About statistical modeling
 intro courses:
 "OpenIntro Statistics" by Diez, Barr and CetinkayaRundel (free textbook)
 "Statistics Done Wrong" by Alex Reinhart (free textbook)
 "Mixed effects models for the population approach" by Marc Lavielle and the POPIX team at INRIA (free wiki)
 "Graphical Models" by Zoubin Ghahramani (2012, free video & slides)
 swirl and mosaic, R packages to learn stats and R simultaneously and interactively
 advanced courses:
 "Advanced Data Analysis from an Elementary Point of View" by Cosma Shalizi (free book)
 "A First Course in Bayesian Statistical Methods" by Peter Hoff (2010, book)
 "Bayesian Data Analysis" by Andrew Gelman & co (2013, free slides, 3rd edition of the book)
 "Statistical Decision Theory and Bayesian Analysis" by James Berger (1993, 2nd edition of the book)
 "Intermediate Statistics" by Larry Wasserman (free lecture notes)
 "Stat Fact Sheets" by Eric Anderson (free tex files)
 mathematical aspects:
 "Introduction to Linear Algebra" by Gilbert Strang (free videos, book)
 "Matrix Differential Calculus with Applications in Statistics and Econometrics" by Magnus and Neudecker (2007, free pdf for the 3rd edition)
 practical, computational aspects:
 "How to share data with a statistician" by Jeff Leek (procedure on GitHub), see also the advice on genomics metadata by Raphael Irrizary and "statistical consulting" by Karl Broman (slides)
 "Exploratory Data Analysis with R" by Jennifer Bryan (free course)
 "Tutorial on Big Data with Python" by Marcel Caraciolo (free Python notebooks)
 interpreted languages: obviously R, but more and more Python (SciPy for NumPy, Matplotlib, and pandas, but see also scikitlearn and statsmodels), as well as others (Julia)
 C/C++: GSL, Armadillo, Eigen, Rcpp, Stan
 editor: obviously Emacs (languageagnostic, orgmode, etc), but also Rstudio (Ronly...) and IPython (Pythononly...)
 visualizing, plotting:
 "Visualizing uncertainty about the future" by Spiegelhalter et al. (Science 2011, DOI)
 "Let's practice what we preach: turning tables into graphs" by Gelman et al. (The American Statistician 2002, DOI)
 "Top ten worst graphs" by Karl Broman (webpage)
 "EDA: Investigate, Visualize, and Summarize Data Using Ra" (on Udacity, free courseware available)
 philosophy, history, pragmatism:
 "Statistical analysis and the illusion of objectivity" by Berger and Berry (American Scientist 1988, DOI, pdf)
 "Bayesian methods: general background" by E. T. Jaynes (1985, free pdf) and "Where do we stand on maximum entropy?" by E. T. Jaynes (1978, free pdf)
 "The Philosophy of Statistics" by Lindley (JRSSD 2000, DOI)
 "What is statistics?" by Feinberg (An.Rev.Stat.Appl. 2014, DOI)
 "Mathematical Models and Reality: A Constructivist Perspective" by Christian Hennig (Foundations of Science 2010, DOI)
 "Philosophy and the practice of Bayesian statistics" by Andrew Gelman and Cosma Shalizi (British Journal of Mathematical and Statistical Psychology 2013, DOI)
 "Statistical Inference : the Big Picture" by Robert Kass (Statistical Science 2011, DOI, free pdf on arXiv)
 "In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist" by Roderick Little (JASA 2013, DOI)
 "Des spécificités de l’approche bayésienne et de ses justifications en statistique inférentielle" par Christian Robert (chapitre 2013, pdf gratuit sur HAL)
 classics:
 list from Christian Robert
 litterature, community:
 Annals of Statistics, JRSSB, JASA, Annals of Applied Statistics, Bayesian Analysis, JMRL, NIPS
 Biometrics, Biostatistics, Biometrika
 Statistical Science, The American Statistician, Annual Review of Statistics and its Application
 see also on Project Euclid and arXiv
 blogs: Andrew Gelman, Christian Robert, Larry Wasserman
 links with society: JRSSA, Statistique et Société (free pdfs)
