Master of Science Data Analysis and Pattern Classification (DATAPAC)
Télécom SudParis
Engineering degree - Master’s level
Courses are taught in English during the first year with French courses in parallel. In the second year, lectures are taught in French.
Presentation
This Master of Science meets the growing needs of multimedia data processing for automatic classification or prediction, in a very wide scope of applications. Artificial Intelligence and Signal Processing techniques are taught with an interdisciplinary approach, with particular emphasis in spanning the complete data processing chain: sensing multimedia data, data pre-processing, automatic feature extraction, artificial learning and decision making. Concrete Case Studies are targeted with this particular view, through personalized projects under the supervision of professors.
This Master program may lead to careers in industrial Research and Development departments as well as to a PhD program in a Research Laboratory.
Program Overview
In several applicative areas such as Medical Imaging, Virtual Reality, Telecommunications, Tele-monitoring, Biometrics, Bioinformatics, Environmental Sciences, Banking, Insurance, Data Mining (textual, multimedia), great amounts of data are captured through devoted sensors and must be processed and further analyzed for different purposes related to the application.
This Master degree is designed to provide Signal Processing and Machine Learning tools allowing multi-sensor real data analysis, at each step of the data processing workflow. Raw data captured by sensors is often noisy or degraded and must be pre-processed; then, pertinent information (features) must be extracted and modeled through Machine Learning techniques, for automatic classification or prediction purposes. Through an interdisciplinary approach, advanced knowledge in Signal Processing, Statistical Models, Machine Learning, Computer Vision, Data Fusion, and Content-based Indexing and Retrieval in Multimedia databases is given to future data scientists.
Objectives
- Providing Statistical Learning and Signal Processing tools to model great amounts of data, captured by specialized sensors, for automatic data classification or prediction;
- Analyzing data from different applicative areas (Environmental Sciences, Telecommunications, Genomics, e-health, Telemedicine, Biometrics, Economics, Banking, Insurance, Multimedia Data Mining, Geo-localization,…)
- Conceiving complex systems in several areas
Whay should you apply?
The Master is open to candidates with a Bachelor Degree in Computer Science, Physics or Engineering from a recognized university. A good background in Mathematics and Computer Science is required.
Career Perspectives
- Data Scientists
- R&D engineers
- Project managers
- PhD position in a research laboratory
Partners
CNES, Renault, Orange, EDF, ONERA, Thales, IFP, IDEMIA, MIRIAD Technologies, Legrand, Société générale, Crédit Agricole, Air Liquide, Safran Aircraft Engines, CEA, Novartis, Institut Pierre Simon Laplace, Centre des Environnements Terrestre et Planétaires (CETP/IPSL).
Language of Instruction
Courses are taught in English during the first year with French courses in parallel. In the second year, lectures are taught in French. Students’ projects or Master Thesis can be written and presented in English or French
First year
Level: Master 1
Language of instruction: English
Location: Telecom SudParis, Evry
Cost: 4000 or 6000 euros (see more on Tuition Fees)
Coordinator: Professor Sonia Garcia-Salicetti, Telecom SudParis
Master 1 (M1): This first year of the Master in English enables students to acquire fundamentals in Computer Science, Mathematics, Optimization, Statistical Data Analysis, Signal Processing, Information Theory, French, and carrying out a Scientific Project. This project, in the second semester, is an initiation to research in Data Science, and an occasion for a complete training through practice, while developing oral and written communication skills.
First year program
| Code | Title | Credits |
| CSC 7001 | Computer Science | 6 |
| HUM 7001 | Effective Communication | 3 |
| MAT 7001 | Fundamentals of Probability and Statistics | 3 |
| FRE 7001 | French | 3 |
| MAT 7006 | Optimization Methods | 6 |
| MAT 7007 | Application of Statistical Methods | 6 |
| IMA 7221 | Statistical Data Analysis | 3 |
| Code | Title | Credits |
| SIC 7221 | Introduction to Information Theory and Signal processing | 5 |
| FRE 7002 | French | 2 |
| IMA 7515 | Scientific Project | 8 |
| IMA 4511 | Pattern Recognition & Biometrics | 5 |
| SIC 7002 | Artificial Intelligence for Data Science | 4 |
| SIC 7001 | Signal Enhancement Methods | 6 |
Second year
Level: Master 2
Language of instruction: French
Location: Telecom SudParis, Palaiseau
Cost: 4000 or 6000 euros (see more on Tuition Fees)
Coordinator: Professor Sonia Garcia-Salicetti, Telecom SudParis
Master 2 (M2): The second year enables students to deepen their knowledge through Core courses combining theory and practice, on Statistical Models, Neural Networks, Machine Learning, Deep Learning, Data Analysis and Signal Processing. Optional Courses allow increasing skills in different possible orientations, as Information Retrieval in Databases, Sensors (IoT), or Image Processing. The second year also involves Case Studies on Data Science, a one-month full time project and the Master Thesis in Industry or a Research Laboratory.
