FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: Learning Statistics with Python
 Author(s) Ethan Weed
 Publisher: Self Publshing (GitHub); eBook (Creative Commons Licensed)
 License(s): Creative Commons License (CC)
 Paperback: N/A
 eBook: HTML
 Language: English
 ISBN10: N/A
 ISBN13: N/A
 Share This:
Book Description
This book explains basic concepts of statistics within the framework of using Python. The blending of statistics and computer coding has quickly become a standard in research to in both academia and industry.
About the Authors N/A

Statistics and Machine Learning in Python (Edouard Duchesnay)
Illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.

O'Reilly® Think Bayes: Bayesian Statistics in Python
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

Think Stats, 2nd Edition: Exploratory Data Analysis in Python
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

Regression Analysis using Python (Eric Marsden)
Become competent at implementing Regression Analysis in Python Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

Bayesian Methods for Hackers: Probabilistic Programming
This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation  no advanced mathematics required.

Bayesian Methods for Statistical Analysis (Borek Puza)
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code.

Bayesian Reasoning and Machine Learning (David Barber)
This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problemsolving skills that equip them for the real world. Numerous examples and exercises are provided.

An Introduction to Bayesian Thinking (Merlise Clyde, et al.)
This book provides an introduction to Bayesian inference in decision making without requiring calculus. It may be used on its own as an openaccess introduction to Bayesian inference using R for anyone interested in learning about Bayesian statistics.

Bayesian Data Analysis (Andrew Gelman, et al.)
This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using uptodate Bayesian methods.

Bayesian Networks and BayesiaLab (Stefan Conrady, et al.)
This practical introduction is geared towards scientists who wish to employ Bayesian Networks for applied research using the BayesiaLab software platform. It can serve as a selfstudy guide for learners and as a reference manual for advanced practitioners.
:






















