Courses taught at CNAM

Storage and data mining (STA211)
CNAM, Paris [2018 -]

• Data warehousing
• Preprocessing
• Data mining using supervised and non-supervised methods
• Bagging, Boosting
• R software

Data analysis (STA101)
CNAM, Paris [2018 -]

• Principal components methods
• Clustering using distance-based methods
• R software
  Material: Week 1 - Week 2 - Week 3 - Week 4

Advanced multivariate analysis (STA201)
CNAM, Paris [2018 -]

• Statistical modelling with incomplete data
• R software

Business Intelligence (NFE212)
CNAM, Paris [2025 -]

• Introduction to supervised learning
• Decision trees, random forests
• AutoML
• Bias and discrimination in decision-making systems
• Text Mining

Courses previously taught

Statistics for video games
CNAM-ENJMIN, Angoulême [2018]

• Probability
• Descriptive statistics
• Statistical inference
• Modelling

Probability and Statistics
CNAM, Paris [2017 - 2018]

• Probability
• Descriptive statistics
• Statistical inference

Mathematical Statistics (STA104)
CNAM, Paris [2017 - 2018]

• Simulation and MCMC methods
• Estimation : maximum likelihood, Fischer information, Cramer Rao bound
• Confidence intervals
• Tests for means, variances, proportions
• Tests for paired samples
• Goodness of fit tests and test of independence
• Non-parametric statistics
• Bootstrap

Biostatistics
CNAM-Pasteur, Paris [2017 - 2019]

• Statistical inference (estimator, bias, variance, convergence, central limit theorem, classical confidence intervals)
• Statistical tests for 2 groups
• Univariate and bivariate analysis

Courses taught at other institutions

Handling incomplete data
ENSAE, Paris [2018 -]

• Missing data mechanisms
• Single imputation
• Multiple imputation
• Data mining on incomplete data

Missing values
L’institut Agro, Dijon [2026 - ]

• Missing data mechanisms
• Single imputation
• Multiple imputation

Courses previously taught

Multiple imputation using Fully Conditional Specification
Rencontres SFC 2024

• Abstract
• Tutorial

Multiple imputation using Fully Conditional Specification
Journées d’Etude en Statistique 2021

• Lecture 1 - Lecture 2
• Tutorial

Modelling
Université Paris Dauphine [2018 - 2019]

• Handling missing values using multiple imputation
• Multiple imputation methods for large data sets

Assessment of analysis methods for biosciences
Université Paris-Diderot, Paris

• Descriptive statistics
• Elementary probability (independence, condional probabilities, Bayes theorem)
• Random variables (density function, cumulative distribution function, expectation, variance)
• Sum of random variables (convergence in distribution, central limit theorem)
• Statistical tests (Khi-2, Fisher, z-test, Wilcoxon)

Biostatistics and R software
Université Paris-Diderot, Paris [2016 - 2017]

• Statistical inference (estimator, bias, variance, convergence, central limit theorem, classical confidence intervals)
• Statistical tests for 3 groups (ANOVA, Kruskal-Wallis, Khi-2, Fisher)
• Statistical tests for categorical variables (Khi-2, Fisher, McNemar)

Modelling
Université Paris-Diderot, Paris [2016 - 2017]

• Handling missing values using multiple imputation

General statistics
Agrocampus-Ouest, Rennes [2012 - 2015]

• Multiple linear regression
• Analysis of variance with several factors and interaction
• Fractional factorial design
• Principal Components Analysis (PCA)

Analysis of survey data
Agrocampus-Ouest, Rennes [2013 - 2015]

• Multiple Correspondence Analysis
• Hierarchical Clustering on Principal Components

Sensometry
Agrocampus-Ouest, Rennes [2013]

• Characterization of products
• Performance of a panel
• Preference mapping