This is just a place to link to the on-line resources we find most interesting.
Please report any dead links to admin@statisticalepidemiology.org.
Growth methods
Open Science
Statistics and Data
- Book – Advanced Data Analysis from an Elementary Point of View (Shalizi, 2012)
- Book – An Introduction to Statistical Inference and Its Applications (Michael W. Trosset, 2006)
- Book – Bayesian reasoning and machine learning (David Barber, 2010)
- Book – Casual Inference (Miguel Hernán, 2012)
- Book – Elements of Statistical Learning (Hastie, Tibshirani, Friedman, 2011)
- Book – Forecasting: principles and practice (Rob J Hyndman and George Athanasopoulos)
- Book – Graph theory with applications (Bondy and Murty)
- Book – Introduction to Probability (Grinstead, Snell)
- Book – Introduction to Probability and Statistics Using R (Kearns, 2010)
- Book – Introduction to Statistical Thought. (Michael Levine)
- Book – Lectures on Stochastic Analysis (Kurtz, 2007)
- Book – Mathematics for Computer Science (Lehman, Leighton, Meyer, 2010)
- Book – Multivariate Statistical Analysis – Old School (John Marden, 2011)
- Book – Probability Theory: The Logic of Science (ET Jaynes)
- Book – Probability: Theory and Examples (Rick Durrett, Berkley, 2010)
- Book – Statistical Theory (Gesine Reinert, 2009)
- Book – Statistics for the Social and Behavioral Sciences (David Kenny)
- Book – StatSoft Electronic Statistics Handbook
- Book – The Little Handbook of Statistical Practice (Dallal)
- Books – InTech Open Access – A ton of machine learning books here.
- Class – Advanced quantitative research methodology (Harvard, Gary King)
- Class – Design and analysis of algorithms (Stanford, Tim Roughgarden)
- Class – Learning from data (Caltech, Yaser Abu-Mostafa)
- Class – Linear Algebra (MIT, Gilbert Strang)
- Class – Machine Learning (Colorado State, Chuck Anderson)
- Class – Machine Learning (Stanford, Andrew Ng)
- Class – Model Thinking (U Michigan, Scott Page)
- Class – Statistical Computing in R (Pitt, Nicholas Christian)
- Class – Statistical Reasoning I (John McGready, Johns Hopkins University)
- Class – Statistical Reasoning II (John McGready, Johns Hopkins University)
- Class – Statistics 110: Probability (Harvard, Joe Blitzstein)
- Class – Topics in Statistical Graphics and Visualization (Iowa, Luke Tierney)
- Lecture – Relations between machine learning problems (Robert Williamson)
- School of Data
- StatSci freely available datasets
- Website – 90 things you can do in R under 2 minutes
- Website – A New View of Statistics (Hopkins, 2001)
- Website – A nice list of beginner R tutorials
- Website – CrossValidated – collaboratively edited question and answer site
- Website – Foundations of Nonparametric Bayesian Methods (Peter Orbanz)
- Website – Free Mathematics Books
- Website – Functional Data Analysis (Ramsay)
- Website – Great tips on Data Citation (Data Pub)
- Website – Machine Learning in Python (SciKit)
- Website – Online Mathematics Textbooks
- Website – Probability Theory and Stochastic Processes (KCL)
- Website – Project Euclid – scholarly communication in the field of theoretical and applied mathematics and statistics
- Website – Reddit r/mathbooks
- Website – Stata Youtube channel
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