Data Analysis and Pattern Classification

This Master of Science (MSc) degree offers :
  • 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, Textual /Multimedia Data Mining, Geo-localization,…)
  • Conceiving complex systems in several areas
Data Analysis and Pattern Classification

Data Analysis and Pattern Classification (DATAPAC)

Télécom SudParis

une datapac

Presentation

This Master of Science degree meets the needs of multi-sensor data processing stored in very large databases. In several applicative areas such as Telecommunications, Medical Imaging, Virtual Reality, Telemonitoring, Biometrics, Bioinformatics, Environmental Sciences, Banking, Insurance, Data Mining (textual, multimedia), great amounts of data are captured through specialized sensors and must be processed and further analyzed for different purposes related to the application. This Master degree is designed to provide Machine Learning tools allowing all types of real data analysis, from end to end: once raw data has been captured, it is often noisy or degraded and must be pre-processed, in order to remove the noise; 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.

Typical Jobs

  • Data Scientists
  • R&D engineers
  • Project managers
  • PhD position in a research laboratory

Background

Good background in Applied Mathematics and Computer Science is required.

Program

First year

Level: Master1
Language of instruction: lectures taught in English with intensive French courses in parallel – the first semester contains leveling technical courses.
Location: Telecom SudParis, Evry
Cost: 6 000 euros
Coordinator: Prof. Sonia GARCIA-SALICETTI, Telecom SudParis

Semester 1 (Fall – Sept. to Feb.)

Core Courses

  • Computer Science - 6 ECTS
  • Probability and Statistics - 3 ECTS
  • Effective Communication - 3 ECTS

Specific Courses

  • Optimization Methods - 6 ECTS
  • Application of Statistical Methods - 9 ECTS
  • French - 3 ECTS
Semester 1 (Fall – Sept. to Feb.)

Semester 2 (Spring – Feb. to June)

  • Pattern Recognition and Biometrics - 5 ECTS
  • Signal Enhancement Methods - 6 ECTS
  • Signal Processing and Statistical Data Analysis - 5 ECTS
  • Conferences on ICT & Operational Systems - 4 ECTS
  • Scientific Project - 8 ECTS
  • French - 2 ECTS
Semester 2 (Spring – Feb. to June)

Second year

Level: Master 2
Language of instruction: lectures taught in French, with monitoring and on-line materials both available in English – the fourth semester is devoted to the preparation of a Master thesis in a public or private research centre
Location: CNAM, Paris ; Telecom SudParis, Evry
Cost: 6 000 euros
Coordinator: Cécile MALLET, LATMOS, UVSQ

Semester 3 (Fall – Sept. to Feb.)

  • Core Courses

    • Pattern Recognition and Neural Networks - 6 ECTS
    • Statistical Analysis of Real Data - 6 ECTS

    Research Track (« Parcours Recherche »)

    • Image Processing - 6 ECTS
    • Case Study on Data Science I : Deep Learning - 3 ECTS
    • Case Study on Data Science II: On Comparing Classifiers - 3 ECTS
    • Research Project on Data Science - 3 ECTS

     Professional Track (« Parcours Professionnel »)

    • Databases and Information Retrieval - 6 ECTS
    • Connected Objects: Principles and Sensors’ Reliability - 6 ECTS
    • Knowledge of Enterprise - 3 ECTS
Semester 3 (Fall – Sept. to Feb.)

Semester 4 (Spring – Feb. to June)

  • Qualitative Data Analysis - 3 ECTS
  • Bayesian Networks and Hidden Markov Models - 3 ECTS
  • Master Thesis - 24 ECTS
Semester 4 (Spring – Feb. to June)