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 or Industry

Location

Palaiseau Campus

Course duration

12 months, full time or apprenticeship

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 or following an apprenticeship program

Asset n°3

Open up numerous job opportunities as data scientists, data analysts, or in academia

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.

The objective of the Master in Datascience is to train experts in this field. At the end of the training, the students have acquired skills in mathematics of statistical learning, in deep learning, reinforcement learning, optimization, and big data infrastructures among others. In particular, these skills are developed through practical projects and data science competitions.  At the end of the year, both the results at the chosen courses and the professional project are evaluated to validate the Master. 

The master can be combined with a Phd track or an apprenticeship program. It is indeed possible to be a student of the Master in Datascience and be an apprentice aside in a company, working in the datascience field (for instance as a data scientist). In this case, during the year the student can work in the company from monday to wednesday, while thursday and friday are dedicated to attend the Master's courses. 

Objectives

This program allows students to:

  • become experts on statistical learning and artificial intelligence
  • gain a comprehensive training in the various disciplines constituting data science, with a strong emphasis on the mathematics and statistics methodology
  • master sophisticated techniques both theoretically and in practice

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.

On average, almost 25% of the students pursue with a PhD, while the others pursue in the industry.

40 ECTS of courses to validate over the 3 quarters

20 ECTS for internship

Students of the Master can choose various courses, from very theoretical and mathematically involved courses, to more practical ones. In broad terms, the courses cover*:

  • Convex optimization 
  • Reinforcement learning 
  • Graphical Models 
  • Deep Learning
  • Theory of Deep Learning 
  • Machine Learning Theory 
  • Machine Learning for Audio, Text, Graph, Dynamical Data
  • Markov Chain Monte Carlo methods 

The courses also cover**:

  1. Large scale machine learning/ Big Data 
  2. (Advanced) Optimization
  3. Machine Learning & advanced methods
  4. Business or ethical aspects of ML 

*quite similarly to the MVA Master

** more specific to the Master Data Science - examples in (3) that are not covered by the MVA Master : Time Series, Causal Inference, Tail Event Analysis...

Data-based generative models

20h

3 ECTS 

English

Large Scale Machine Learning/Big Data

Systems for Big Data Analytics

20h

3 ECTS 

English

Project + oral presentation

Big Data Framework

40h

5 ECTS 

English

Optimization

Convex Analysis and Optimization Theory

40h

5 ECTS 

English

Final exam

Optimization for Data Science

40h

5 ECTS

English

Machine Learning

Practical Introduction to Machine Learning

20h

3 ECTS 

English

Practical session reports

An Introduction to Machine Learning Theory

20h

3 ECTS 

English

Statistical Learning Theory 

20h

3 ECTS 

English

Final exam

High-Dimensional Statistics     

20h

3 ECTS 

English

Final exam

Non Parametric Estimation and Testing

20h

3 ECTS 

English

Projects

Probabilistic Graphical Models - Applications to Information Access

20h

3 ECTS 

English

Final exam + homeworks

Markov Chain Monte Carlo- Theory and practical applications

20h

3 ECTS 

English

Quiz + project

Hidden Markov models and Sequential Monte Carlo methods

20h

3 ECTS 

English

Projects

Natural Language Processing and Sentiment Analysis

20h

3 ECTS 

English

Lab report / Article study

Deep Learning I

20h

3 ECTS 

English

Final exam

Introduction to reinforcement learning

20h

3 ECTS 

English

Project/article study


Partially Observed Markov Chains in Signal and Image

20h

3 ECTS 

French

Final exam


Advanced AI methods for Graphs and NLP (ALTEGRAD)

