Practical-statistics-for-data-scientists - Code repository for O'Reilly book

Overview

Code repository

Practical Statistics for Data Scientists:

50+ Essential Concepts Using R and Python

by Peter Bruce, Andrew Bruce, and Peter Gedeck

Online

View the notebooks online: nbviewer

Excecute the notebooks in Binder: Binder

This can take some time if the binder environment needs to be rebuilt.

Other language versions

English:
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
2020: ISBN 149207294X
Google books, Amazon
Japanese:
データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
2020: ISBN 487311926X, Shinya Ohashi (supervised), Toshiaki Kurokawa (translated)
Google books, Amazon
German:
Praktische Statistik für Data Scientists: 50+ essenzielle Konzepte mit R und Python 
2021: ISBN 3960091532, Marcus Fraaß (Übersetzer)
Google books, Amazon
Korean:
Practical Statistics for Data Scientists: 데이터 과학을 위한 통계(2판) 2021: ISBN 9791162244180, Junyong Lee (translation)
Google books, Hanbit media
Polish:
Statystyka praktyczna w data science. 50 kluczowych zagadnien w jezykach R i Python 2021: ISBN 9788328374270
Google books, Amazon, Helion

See also

Setup R and Python environments

R

Run the following commands in R to install all required packages

if (!require(vioplot)) install.packages('vioplot')
if (!require(corrplot)) install.packages('corrplot')
if (!require(gmodels)) install.packages('gmodels')
if (!require(matrixStats)) install.packages('matrixStats')

if (!require(lmPerm)) install.packages('lmPerm')
if (!require(pwr)) install.packages('pwr')

if (!require(FNN)) install.packages('FNN')
if (!require(klaR)) install.packages('klaR')
if (!require(DMwR)) install.packages('DMwR')

if (!require(xgboost)) install.packages('xgboost')

if (!require(ellipse)) install.packages('ellipse')
if (!require(mclust)) install.packages('mclust')
if (!require(ca)) install.packages('ca')

Python

We recommend to use a conda environment to run the Python code.

conda create -n sfds python
conda activate sfds
conda env update -n sfds -f environment.yml
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Parallel t-SNE implementation with Python and Torch wrappers.

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With Holoviews, your data visualizes itself.

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Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters

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Sentiment Analysis application created with Python and Dash, hosted at socialsentiment.net

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Interactive plotting for Pandas using Vega-Lite

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