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**:
- Large scale machine learning/ Big Data
- (Advanced) Optimization
- Machine Learning & advanced methods
- 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
|
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 |
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 |
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 |
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 |
20 h00 2.5 ECTS English |
Optimization
|
Optimization for Data Science |
40 h00 5 ECTS English |
| Non Differentiable Optimization and Proximal Methods |
20 h00 3 ECTS English |
Machine Learning
|
Generalisation properties of algorithms in ML |
20 h00 3 ECTS English Quizzes + project / article |
| High Dimensional Matrix Estimation |
20 h00 3 ECTS English Final exam/article |
| Bootstrap and resampling methods in machine learning |
20 h00 3 ECTS English |
| Causal Inference |
20 h00 3 ECTS English Final exam + project |
| Online Learning and Aggregation |
20 h00 3 ECTS English Final exam |
| Advanced Learning for Text and Graph Data |
28 h00 5 ECTS English Data challenge on kaggle / 6 weeks project + oral presentation |
| Computer Vision |
20 h00 3 ECTS English |
| Introduction to Reinforcement Learning |
20 h00 3 ECTS English Project / Article study |
| Partially Observed Markov Chains in Signal and Image |
20 h00 3 ECTS French Final exam |
Business and ethical aspects of Machine Learning
|
Law and Ethics of Artificial Intelligence |
20 h00 3 ECTS English |
| Machine Learning : Business cases |
20 h00 3 ECTS English Project |
Large Scale Machine Learning
|
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 |
Machine Learning
|
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 |
| Multi Object Estimation and Filtering |
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 |
Business and ethical aspects of Machine Learning
|
Big Data & Insurance Project |
20 h00 3 ECTS English |
Internship for non-apprentice students
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.
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**:
- Large scale machine learning/ Big Data
- (Advanced) Optimization
- Machine Learning & advanced methods
- 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
|
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 |
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 |
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 |
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 |
20 h00 2.5 ECTS English |
Optimization
|
Optimization for Data Science |
40 h00 5 ECTS English |
| Non Differentiable Optimization and Proximal Methods |
20 h00 3 ECTS English |
Machine Learning
|
Generalisation properties of algorithms in ML |
20 h00 3 ECTS English Quizzes + project / article |
| High Dimensional Matrix Estimation |
20 h00 3 ECTS English Final exam/article |
| Bootstrap and resampling methods in machine learning |
20 h00 3 ECTS English |
| Causal Inference |
20 h00 3 ECTS English Final exam + project |
| Online Learning and Aggregation |
20 h00 3 ECTS English Final exam |
| Advanced Learning for Text and Graph Data |
28 h00 5 ECTS English Data challenge on kaggle / 6 weeks project + oral presentation |
| Computer Vision |
20 h00 3 ECTS English |
| Introduction to Reinforcement Learning |
20 h00 3 ECTS English Project / Article study |
| Partially Observed Markov Chains in Signal and Image |
20 h00 3 ECTS French Final exam |
Business and ethical aspects of Machine Learning
|
Law and Ethics of Artificial Intelligence |
20 h00 3 ECTS English |
| Machine Learning : Business cases |
20 h00 3 ECTS English Project |
Large Scale Machine Learning
|
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 |
Machine Learning
|
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 |
| Multi Object Estimation and Filtering |
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 |
Business and ethical aspects of Machine Learning
|
Big Data & Insurance Project |
20 h00 3 ECTS English |
Internship for non-apprentice students
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