Core courses (24 ECTS)
| Code | Title | Credits |
| MAT 7201 | Analyse statistique jeu de données réelles / Statistical Analysis of Real Data | 6 |
| NET 7202 | Apprentissage profond / Deep Learning | 3 |
| PHY 7201 | Reconnaissance de formes et méthodes neuronales / Pattern Recognition and Neural Networks | 6 |
| MAT 7202 | Méthodes statistiques données qualitatives / Qualitative Data Analysis | 3 |
| CSC 7202 | Réseaux bayésiens-Chaînes Markov Cachées / Bayesian Networks and Hidden Markov Models | 3 |
| ENG 7201 | Anglais / English | 3 |
Optional courses: 9 ECTS to be chosen
| Code | Title | Credits |
| CSC 7203 | Base de données recherche d'information/ Databases and Information Retrieval | 3 |
| CSC 7204 | Objets connectés: principes & fiabilité capteurs / Connected Objects: Principles and Sensors’ Reliability | 6 |
| CSC 7205 | Traitement d’images / Image Processing | 6 |
| CSC 7206 | Comparaison de méthodes de classification / Case Study on Data Science II: On Comparing Classifiers | 3 |
| CSC 7207 | Etude de Cas en Data Science / Case Study on Data Science I | 3 |
Optional courses: 3 ECTS to be chosen
| Code | Title | Credits |
| CSC 7208 | Projet de recherche en data science / Research Project on Data Science | 3 |
| HUM 7201 | Connaissance de l'entreprise/ R&D in Industry and Data Science | 3 |
| CSC 7209 | Base de données pour Big Data/ Databases for Big Data | 3 |
The second semester is devoted to an internship in a company or in a research and development laboratory related to the field (24 ECTS).
Core courses (24 ECTS)
| Code | Title | Credits |
| MAT 7201 | Analyse statistique jeu de données réelles / Statistical Analysis of Real Data | 6 |
| NET 7202 | Apprentissage profond / Deep Learning | 3 |
| PHY 7201 | Reconnaissance de formes et méthodes neuronales / Pattern Recognition and Neural Networks | 6 |
| MAT 7202 | Méthodes statistiques données qualitatives / Qualitative Data Analysis | 3 |
| CSC 7202 | Réseaux bayésiens-Chaînes Markov Cachées / Bayesian Networks and Hidden Markov Models | 3 |
| ENG 7201 | Anglais / English | 3 |
Optional courses: 9 ECTS to be chosen
| Code | Title | Credits |
| CSC 7203 | Base de données recherche d'information/ Databases and Information Retrieval | 3 |
| CSC 7204 | Objets connectés: principes & fiabilité capteurs / Connected Objects: Principles and Sensors’ Reliability | 6 |
| CSC 7205 | Traitement d’images / Image Processing | 6 |
| CSC 7206 | Comparaison de méthodes de classification / Case Study on Data Science II: On Comparing Classifiers | 3 |
| CSC 7207 | Etude de Cas en Data Science / Case Study on Data Science I | 3 |
Optional courses: 3 ECTS to be chosen
| Code | Title | Credits |
| CSC 7208 | Projet de recherche en data science / Research Project on Data Science | 3 |
| HUM 7201 | Connaissance de l'entreprise/ R&D in Industry and Data Science | 3 |
| CSC 7209 | Base de données pour Big Data/ Databases for Big Data | 3 |
The second semester is devoted to an internship in a company or in a research and development laboratory related to the field (24 ECTS).
Contact
For any question related to admissions:
E-mail: @admissions-msc
