Julie Josse

Teaching

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I was teaching at Polytechnique for the 3A, in the Master Data Sciences for Business (I was in charge of) and in the Master Data Sciences.

• • • R for statistics: bookdown 2017.
    • Machine learning: unsupervised – supervised learning
    • Regression
    • Missing values: slides  – Lecture notes –
      Data analysis html –   Data analysis Rmd –   ozone data – plant data
    • Causal Inference

Lecture on multivariate exploratory data analysis, PCA, CA, MCA, MultiTable data:
François Husson playlist on youtube (lecture, lab with R + MOOC) + FactoShiny
older: Correspondence Analysis, multiple correspondence analysis, multi-block methods–

Reference book: Elements of statistical learning

Some tutorials. For recent tutorials on missing values see the Rmistatic plateform.

Year	Title	Events	slides/video
2021	Short intro on causal inference	Google Brain	slides
2021	Causal inference for observational clinical data	AI4Health	slides Lab, ATE Lab, HTE
2016-2017	Missing values imputation	useR!2016, Stanford Gdr Ecology stat, Lyon INRIA, saclay	Ozone data codeMissUSER2016 slides Lab Notes
2015	Handling missing values with a special focus on the use of principal component methods. (3h).	useR! 2015, the R useR conference, Aalborg, Denmark
2014	Lecture on dealing with missing values. (3h)	Stanford University, CA, USA	[slides] [ozone data] [code] [ecological data] [code]
2011	Panorama sur les méthodes d'analyse exploratoire des données. (3h)	Journées de l'Ecole Doctorale Pierre Louis de Santé Publique : Epidémiologie et Sciences de l'Information Biomédicale. St Malo, France	[slides, data]