Statistical Foundations of Data Science

In the last two decades, the understanding of complex dynamical systems underwent important conceptual shifts. The catalyst was the infusion of new ideas from the theory of critical phenomena (scaling laws, renormalization group, etc.), (multi)fractals and trees, random matrix theory, network theory, and non-Shannonian information theory. The usual Boltzmann-Gibbs statistics were proven to be grossly inadequate in this context. While successful in describing stationary systems characterized by ergodicity or metric transitivity, Boltzmann-Gibbs statistics fail to reproduce the complex statistical behavior of many real-world systems in biology, astrophysics, geology, and the economic and social sciences.

by John D Ramshaw (Author)

Beginners approach to technicalities of Statistics for Data Science

Build a strong math foundation with linear algebra,stats,probability & differential calculus for mastering data science

Data Science 2021 : Complete Data Science