (functional analysis, numerical analysis, convex and non-convex optimization) also plays  an essential role.

Objectives

This program allows students to:

  • Become the data scientists of tomorrow both in academia and industry - a large proportion of our students choose to pursue doctorate studies
  • Master sophisticated mathematical techniques to extract relevant information: statistical processing of information, analysis of exploratory data, techniques of inference and classification or regression, data distribution mechanisms and very large-scale calculations

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 global job market. Like all fields of breakthrough innovation (e.g. biotechnology and e-medicine), there is a high need for high-level engineers and doctoral candidates.

40 ECTS of courses to validate over the 3 quarters

20 ECTS for internship

Core courses

Data Camp

20h

5 ECTS

English

 

 

Elective courses     

Optimization for Data science       

40 h

5 ECTS

English

Markov Chain Monte Carlo- Theory and practical applications 

20 h

2.5 ECTS

English

Systems for Big Data Analytics 

20 h

2.5 ECTS

English

Convex Analysis and Optimization Theory      

40 h

5 ECTS

English

Machine Learning         

20 h

2.5 ECTS

English

Deep Learning I       

20 h

2.5 ECTS

English

Big Data Framework     

40 h

5 ECTS

English

Graphical Models for large scale content access   

20 h

2.5 ECTS

English

Statistical Learning Theory  

20 h

2.5 ECTS

Français/English

Modèles à chaîne de Markov cachée et méthodes de Monte Carlo séquentielles   

20 h

2.5 ECTS

Français/English

High-dimensional statistics     

20 h

2.5 ECTS

English

Estimation non paramétrique  

20 h

2.5 ECTS

Français/English

Enchères et Matching : apprentissage et approximations         

20 h

2.5 ECTS

Français/English

 

Partially observed Markov chains in signal and image  

20 h

2.5 ECTS

Français/English

Advanced learning for text and graph Data      

20 h

2.5 ECTS

Français/English

Reinforcement learning       

20 h

2.5 ECTS

English

Generalisation properties of algorithms in ML

20 h

2.5 ECTS

Français/English

Bootstrap and resampling methods in machine learning       

20 h

2.5 ECTS

English

Missing Data and causality    

20 h

2.5 ECTS

Français/English

High dimensional matrix estimation     

20 h

2.5 ECTS

English

Recherche opérationnelle et données massives      

20 h

2.5 ECTS

Français/English

Optimisation non différentiable et méthodes proximales

20 h

2.5 ECTS

Français/English

Introduction to Deep Learning with Python part 1

20 h

2.5 ECTS

English

Machine Learning, Business Case

20 h

2.5 ECTS

Français/English

Data Stream Processing

20 h

2.5 ECTS

Français/English

Computer Vision

20 h

2.5 ECTS

English

Natural Langage Processing

20 h

2.5 ECTS

English

Courses

Structured Data: Learning and Prediction      

20 h

2.5 ECTS

Français/English

Deep Learning II            

20 h

2.5 ECTS

English

Tail events analysis: Robustness, outliers and models for extreme  

20 h

2.5 ECTS

English

Stochastic approximation and applications to reinforcement learning

20 h

2.5 ECTS

English

Mixed effects models: methods, algorithms and applications in life sciences

20 h

2.5 ECTS

Français/English

Multi-object estimation and filtering    

20 h

2.5 ECTS

English

Audio and music information retrieval 

20 h

2.5 ECTS

English

Introduction mathématiques au Compressed Sensing  

20 h

2.5 ECTS

Français/English

Online learning and aggregation    

20 h

2.5 ECTS

Français/English

Optimal Transport: Theory, Computations, Statistics, and ML Applications        

20 h

2.5 ECTS

Français/English

Cours Projet Big Data & Assurance        

20 h

2.5 ECTS

Français/English

Infrastructure de données pour le Big Data

20 h

2.5 ECTS

Français/English

Time series for financial Data

20 h

2.5 ECTS

Français/English

Systèmes de recommandation

20 h

2.5 ECTS

Français/English

Introduction to Deep Learning with Python part 2

20 h

2.5 ECTS

English

Natural language processing

20 h

2.5 ECTS

English

Law and ethics of artificial intelligence

20 h

2.5 ECTS

Français/English

 

Internship

Internship of minimum 16 weeks

From April to end of August

20 ECTS

 

 

 

 

 

Admission requirements

Academic prerequisites

Completion of the first year of a Master in mathematics at Institut Polytechnique de Paris or equivalent in France or abroad.

Language prerequisites

  • English
  • French

How to apply

Applications can be submitted exclusively online. You will need to provide the following documents:

  • Transcript
  • Two academic references (added online directly by your referees)
  • CV/resume
  • Statement of purpose

You will receive an answer in your candidate space within 2 months of the closing date for the application session.

Fees and scholarships

  • EU/EEA/Switzerland students: 4243€
  • Non-EU/EEA/Switzerland students: 6243€
  • Engineer students enrolled in one of the five member schools of Institut Polytechnique de Paris (Ecole polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris and Télécom SudParis): 159€
  • Special cases: please refer to the "Cost of studies" section of the FAQs

Find out more about scholarships

Applications and admission dates

Coordinator

Erwan Le Pennec

Program office

Leyla Marzuk

Nicoletta Bourgeois

General enquiries

master-admission@ip-paris.fr