**Basic Data Analysis and More: A Guided Tour Using Python**

by O. Melchert

**Publisher**: arXiv 2012**Number of pages**: 62

**Description**:

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.

Download or read it online for free here:

**Download link**

(910KB, PDF)

## Similar books

**Markov Chains and Stochastic Stability**

by

**S.P. Meyn, R.L. Tweedie**-

**Springer**

The book on the theory of general state space Markov chains, and its application to time series analysis, operations research and systems and control theory. An advanced graduate text and a monograph treating the stability of Markov chains.

(

**16039**views)

**Probability and Mathematical Statistics**

by

**Prasanna Sahoo**-

**University of Louisville**

This book is an introduction to probability and mathematical statistics intended for students already having some elementary mathematical background. It is intended for a one-year junior or senior level undergraduate or beginning graduate course.

(

**3503**views)

**Advanced Data Analysis from an Elementary Point of View**

by

**Cosma Rohilla Shalizi**-

**Cambridge University Press**

This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes.

(

**4504**views)

**Introduction Probaility and Statistics**

by

**Muhammad El-Taha**-

**University of Southern Maine**

Topics: Data Analysis; Probability; Random Variables and Discrete Distributions; Continuous Probability Distributions; Sampling Distributions; Point and Interval Estimation; Large Sample Estimation; Large-Sample Tests of Hypothesis; etc.

(

**21543**views)