28h

5 ECTS 

English

Data challenge on kaggle/6 weeks project + oral presentation

Generalisation properties of algorithms in ML

20h

3 ECTS 

English

Quizzes + project/article

High Dimensional Matrix Estimation

20h

3 ECTS 

English

Final exam/article

Introduction to computer vision

20h

3 ECTS 

English

Business and ethical aspects of ML

Law and ethics of artificial intelligence

20h

3 ECTS 

English

Machine learning: business cases

20h

3 ECTS 

English

Project

Large scale Machine Learning/Big Data

Data Stream Processing  

20 h00

2.5 ECTS 

English

Operations Research and Big Data  

20 h00

3 ECTS 

French

 

Cloud Data Infrastructure

20 h00

3 ECTS 

English

 

Data Stream Processing

20h00

3 ECTS 

English

Practical assignments + article presentation

 

Optimization

Optimization for Data Science

40 h00

5 ECTS 

English

Non Differentiable Optimization and Proximal Methods

20 h00

3 ECTS 

English

Machine Learning

Causal Inference

20 h00

3 ECTS 

English

Final exam + project

Online Learning and Aggregation

20 h00

3 ECTS 

English

Final exam

Auction Theory & in practice

20 h00

3 ECTS 

English

Deep Learning Advanced

20 h00

3 ECTS 

English

Introduction to Time Series

20 h00

3 ECTS 

English

A Mathematical Introduction to Compressed Sensing

20 h00

3 ECTS 

English

Deep Learning II

20 h00

3 ECTS 

English

Project

Audio and Music Information Retrieval

20h00

3 ECTS

English

Final exam

Topics in stochastic filtering, Information and Control

20h00

3 ECTS

English

Practical and theoretical assignments

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

20h00

3 ECTS

English

Final exam

NLP

20h00

3 ECTS

English

Quizz

Applied Deep Learning with Python

40 h00

5 ECTS 

English

Final exam + optional project

Big data & insurance project

20h00

3 ECTS

English

Structured Data: Learning and Prediction

20h00

3 ECTS

English

Tail events analysis: Robustness, outliers and models for extreme

20h00

3 ECTS

English

Stochastic approximation and reinforcement leaning

20h00

3 ECTS

English

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

20h00

3 ECTS

English

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" of the FAQs

Find out more about scholarships

Applications and admission dates

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.

The objective of the Master in Datascience is to train experts in this field. At the end of the training, the students have acquired skills in mathematics of statistical learning, in deep learning, reinforcement learning, optimization, and big data infrastructures among others. In particular, these skills are developed through practical projects and data science competitions.  At the end of the year, both the results at the chosen courses and the professional project are evaluated to validate the Master. 

The master can be combined with a Phd track or an apprenticeship program. It is indeed possible to be a student of the Master in Datascience and be an apprentice aside in a company, working in the datascience field (for instance as a data scientist). In this case, during the year the student can work in the company from monday to wednesday, while thursday and friday are dedicated to attend the Master's courses. 

Objectives

This program allows students to:

  • become experts on statistical learning and artificial intelligence
  • gain a comprehensive training in the various disciplines constituting data science, with a strong emphasis on the mathematics and statistics methodology
  • master sophisticated techniques both theoretically and in practice

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.

On average, almost 25% of the students pursue with a PhD, while the others pursue in the industry.

40 ECTS of courses to validate over the 3 quarters

20 ECTS for internship

Students of the Master can choose various courses, from very theoretical and mathematically involved courses, to more practical ones. In broad terms, the courses cover*:

  • Convex optimization 
  • Reinforcement learning 
  • Graphical Models 
  • Deep Learning
  • Theory of Deep Learning 
  • Machine Learning Theory 
  • Machine Learning for Audio, Text, Graph, Dynamical Data
  • Markov Chain Monte Carlo methods 

The courses also cover**:

  1. Large scale machine learning/ Big Data 
  2. (Advanced) Optimization
  3. Machine Learning & advanced methods
  4. Business or ethical aspects of ML 

*quite similarly to the MVA Master

** more specific to the Master Data Science - examples in (3) that are not covered by the MVA Master : Time Series, Causal Inference, Tail Event Analysis...

