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" of the FAQs
Find out more about scholarships
Applications and admission dates
Coordinator
Program office
General enquiries
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" of the FAQs
Find out more about scholarships