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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

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

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. 

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.

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...

Large Scale Machine Learning/Big Data

Big Data Framework

40h

6 ECTS 

English

 

Optimization

Convex Analysis and Optimization Theory

40h

6 ECTS 

English

Final exam

 

Machine Learning 

Practical Introduction to Machine Learning

20h

3 ECTS 

English

Practical session reports

An Introduction to Machine Learning Theory

20h

3 ECTS 

English

Final exam

Statistical Learning Theory  

20h

3 ECTS 

English

Final exam

High-Dimensional Statistics     

20h

3 ECTS 

English

Final exam

Non Parametric Estimation

20h

3 ECTS 

English

Final exam

Markov Chain Monte Carlo - Theory and practical applications

20h

3 ECTS 

English

Projects

Monte Carlo Methods: from MCMC to Data-based Generative model

40h

6 ECTS

English

Quiz+projects

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

Large Scale Machine Learning/Big Data

Data Stream Processing

20h

3 ECTS 

English

Practical assignments+article presentation

 

Optimization

Optimization for Data Science

40h

6 ECTS 

English

 

Machine Learning

Generalisation properties of algorithms in ML

20h

3 ECTS 

English

Quizzes + project/article

High Dimensional Matrix Estimation

20h 

3 ECTS 

English

Final exam/article

Online Learning and Aggregation

20h

3 ECTS 

English

Final exam

Advanced AI for Text and Graph Data

28h

5 ECTS 

English

Data challenge  on kaggle/6 weeks project + oral presentation

Computer Vision

20h

3 ECTS 

English

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

DataCamp (Mandatory, end of P2, all week)

3 ECTS 

English

 

Business and ethical aspects of ML

Law and Ethics of Artificial Intelligence

20h

3 ECTS 

English

 


 

Large Scale Machine Learning

Operations Research and Big Data

20h

3 ECTS 

French

Cloud Data Infrastructure

20h

3 ECTS 

English

 

Optimization

Cooperative Optimization for Data Science

20h

3 ECTS

English

Stochastic approximation and reinforcement learning

20h00

3 ECTS

English

 

Machine Learning

Advanced topics in Deep Learning

20h

3 ECTS 

English

Final exam

Introduction to Time Series

20h

3 ECTS 

English

Deep Learning II

20h

3 ECTS 

English

Project

Audio and Music Information Retrieval

20h

6 ECTS

English

Final exam

Multi Object Estimation and Filtering

20h

3 ECTS

English

Practical and theoretical assignments

Structured Data: Learning and Prediction 

20h

3 ECTS

English

Tail events analysis: Robustness, outliers and models for extreme 

20h

3 ECTS

English

Missing Data and causality

20h

3 ECTS

English

Final exam + project

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

20h

3 ECTS

English

Final exam + project

ML Research Seminar

6 ECTS

English

Capstone 

6 ECTS

English

Project

 

Business and Ethical Aspects of ML 

Big Data & Insurance Project

20h

3 ECTS 

English

 

  • Internship of minimum 16 weeks
  • From April to end of August
  • 18 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

Registration fees are available here

Find out more about scholarships

Please note that fees and scholarships may change for the following year.

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. 

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.

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...

Large Scale Machine Learning/Big Data

Big Data Framework

40h

6 ECTS 

English

 

Optimization

Convex Analysis and Optimization Theory

40h

6 ECTS 

English

Final exam

 

Machine Learning 

Practical Introduction to Machine Learning

20h

3 ECTS 

English

Practical session reports

An Introduction to Machine Learning Theory

20h

3 ECTS 

English

Final exam

Statistical Learning Theory  

20h

3 ECTS 

English

Final exam

High-Dimensional Statistics     

20h

3 ECTS 

English

Final exam

Non Parametric Estimation

20h

3 ECTS 

English

Final exam

Markov Chain Monte Carlo - Theory and practical applications

20h

3 ECTS 

English

Projects

Monte Carlo Methods: from MCMC to Data-based Generative model

40h

6 ECTS

English

Quiz+projects

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

Large Scale Machine Learning/Big Data

Data Stream Processing

20h

3 ECTS 

English

Practical assignments+article presentation

 

Optimization

Optimization for Data Science

40h

6 ECTS 

English

 

Machine Learning

Generalisation properties of algorithms in ML

20h

3 ECTS 

English

Quizzes + project/article

High Dimensional Matrix Estimation

20h 

3 ECTS 

English

Final exam/article

Online Learning and Aggregation

20h

3 ECTS 

English

Final exam

Advanced AI for Text and Graph Data

28h

5 ECTS 

English

Data challenge  on kaggle/6 weeks project + oral presentation

Computer Vision

20h

3 ECTS 

English

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

DataCamp (Mandatory, end of P2, all week)

3 ECTS 

English

 

Business and ethical aspects of ML

Law and Ethics of Artificial Intelligence

20h

3 ECTS 

English

 


 

Large Scale Machine Learning

Operations Research and Big Data

20h

3 ECTS 

French

Cloud Data Infrastructure

20h

3 ECTS 

English

 

Optimization

Cooperative Optimization for Data Science

20h

3 ECTS

English

Stochastic approximation and reinforcement learning

20h00

3 ECTS

English

 

Machine Learning

Advanced topics in Deep Learning

20h

3 ECTS 

English

Final exam

Introduction to Time Series

20h

3 ECTS 

English

Deep Learning II

20h

3 ECTS 

English

Project

Audio and Music Information Retrieval

20h

6 ECTS

English

Final exam

Multi Object Estimation and Filtering

20h

3 ECTS

English

Practical and theoretical assignments

Structured Data: Learning and Prediction 

20h

3 ECTS

English

Tail events analysis: Robustness, outliers and models for extreme 

20h

3 ECTS

English

Missing Data and causality

20h

3 ECTS

English

Final exam + project

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

20h

3 ECTS

English

Final exam + project

ML Research Seminar

6 ECTS

English

Capstone 

6 ECTS

English

Project

 

Business and Ethical Aspects of ML 

Big Data & Insurance Project

20h

3 ECTS 

English

 

  • Internship of minimum 16 weeks
  • From April to end of August
  • 18 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

Registration fees are available here

Find out more about scholarships

Please note that fees and scholarships may change for the following year.

Applications and admission dates