About statistical modeling
 great 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 (book)
 "Bayesian Data Analysis" by Andrew Gelman (free slides, book)
 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 (free pdf, book)
 practical, computational aspects:
 "How to share data with a statistician" by Jeff Leek (free on github)
 "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, IPython, Matplotlib, and pandas, but also scikitlearn and statsmodels), as well as others (Julia?)
 C/C++: GSL, Armadillo, Eigen, Rcpp, Stan
 editor: Emacs
 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)
 philosophy, history, pragmatism:
 "Mathematical Models and Reality: A Constructivist Perspective" by Christian Hennig (Foundations of Science 2007, 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" by Christian Robert (chapitre 2013, free pdf on HAL)
 classics:
 list from Christian Robert
 litterature, community:
 Annals of Statistics, JRSSB, JASA, Annals of Applied Statistics, Bayesian Analysis, JMRL, NIPS
 Biometrics, Biostatistics
 Statistical Science, The American Statistician
 see also on Project Euclid and arXiv
 blogs: Andrew Gelman, Christian Robert, Larry Wasserman
