Cognitive, Creation and Learning Engineering

Specialty: Modeling and Statistical Learning in Social Sciences


Last update:
30/10/08


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Description

The teaching of core focuses on methodological issues, data processing and computer testing. The mathematical formalization is rather in dealt optional. Students are highly motivated and have enough mathematical background, will be allowed to move towards research and possibly higher education. The training period is compulsory study in business for orientation "professional" and laboratory guidance for "research".

First year

Semester 1
Common core
- Introduction to experimental probability (24 h. - 3 credits)
- Basic Statistics Data Mining (24 h. - 3 credits)
- Data Analysis (48 h. - 6 credits)
- Linear models (24 h. - 3 credits)
- Dynamical Systems 1 (48 h. - 6 credits)
- Statistical Software 1 (24 h. - 3 credits)
- Databases 1 (24 h. - 3 credits)
Options
- Probability and analysis (24 h. - 3 credits)
- Basic statistical inference (24 h. - 3 credits)
- Mathematics for signal processing (24 h. - 3 credits)
- Modeling by graphs (24 h. - 3 credits)
- Algorithms 1 (48 h. - 6 credits)

Semester 2
Common core
- Nonparametric statistics and resampling methods (24 h. - 3 credits)
- Sampling techniques (24 h. - 3 credits)
- Time series 1 (24 - 3 credits)
- Introduction to stochastic processes (24 h. - 3 credits)
- Statistical Software 2 (24 - 3 credits)
- English (scientific communication) (24 - 3 credits)

Options
- Databases 2 (24 - 3 credits)
- Econometric methods (24 h. - 3 credits)
- Supervised statistical learning (24 h. - 3 credits)
- Unsupervised statistical learning (24 h. - 3 credits)
- Project Management (24 h. - 3 credits)
- Languages and Web technologies (24 h. - 3 credits)
- Information Retrieval (24 h. - 3 credits)
- Right of information technology and business organization (24 h. - 3 credits)

Second year

Semester 3
Common core
- Introduction to spatial statistics (24 h. - 3 credits)
- Designs of experience, analysis of variance (48h - 6 credits)
- Time Series 2 (24 h. - 3 credits)
- Modeling qualitative data (24 h. - 3 credits)
- Structural equation model (24 h. - 3 credits)
- Options
- Nonparametric models and non-parametric and Generalized linear model
- Dynamical Systems 2 (24 h. - 3 credits)
- Mixed models and structured data (24 h. - 3 credits)
- Neural Networks (24 h. - 3 credits)
- English (Science Communication) (24 h. - 3 credits)
- Research Seminar (Master research purposes) (48 h. - 6 credits)

Semester 4
- Professional trainee (Professionnal profile) ( 24 credits)
- Research trainee (Research profile) (24 credits)
- Statistical project (48h. - 6 credits)