Statistics

This course covers linear models, regression analysis, analysis of variance, with applications in various fields. Students use a specialized statistical software package to analyze linear models. 

Statistical learning is the process of extracting regularities from data using statistical models with the goal of finding a predictive function based on existing data to be able to make prediction on unseen data of similar type. The course introduces the concepts and analytical tools of statistical learning, it emphasizes “learning by doing“ with the use of R programming language to perform analysis on empirical data. The first part of the course starts with a refresher on the fundamentals of statistics—mean, variance, distribution, probabilities—before proceeding to more specialized topics. The first part of this course also gives a gentle introduction to R programming, during which issues of dimensionality and balance are discussed with their diagnostic and preprocessing tasks implemented in R. The second part of the course introduces families of binary, penalized, discriminant, and mixture models, along with performance evaluation metrics. The course concludes with the trendy topic on text mining, that is, drawing inference from text data.

This course is part of the LM degree program and is intended for advanced level students. Enrollment is by consent of the instructor. This course discusses bioinformatics and data science, with direct applications on molecular biology, genetics, genomics, and transcriptomics. The course discusses topics including next generation sequencing, bioinformatics file formats, the UNIX environment, online bioinformatics tools, gene networks, bioinformatics databases, cancer bioinformatics, sequence acquisition, phylogenetic analysis, R statistical environment, and graphics with R.

This course offers a study of statistical software. Topics include: R language; introduction to programming and manipulation of objects and files; user-defined functions, statistics, and graphs; R libraries and internet resources.

This course provides an overview of the use of quantitative methods in sociology. It alternates between presentation and discussion of quantitative sociology methods and the techniques most used in this discipline (univariate and bivariate descriptive statistics, logistic regressions), and practical application using R software. Students conduct their own research project implementing these methods with research topics based on the 2018 European Values Survey highlighting the values, political opinions, and representations of the French. 

This course covers actuarial mathematics for life insurance. Students learn how to price standard life insurance and annuity products. The course is highly mathematical with quite a few actuarial formulas used in both academia and industry.