Data-based generative models

20h

3 ECTS 

English

Large Scale Machine Learning/Big Data

Systems for Big Data Analytics

20h

3 ECTS 

English

Project + oral presentation

Big Data Framework

40h

5 ECTS 

English

Optimization

Convex Analysis and Optimization Theory

40h

5 ECTS 

English

Final exam

Optimization for Data Science

40h

5 ECTS

English

Machine Learning

Practical Introduction to Machine Learning

20h

3 ECTS 

English

Practical session reports

An Introduction to Machine Learning Theory

20h

3 ECTS 

English

Statistical Learning Theory 

20h

3 ECTS 

English

Final exam

High-Dimensional Statistics     

20h

3 ECTS 

English

Final exam

Non Parametric Estimation and Testing

20h

3 ECTS 

English

Projects

Probabilistic Graphical Models - Applications to Information Access

20h

3 ECTS 

English

Final exam + homeworks

Markov Chain Monte Carlo- Theory and practical applications

20h

3 ECTS 

English

Quiz + project

Hidden Markov models and Sequential Monte Carlo methods

20h

3 ECTS 

English

Projects

Natural Language Processing and Sentiment Analysis

20h

3 ECTS 

English

Lab report / Article study

Deep Learning I

20h

3 ECTS 

English

Final exam

Introduction to reinforcement learning

20h

3 ECTS 

English

Project/article study


Partially Observed Markov Chains in Signal and Image

20h

3 ECTS 

French

Final exam


Advanced AI methods for Graphs and NLP (ALTEGRAD)

28h

5 ECTS 

English

Data challenge on kaggle/6 weeks project + oral presentation

Generalisation properties of algorithms in ML

20h

3 ECTS 

English

Quizzes + project/article

High Dimensional Matrix Estimation

20h

3 ECTS 

English

Final exam/article

Introduction to computer vision

20h

3 ECTS 

English

Business and ethical aspects of ML

Law and ethics of artificial intelligence

20h

3 ECTS 

English

Machine learning: business cases

20h

3 ECTS 

English

Project

Large scale Machine Learning/Big Data

Data Stream Processing  

20 h00

2.5 ECTS 

English

Operations Research and Big Data  

20 h00

3 ECTS 

French

 

Cloud Data Infrastructure

20 h00

3 ECTS 

English

 

Data Stream Processing

20h00

3 ECTS 

English

Practical assignments + article presentation

 

Optimization

Optimization for Data Science

40 h00

5 ECTS 

English

Non Differentiable Optimization and Proximal Methods

20 h00

3 ECTS 

English

Machine Learning

Causal Inference

20 h00

3 ECTS 

English

Final exam + project

Online Learning and Aggregation

20 h00

3 ECTS 

English

Final exam

Auction Theory & in practice

20 h00

3 ECTS 

English

Deep Learning Advanced

20 h00

3 ECTS 

English

Introduction to Time Series

20 h00

3 ECTS 

English

A Mathematical Introduction to Compressed Sensing

20 h00

3 ECTS 

English

Deep Learning II

20 h00

3 ECTS 

English

Project

Audio and Music Information Retrieval

20h00

3 ECTS

English

Final exam

Topics in stochastic filtering, Information and Control

20h00

3 ECTS

English

Practical and theoretical assignments

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

20h00

3 ECTS

English

Final exam

NLP

20h00

3 ECTS

English

Quizz

Applied Deep Learning with Python

40 h00

5 ECTS 

English

Final exam + optional project

Big data & insurance project

20h00

3 ECTS

English

Structured Data: Learning and Prediction

20h00

3 ECTS

English

Tail events analysis: Robustness, outliers and models for extreme

20h00

3 ECTS

English

Stochastic approximation and reinforcement leaning

20h00

3 ECTS

English

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

20h00

3 ECTS

English

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" of the FAQs

Find out more about scholarships

Applications and admission dates