About statistical modeling
 great courses:
 "Advanced Data Analysis from an Elementary Point of View" by Cosma Shalizi (free online book)
 "A First Course in Bayesian Statistical Methods" by Peter Hoff (book)
 "Bayesian Data Analysis" by Andrew Gelman (free online slides, book)
 mathematical aspects:
 "Introduction to Linear Algebra" by Gilbert Strang (free online videos, book)
 "Matrix Differential Calculus with Applications in Statistics and Econometrics" by Magnus and Neudecker (free online pdf, book)
 practical, computational aspects:
 "Exploratory Data Analysis with R" by Jennifer Bryan (free online course)
 "Tutorial on Big Data with Python" by Marcel Caraciolo (free online 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:
 "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)
 "Des spécificités de l’approche bayésienne et de ses justifications en statistique inférentielle" by Christian Robert (chapitre 2013, free online pdf)
 classics:
 list from Christian Robert
