Basic Data Analysis and More: A Guided Tour Using Python
by O. Melchert
Publisher: arXiv 2012
Number of pages: 62
In these lecture notes, a selection of frequently required statistical tools will be introduced and illustrated. They allow to post-process data that stem from, e.g., large-scale numerical simulations. From a point of view of data analysis, the concepts and techniques introduced here are of general interest and are, at best, employed by computational aid. Consequently, an exemplary implementation of the presented techniques using the Python programming language is provided.
Home page url
Download or read it online for free here:
by Arak M. Mathai, Hans J. Haubold - De Gruyter Open
This is an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. Designed for students in engineering and physics.
by T. Devlin, J. Guo, D. Kunin, D. Xiang - Brown University
The intent of the website and these notes is to provide an intuitive supplement to an introductory level probability and statistics course. The level is also aimed at students who are returning to the subject and would like a concise refresher ...
by J. C. Lemm - arXiv.org
A particular Bayesian field theory is defined by combining a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data.
by Allen B. Downey - Green Tea Press
Think Stats is an introduction to Probability and Statistics for Python programmers. This new book emphasizes simple techniques you can use to explore real data sets and answer interesting statistical questions. Basic skills in Python are assumed.